Psychology Stress Effects On Cognitive Functions

timer Asked: Feb 1st, 2019
account_balance_wallet $15

Question Description

Consider the work of an air traffic controller or an emergency room physician. Both jobs require alertness, quick thinking, and sound judgment in the making of a constant stream of life-or-death decisions. This process occurs with every worker’s shift, day after day. Imagine the impact of such an ongoing responsibility on cognitive functioning.

Stress has profound effects on cognitive functions, such as decision making, occasionally altering the brain in surprising ways. Stress symptoms may lead to prominent clinical characteristics, which often go beyond anxiety and fear. Stress hormones can affect neurotransmitter systems in the brain, causing physical changes in some cases. The hippocampus, for example, can atrophy as a result of chronic stress.

For this Discussion, consider effects of stress on cognitive functions. Then think about a time when stress affected your cognitive functions.

With these thoughts in mind:

Post a brief explanation of how stress affects cognitive functions, including the roles of the amygdala and the prefrontal cortex. Then provide examples of situations when stress affected your attention, memory, problem solving, or decision making. Finally, explain how you might mitigate the effects of stress on cognitive functions.

Support your response using at least scholarly 3 references. APA Format. 2-3 paragraphs.

Emotion 2007, Vol. 7, No. 2, 336 –353 Copyright 2007 by the American Psychological Association 1528-3542/07/$12.00 DOI: 10.1037/1528-3542.7.2.336 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Anxiety and Cognitive Performance: Attentional Control Theory Michael W. Eysenck Nazanin Derakshan Royal Holloway University of London Birkbeck University of London Rita Santos Manuel G. Calvo Royal Holloway University of London University of La Laguna Attentional control theory is an approach to anxiety and cognition representing a major development of Eysenck and Calvo’s (1992) processing efficiency theory. It is assumed that anxiety impairs efficient functioning of the goal-directed attentional system and increases the extent to which processing is influenced by the stimulus-driven attentional system. In addition to decreasing attentional control, anxiety increases attention to threat-related stimuli. Adverse effects of anxiety on processing efficiency depend on two central executive functions involving attentional control: inhibition and shifting. However, anxiety may not impair performance effectiveness (quality of performance) when it leads to the use of compensatory strategies (e.g., enhanced effort; increased use of processing resources). Directions for future research are discussed. Keywords: anxiety, attention, inhibition, shifting mance because it is often associated with adverse effects on the performance of cognitive tasks (see Eysenck, 1992, for a review). The main focus of the theoretical predictions in this article is the effects of anxiety on cognitive tasks, in particular those placing significant demands on cognitive resources. The emphasis is on short-lasting cognitive tasks performed under laboratory conditions. Such tasks permit the identification of the cognitive processes underlying performance under controlled conditions. The structure of the article is as follows. Initially, we discuss processing efficiency theory to provide a background to the theoretical context. Then we present the assumptions of the attentional control theory. Next, we evaluate the evidence relating to this theory’s major hypotheses, and finally, we discuss future research directions. In this article, we are concerned primarily with the effects of anxiety on cognitive performance. The emphasis is on anxiety within normal populations rather than within clinically anxious ones, and there is a focus on individual differences in anxiety as a personality dimension, typically assessed by measures of trait anxiety such as Spielberger’s State–Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983). Nevertheless, individual differences in more specific measures (e.g., test anxiety; Spielberger et al., 1980) are also considered, as are studies in which anxiety is manipulated experimentally (e.g., via evaluative instructions; competitive situations). Anxiety is an aversive emotional and motivational state occurring in threatening circumstances. State anxiety (the currently experienced level of anxiety) is determined interactively by trait or test anxiety and by situational stress (see Eysenck, 1992). It can be conceptualized as “a state in which an individual is unable to instigate a clear pattern of behavior to remove or alter the event/ object/interpretation that is threatening an existing goal” (Power & Dalgleish, 1997, pp. 206 –207). Individuals in an anxious state frequently worry about the threat to a current goal and try to develop effective strategies to reduce anxiety to achieve the goal. Anxiety is of importance within the field of cognition and perfor- Processing Efficiency Theory The theory developed here represents a major extension of the processing efficiency theory put forward by Eysenck and Calvo (1992), itself an extension of the theoretical views of Eysenck (1979). As such, we first briefly consider processing efficiency theory. The most important distinction in processing efficiency theory is between effectiveness and efficiency. Effectiveness refers to the quality of task performance indexed by standard behavioral measures (generally, response accuracy). In contrast, efficiency refers to the relationship between the effectiveness of performance and the effort or resources spent in task performance, with efficiency decreasing as more resources are invested to attain a given performance level. Ways of measuring resource utilization are discussed later. Negative effects of anxiety are predicted to be significantly greater on processing efficiency than on performance effectiveness. Michael W. Eysenck and Rita Santos, Department of Psychology, Royal Holloway University of London, Egham, United Kingdom; Nazanin Derakshan, School of Psychology, Birkbeck University of London, United Kingdom; Manuel G. Calvo, Department of Cognitive Psychology, University of La Laguna, San Cristobal de la Laguna, Tenerife, Spain. We thank Sinead Smyth for her assistance with the manuscript of this article. Correspondence concerning this article should be addressed to Michael W. Eysenck, Department of Psychology, Royal Holloway University of London, Egham, Surrey TW20 0EX, United Kingdom. E-mail: 336 ANXIETY AND COGNITIVE PERFORMANCE This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Assumptions Processing efficiency theory rests on two major assumptions. First, worry is the component of state anxiety responsible for effects of anxiety on performance effectiveness and efficiency. Worry or self-preoccupation is characterized by concerns over evaluation and failure and expectations of aversive consequences (e.g., Borkovec, 1994). Worry is activated in stressful situations (especially in test, evaluative, or competitive conditions) and is most likely to occur in individuals high in trait anxiety (e.g., see Eysenck, 1992, for a review). Worry has two effects. One effect involves cognitive interference by preempting the processing and temporary storage capacity of working memory. The worrisome thoughts consume the limited attentional resources of working memory, which are therefore less available for concurrent task processing. The other effect involves increased motivation to minimize the aversive anxiety state. This function is accomplished by promoting enhanced effort and use of auxiliary processing resources and strategies. Thus, potential performance impairments caused by the preemption of working memory resources can be compensated for. If auxiliary processing resources are available, impaired performance effectiveness is less likely to occur but at the cost of reduced efficiency. If these resources are unavailable, then performance effectiveness will be impaired. The second assumption concerns the mechanisms and components of working memory affected by anxiety. Processing efficiency theory is based on the tripartite working memory model (Baddeley, 1986), since expanded into a four-component model (Baddeley, 2001). According to the original model, the limitedcapacity working memory system consists of (a) a modality-free central executive involved in the processing of information and having self-regulatory functions (e.g., performance monitoring, planning, and strategy selection); (b) a phonological loop for the rehearsal and transient storage of verbal information; and (c) a visuospatial sketchpad for the processing and transient storage of visual and spatial information. It is assumed that the main effects of worry (and, more generally, of anxiety) are on the central executive. Accordingly, adverse effects of anxiety on performance and efficiency should be greater on tasks imposing substantial demands on the processing and storage capacity of working memory (especially the central executive). Worrisome thoughts interfere with this processing-andstorage function, and there is an additional burden on the selfregulatory mechanism inhibiting such thoughts and producing auxiliary processing activities. Detrimental effects of anxiety are also expected on the phonological loop rather than on the visuospatial sketchpad because worry typically involves inner verbal activity rather than imagery representations (Rapee, 1993). Theoretical Limitations Some of the theoretical assumptions of processing efficiency theory lack precision, explanatory power, or both. In addition, the scope of the theory is insufficient to account for several findings. Specific examples are itemized below. First, the notion that anxiety impairs the processing efficiency of the central executive is imprecise because it fails to specify which central executive functions are most adversely affected by anxiety. For example, E. E. Smith and Jonides (1999) argued that the 337 central executive fulfills five functions: switching attention between tasks; planning subtasks to achieve a goal; selective attention and inhibition (i.e., focusing attention on relevant information and processes and inhibiting irrelevant ones); updating and checking the contents of working memory; and coding representations in working memory for time and place of appearance. It is unclear from processing efficiency theory whether anxiety affects some (or all) of these functions. Second, there are no theoretical assumptions concerning the effects of distracting stimuli on anxious individuals. This is important given the accumulating empirical evidence that the performance of anxious individuals is more impaired by distracting stimuli than is that of nonanxious individuals (e.g., Calvo & Eysenck, 1996; Eysenck & Graydon, 1989; Hopko, Ashcraft, Gute, Ruggiero, & Lewis, 1998; see Eysenck, 1992, for a review). Third, processing efficiency theory focuses exclusively on cognitive tasks involving neutral or nonemotional stimuli (defined in terms of their content). However, the performance of anxious individuals is more affected by threat-related stimuli (especially social threat) than that of nonanxious ones. For example, adverse effects of distracting stimuli on the performance of anxious individuals compared with nonanxious ones are often greater when the distracting stimuli are threat related rather than neutral (e.g., Egloff & Hock, 2001; Eysenck & Byrne, 1992; Keogh & French, 2001; Mogg et al., 2000). Fourth, processing efficiency theory does not directly consider circumstances in which anxious individuals might outperform nonanxious ones. In fact, there are several studies (mostly involving paired-associate learning) in which the high-anxious group outperformed the low-anxious group (e.g., Byrne & Eysenck, 1995; Spence, Farber, & McCann, 1956; Spence, Taylor, & Ketchel, 1956; Standish & Champion, 1960). Attentional Control Theory: Assumptions In this section, we present the attentional control theory. The literature has used the term control in various ways. The sense in which we use it here is the same as that of Yantis (1998), who focused on whether attention is controlled or determined in a goal-driven, top-down fashion or in a stimulus-driven, bottom-up fashion. The theory is not a general theory of attentional control but rather is concerned with attentional control in the context of anxiety and cognitive performance. As its name suggests, the theory is not designed to apply to all effects of anxiety on the cognitive system. For example, there is much evidence suggesting that anxiety influences explicit and implicit memory (see J. M. G. Williams, Watts, MacLeod, & Mathews, 1997; Rinck & Becker, 2005). Such effects are somewhat inconsistent and lie outside the theory’s scope. Attentional control theory represents a major development of the previous processing efficiency theory, building on its strengths and addressing its limitations. The key assumption that there is an important distinction between processing efficiency and performance effectiveness is central to attentional control theory. However, this theory extends the scope of the previous theory and is more precise about effects of anxiety on the functioning of the central executive. The development of attentional control theory has been much influenced by the theoretical ideas and empirical research of several researchers (e.g., Derryberry & Reed, 2002; This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 338 EYSENCK, DERAKSHAN, SANTOS, AND CALVO Fox, 1993; Fox, Russo, & Dutton, 2002; Hopko et al., 1998; Mathews & Mackintosh, 1998). The most general assumption within attentional control theory is that effects of anxiety on attentional processes are of fundamental importance to an understanding of how anxiety affects performance. Why is this the case? Power and Dalgleish (1997) assumed that anxiety is experienced when a current goal is threatened, a general assumption consistent with much empirical evidence. Threat to a current goal causes attention to be allocated to detecting its source and to deciding how to respond. Some support for the assumption that anxiety facilitates the detection (and processing) of danger or threat comes from studies on attentional bias (e.g., Egloff & Hock, 2001; Eysenck & Byrne, 1992; Fox et al., 2002; Mogg & Bradley, 1998; Mogg et al., 2000; Wilson & MacLeod, 2003), in which anxious individuals preferentially attend to (or more often preferentially have delayed disengagement from) threat-related stimuli in the presence of neutral stimuli. Attentional Control The assumption that anxiety increases the allocation of attention to threat-related stimuli (and to deciding how to respond in the anxiety-provoking circumstances) means that anxiety typically reduces attentional focus on the current task unless it involves threatening stimuli. More specifically, anxiety impairs attentional control, a key function of the central executive. It follows that anxious individuals preferentially allocate attentional resources to threat-related stimuli whether internal (e.g., worrisome thoughts) or external (e.g., threatening task-irrelevant distractors). High levels of worry are often associated with low levels of performance (see Sarason, 1988, for a review). However, there are several studies in which high-anxious participants reported significantly more worry than low-anxious ones, but the two groups did not differ in performance (e.g., Blankstein, Flett, Boase, & Toner, 1990; Blankstein, Toner, & Flett, 1989; Calvo, Alamo, & Ramos, 1990; Calvo & Ramos, 1989). According to attentional control theory, this pattern could occur because worry impairs efficiency more than performance effectiveness. However, most of the studies in which worry has been considered have limitations. Worry is seldom manipulated explicitly, it is often assessed only retrospectively, and the relationship between worry and attention has not been investigated systematically. In view of these limitations, relatively little research on worry, anxiety, and performance has provided a direct test of the theory. There is a further assumption that anxiety also impairs attentional control even when no threat-related, task-irrelevant stimuli are present. When an individual perceives him- or herself to be under threat and so experiences anxiety, it is potentially dangerous to maintain very high attentional control to a specific stimulus or location. Instead, the optimal strategy is to allocate attentional resources widely, thereby reducing attentional control with respect to any ongoing task. The theoretical assumption that anxiety impairs attentional control can be related to the view (e.g., Corbetta & Shulman, 2002; Posner & Petersen, 1990) that there are two attentional systems. For example, Corbetta and Shulman distinguished between a goaldirected attentional system influenced by expectation, knowledge, and current goals and a stimulus-driven attentional system responding maximally to salient or conspicuous stimuli. The goal- directed attentional system is involved in the top-down control of attention (e.g., via attentional set). It resembles the anterior attentional system proposed by Posner and Petersen and the cognitive control system identified by Miller and Cohen (2001). There are important commonalities among these three systems (e.g., they are involved in top-down control of attention; they are centered in the prefrontal cortex), and these commonalities provide a framework for attentional control theory. The stimulus-driven attentional system identified by Corbetta and Shulman (2002) is involved in the bottom-up control of attention and “is recruited during the detection of behaviorally relevant sensory events, particularly when they are salient and unattended” (pp. 201–202). This system includes the temporoparietal and ventral frontal cortex and resembles Posner and Petersen’s (1990) posterior attentional system. In practice, the goal-directed and stimulus-driven attentional systems frequently interact in their functioning (see Pashler, Johnston, & Ruthroff, 2001, for a review). According to attentional control theory, anxiety disrupts the balance between these two attentional systems. It is associated with an increased influence of the stimulus-driven attentional system and a decreased influence of the goal-directed attentional system. This involves bidirectional influences of each system on the other. For example, anxiety affects the stimulus-driven attentional system via automatic processing of threat-related stimuli (e.g., Fox, Russo, & Georgiou, 2005), thereby decreasing the influence of the goal-directed attentional system. In addition, reduced influence of goal direction on attentional processes means that such processes are more affected by salient and conspicuous stimuli. All these effects of anxiety should be greater when anxiety levels are especially high (e.g., under stressful conditions). The two attentional systems identified by Corbetta and Shulman (2002) and Posner and Petersen (1990) provide a valuable framework within which to consider the effects of anxiety on cognitive processing. However, this theoretical approach is a general one, and higher level functions such as goal-directed planning are difficult to define operationally. The position is similar with respect to the central executive. As discussed earlier, it is oversimplified to regard the central executive as unitary, and so hypotheses framed in terms of the central executive tend to be general and vague. What is needed is a theoretical approach focusing on lower level functions that are related to the goal-directed attentional system and to the central executive and that can be operationalized. In an impressive contribution, Miyake et al. (2000) used latent-variable analysis to identify the basic control functions of the central executive, basing their selection of tasks on lower level functions that had previously been proposed for the central executive by various theorists (e.g., Baddeley, 1996; E. E. Smith & Jonides, 1999). Miyake et al. identified three major functions: 1. Inhibition: “One’s ability to deliberately inhibit dominant, automatic, or prepotent responses when necessary” (p. 57); this involves using attentional control to resist disruption or interference from task-irrelevant stimuli or responses. 2. Shifting: “Shifting back and forth between multiple tasks, operations, or mental sets” (p. 55); this function involves ANXIETY AND COGNITIVE PERFORMANCE adaptive changes in attentional control based on task demands. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 3. Updating: “Updating and monitoring of working memory representations” (p. 56) Friedman and Miyake (2004) extended the scope of the inhibition function. Using latent-variable analysis, they found that this function was used when resisting distractor interference as well as when inhibiting prepotent responses, suggesting that it involves maintaining task goals when confronted by environmental task-irrelevant stimuli or responses. The inhibition function is a general one involving executive control. This approach can be contrasted with other approaches identifying several different inhibition processes. For example, Nigg (2000) identified four types of effortful inhibition: interference control (interference due to resource or stimulus competition); cognitive inhibition (suppression of irrelevant information from working memory); behavioral inhibition (suppression of prepotent responses); and oculomotor inhibition (suppression of reflexive saccades). These inhibition processes may be conceptually separate, but Miyake et al. (2000) and Friedman and Miyake have found that at least three of these processes (interference control, behavioral inhibition, and oculomotor inhibition) seem to involve the same underlying inhibition function. The evidence reviewed here suggests that the inhibition function involves using attentional control in a restraining way to prevent attentional resources being allocated to task-irrelevant stimuli and responses. As such, it is of direct relevance to attentional control theory. It remains to be determined whether the same inhibition function is involved in other forms of inhibition (e.g., inhibition of dominant conceptual pathways). The shifting function is also of direct relevance to attentional control theory. It involves using attentional control in a positive way to shift the allocation of attention to remain focused on task-relevant stimuli. For example, a task in which two-digit numbers are presented and addition and subtraction are performed alternately involves shifting. Wager, Jonides, and Reading (2004) found in a meta-analysis that the same seven distinct brain areas were consistently activated across diverse shifting tasks, suggesting there is a single important shifting function. The third central executive function identified by Miyake et al. (2000) is updating, which involves monitoring as well as updating. A representative task involving updating is one in which members of various categories are presented and participants keep track of the most recently presented member of each category. The updating function involves the transient storage of information rather than being directly concerned with attentional control. Accordingly, effects of anxiety on updating should be weaker than those on inhibition and shifting. It is worth stressing that the brain areas most associated with the inhibition and shifting functions of the central executive are similar to those associated with the goal-directed attentional system (Miller & Cohen, 2001). Collette and Van der Linden (2002) reviewed brain-imaging studies focusing on the inhibition, shifting, and updating functions of the central executive and concluded that “some prefrontal areas (e.g., BA 9/46, 10 and anterior cingulate gyrus) are systematically activated by a large range of various executive tasks, suggesting their involvement in rather general executive processes” (p. 121). 339 In sum, the inhibition, shifting, and updating functions are partially separable. However, they are also partially interdependent in their functioning, suggesting they all rely to some extent on the resources of the central executive or top-down attentional system. Thus, demands on one function may reduce the processing resources of the central executive available for the other functions. Attentional Control, Inhibition, and Shifting According to attentional control theory, anxiety impairs processing efficiency because it reduces attentional control (especially in the presence of threat-related distracting stimuli). As a result, the probability that processing resources will be diverted from taskrelevant stimuli to task-irrelevant ones on tasks involving the inhibition and/or shifting functions is increased. In contrast, it was assumed within processing efficiency theory that anxiety impairs processing efficiency because anxiety produces worry. This reason for impaired processing efficiency is now subsumed within a broader conceptualization, according to which anxiety impairs the inhibition function. Anxious individuals are more distracted by task-irrelevant stimuli whether those stimuli are external (conventional distractors) or internal (e.g., worrying thoughts). The inhibition function is impaired when task demands on the central executive are high. For example, Graydon and Eysenck (1989) used several tasks in which the demands on working memory differed by varying the processing and storage requirements. The adverse effects of distracting stimuli on task performance increased in line with task demands on working memory capacity. Lavie, Hirst, de Fockert, and Viding (2004) explored the same issue. Performance on a selective attention task was more adversely affected by distracting stimuli when overall demands on working memory were high. In addition, distraction effects on a task were greater when it involved the shifting function. An alternative approach is to consider susceptibility to distraction as a function of individual differences in working memory capacity based on complex span measures (e.g., Daneman & Carpenter’s [1980] reading span) assessing the ability to engage in concurrent processing and storage. Individuals low in working memory capacity were more susceptible to distraction than those high in working memory capacity (e.g., see Barrett, Tugade, & Engle, 2004, for a review). This approach demonstrates the usefulness of relating distraction effects to working memory capacity, but it has not been extended to be of direct relevance to an understanding of anxiety and susceptibility to distraction. It is theoretically predicted that the functioning of the shifting function should also be impaired when task demands on the central executive are high. As yet, however, there is no evidence directly relevant to this prediction. Summary In this section, we have presented the general theoretical framework. Its starting point is the crucial assumption of processing efficiency theory that anxiety impairs processing efficiency more than it does performance effectiveness. Of central importance to the revised theory is the notion that anxiety decreases the influence of the goal-directed attentional system and increases the influence of the stimulus-driven attentional system. This results in reduced 340 EYSENCK, DERAKSHAN, SANTOS, AND CALVO This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. attentional control and impairment of the inhibition and shifting functions. The theoretical framework provides the basis for several hypotheses, all of which have been investigated empirically. There are six main hypotheses associated with attentional control theory. Each hypothesis is discussed in the following section, along with the relevant findings. In the great majority of studies, participants were assigned to low- and high-anxious groups on the basis of their test or trait anxiety scores. Unless otherwise stated, this was the case with the experimental studies discussed below. Attention Control Theory: Hypotheses and Empirical Support Hypothesis 1: Anxiety Impairs Processing Efficiency to a Greater Extent Than Performance Effectiveness on Tasks Involving the Central Executive This hypothesis is based on the theoretical assumption that anxiety impairs two of the three key functions of the central executive (i.e., inhibition and shifting), thus producing processing inefficiency on the great majority of tasks involving the central executive. This processing inefficiency does not necessarily lead to decrements in performance effectiveness provided that anxious individuals respond to processing inefficiency by using compensatory strategies such as enhanced effort and use of processing resources. Three kinds of evidence support Hypothesis 1 and are shortly discussed in turn. Most of the relevant findings were based on a theoretical framework in which the central executive was regarded as unitary. Thus, they are of only general relevance to attentional control theory, and their interpretation is somewhat equivocal. However, subsequent hypotheses are of direct relevance to attentional control theory. Time versus accuracy. In most studies, accuracy is regarded as the primary measure of performance effectiveness. Within that context, the more time spent achieving a given level of performance, the lower the processing efficiency. Thus, response accuracy is typically a measure of performance effectiveness and response time efficiency. When low- and high-anxious individuals have comparable performance effectiveness, group differences in efficiency can be inferred from differences in response time. High anxiety was associated with comparable performance to low anxiety but with lengthened response time in several studies. This pattern was reported with verbal reasoning (Darke, 1988b, Experiments 2 and 3); spatial reasoning (Markham & Darke, 1991); grammatical reasoning (Derakshan & Eysenck, 1998; MacLeod & Donnellan, 1993); reading comprehension (Calvo & Carreiras, 1993; Calvo, Eysenck, Ramos, & Jiménez, 1994, Experiments 2, 3, and 4); verbal working memory (Ikeda, Iwanaga, & Seiwa, 1996); sustained attention (Elliman, Green, Rogers, & Finch, 1997); digitstring short-term memory (Derakshan & Eysenck, 1998; Richards, French, Keogh, & Carter, 2000); and course examinations (Benjamin, McKeachie, Lin, & Holinger, 1981). Effort and compensatory strategies. One way in which highanxious individuals can show impaired processing efficiency compared with low-anxious ones is by exerting greater effort but achieving only comparable performance. Effects of anxiety on effort can be assessed by self-report measures, psychophysiologi- cal measures, and incentive manipulations. Each approach is considered in turn. Dornic (1977) asked participants to estimate expended effort after task performance. Those who were neurotic introverts (high anxious) reported expending significantly more effort than those who were stable extraverts (low anxious) on complex versions of a closed-system thinking task. The two groups had comparable performance, so these findings suggest that anxiety reduces processing efficiency. Dornic (1980) found that anxiety was associated with increased mental effort on two versions of a complex task even though anxiety did not impair performance. N. C. Smith, Bellamy, Collins, and Newell (2001), using motor tasks, and Hadwin, Brogan, and Stevenson (2005), using cognitive tasks, also found higher effort ratings in high-anxious participants combined with no effects of anxiety on performance. An alternative method of assessing effort expenditure involves psychophysiological measures. Cardiovascular measures are useful because they reflect motivation and task engagement (Schwerdtfeger & Kohlmann, 2004). The findings are nevertheless difficult to interpret. High-anxious groups exhibit greater cardiovascular reactivity than low-anxious ones in the pretask instruction phase and the posttask recovery phase (Calvo, Avero, & Jiménez, 1997; Calvo & Cano, 1997). However, there are typically no differences in cardiovascular indices of effort during task performance (Calvo, Szabo, & Capafons, 1996; Di Bartolo, Brown, & Barlow, 1997; Schönpflug, 1992), suggesting that high-anxious individuals do not increase effort expenditure more than lowanxious individuals. A third approach involves the use of external incentives to manipulate motivation. Theoretically, high-anxious individuals typically use more processing resources than low-anxious ones in achieving a comparable level of performance. Thus, there is less scope for incentives to produce enhanced effort and performance in high-anxious groups. Calvo (1985) and Eysenck (1985) provided monetary incentives for good performance on a nonverbal inductive reasoning task or a letter-transformation task, respectively. In both studies, the performance of the high-anxious groups was generally unaffected by incentive, whereas that of the lowanxious groups was enhanced. Schönpflug (1992) obtained similar results. In sum, self-report and incentive studies support the assumption that anxious individuals often compensate for impaired processing efficiency with additional effort. What compensatory strategies do they use? The answer depends on various factors (e.g., the precise task demands). The most systematic research was carried out on reading tasks by Calvo and colleagues (e.g., Calvo & Castillo, 1995; Calvo & Eysenck, 1996; Calvo et al., 1994; Calvo & Jiménez, 1996; Calvo, Ramos, & Eysenck, 1993). Two compensatory strategies frequently used by high-anxious individuals in evaluative stress conditions were reading regressions (i.e., looking back at previous text) and articulatory rehearsal (vocal and subvocal articulation during reading). It was assumed that regressions assist in the integration of prior with current text information, whereas articulatory rehearsal assists the phonological loop with the coding and short-term retention of words. Regressions were consistently the preferred strategy of high-anxious readers. Only if regressions were not possible (i.e., fixed-pace forward presentation of text) did high-anxious readers show increased vocal and subvocal articulation. Comprehension performance of high-anxious This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ANXIETY AND COGNITIVE PERFORMANCE individuals was comparable to that of low-anxious individuals when at least one compensatory strategy was available. Theoretically, impaired attentional control is central to the reduced efficiency shown by high-anxious individuals. Accordingly, the most direct compensatory strategy would be to increase use of the shifting and/or inhibition functions to regain attentional control. Santos, Wall, and Eysenck (2006) used functional magnetic resonance imaging (fMRI) to assess brain activation while participants performed three tasks under no-switch and high-switch conditions. A comparison of brain activation in these two conditions (subtracting brain activation under no-switch conditions from that under high-switch conditions) indicated that high state anxiety was associated with significantly greater activation than low state anxiety in the right lateral prefrontal cortex (principally BA 9/46), an area associated with the shifting function (Collette & Van der Linden, 2002). Thus, anxiety produced inefficiency, and anxious individuals made increased use of the shifting function to compensate. These findings show the potential value of neuroimaging in assessing processing efficiency. Probe technique. It follows from Hypothesis 1 that anxious individuals should devote more central executive processing resources to the performance of a main task and thus have fewer spare processing resources. This prediction can be tested by the probe technique. In essence, the instructions emphasize that the main task should be performed as well as possible. There is also a secondary task (responding rapidly to occasional auditory or visual probe signals). The more resources allocated to the main task, the fewer are available for the secondary task, and so probe reaction times will be slowed. Hamilton (1978) used digit span as the primary task and interpolated probe stimuli between presentation of the digit string and its subsequent recall. In the most difficult condition (seven-digit string), high-anxious participants had significantly slower response latencies than low-anxious ones, implying they had less spare processing capacity. Eysenck (1989) used the probe technique, with the main task consisting of simple versions (one and two letters) of a letter-transformation task. The low- and high-anxious groups had comparable performance effectiveness on this task. However, they had significantly lower spare processing capacity than low-anxious individuals (and thus lower processing efficiency), as indicated by their significantly longer probe reaction times during the performance of two-letter problems. Eysenck and Payne (2006) extended these findings. There were no effects of anxiety on performance effectiveness on the lettertransformation task. However, probe reaction time in high-anxious individuals was slowed under evaluative conditions compared with nonevaluative conditions, whereas the opposite pattern was found for low-anxious individuals. Under evaluative conditions, the slowing of high-anxious participants was directly related to the number of letters in the letter-transformation task. The probe technique has also been used when the main task involves motor performance (simulated driving [Murray & Janelle, 2003]; table tennis [A. M. Williams, Vickers, & Rodrigues, 2002]). Murray and Janelle reported slower probe reaction times for high than for low trait-anxious participants, especially under competitive conditions. A. M. Williams et al. found that anxious participants had worse performance than nonanxious ones on the table-tennis task, as well as slower reaction times to probes. These findings suggest that anxiety reduced processing efficiency. 341 Summary. Research based on all three approaches indicates that anxiety impairs efficiency more than effectiveness. There is thus considerable support for one of the key assumptions of attentional control theory. The most direct evidence has come from studies using the probe technique (Eysenck, 1989; Eysenck & Payne, 2006; Hamilton, 1978; Murray & Janelle, 2003; A. M. Williams et al., 2002) and from use of fMRI (Santos et al., 2006). Future research should focus on replicating and extending the findings of Santos et al. because they assessed the effects of anxiety on processing efficiency and performance effectiveness on a relatively pure task involving the shifting function. Most of the existing research has used tasks involving various central executive functions, thereby making it difficult to provide an unequivocal interpretation of the findings. Hypothesis 2: Adverse Effects of Anxiety on Performance Become Greater as Overall Task Demands on the Central Executive Increase According to attentional control theory, anxious individuals can compensate for the adverse effects of anxiety on processing efficiency of the inhibition and shifting functions by increased effort and use of resources. As a consequence, there may be small or nonexistent effects of anxiety on performance effectiveness. However, it becomes decreasingly possible for anxious individuals to compensate for impaired efficiency through increased effort and use of resources as overall task demands increase, and so decrements in performance become greater. Two types of empirical research provide tests of Hypothesis 2. First, there is research in which only a single task is performed, with performance on different tasks varying in their demands on working memory (especially the central executive) being compared. Second, there is research using the loading paradigm, in which two tasks are performed concurrently. There is an invariant primary task performed concurrently with secondary tasks varying in their processing demands on the central executive. All the studies reported manipulated the demands on the central executive. In the great majority of studies, this represented the major manipulation. However, the studies by Eysenck (1985) and by Ashcraft and Kirk (2001) manipulated demands on the phonological loop as well as on the central executive. These studies were based on a conceptualization in which the central executive was regarded as unitary. As such, most findings can be interpreted by processing efficiency theory (based on a unitary view of the central executive) and by attentional control theory (emphasizing its shifting and inhibition functions). Processing demands. Much is known of the processing demands associated with reading, which makes it suitable for testing Hypothesis 2. For example, readers may draw anaphoric or elaborative inferences while reading. Anaphoric inferences are necessary for coherence and are drawn rapidly and automatically with minimal processing resources. In contrast, elaborative inferences take longer to construct, suggesting they require use of central executive resources. Darke (1988b) found that anxiety had no effect on verification speed of anaphoric inferences, but highanxious individuals took longer than low-anxious ones to verify elaborative inferences. Richards et al. (2000) obtained convergent findings. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 342 EYSENCK, DERAKSHAN, SANTOS, AND CALVO Text integration processes in reading involve connecting information held temporarily in memory across sentences and so increase demands on working memory capacity. Such demands are greater for integration processes than for individual-word lexical access. Calvo and Carreiras (1993) found an interaction between trait anxiety and psycholinguistic variables producing (or not producing) integration processes during reading. High-anxious participants were only more strongly affected than low-anxious ones by variables influencing text-level processes. An alternative approach uses related tasks, with processing and storage demands being systematically manipulated. This approach is of direct relevance to working memory, a cognitive capacity involved in the transitory storage of the products of previous processes while subsequent information is being processed to integrate the previous and current information (Baddeley, 1986). Relevant research was reported by Eysenck (1985) and Ashcraft and Kirk (2001, Experiment 3), using a task involving transforming each letter of a one- to four-letter series mentally by counting forward (e.g., BH ⫹ 4 ⫽ ? [FL]). In both studies, high anxiety was related to impaired performance with increased demands, with the most detrimental effects of anxiety being obtained on four-letter tasks with a large transformation. Ashcraft and Kirk also reported similar findings with a number-transformation task. In sum, there is consistent support for Hypothesis 2 that adverse effects of anxiety on performance are greater on tasks imposing considerable demands on central executive processes and/or the working memory system as a whole. Loading paradigm. In the loading paradigm, the same main or primary task is performed concurrently with a secondary task or load imposing low or high demands on the central executive. Adverse effects of anxiety on main-task performance should be greater when the secondary or load task imposes high demands on the central executive (especially the inhibition and shifting functions). There are two differences between the precise predictions of processing efficiency theory and attentional control theory with the loading paradigm. First, attentional control theory emphasizes demands on the inhibition and shifting functions rather than general demands on the central executive. Second, the emphasis in processing efficiency theory was on the demands of the two tasks considered separately. In contrast, attentional control theory emphasizes the demands on attentional control. Performing two tasks concurrently typically requires attentional control (especially the shifting function) to coordinate processing on the two tasks in addition to the demands of each task separately. Accordingly, anxiety should impair performance on the primary task even if the secondary task does not require central executive processes, provided attentional control is needed to coordinate performance. In contrast, processing efficiency theory predicts no impairment of performance in these circumstances. The loading paradigm was used by MacLeod and Donnellan (1993). A verbal reasoning task formed the primary task; the secondary task involved low or high memory load. No effect of anxiety was observed on the concurrent memory task. As predicted by Hypothesis 2, the adverse effects of the more demanding secondary task on verbal reasoning performance were significantly greater in high trait-anxious individuals. Derakshan and Eysenck (1998) successfully replicated MacLeod and Donnellan’s key findings. From the perspective of attentional control theory, it is important that the low load condition in these two studies did not require use of the shifting function to coordinate processing on the two concurrent tasks. Ashcraft and Kirk (2001, Experiment 2) used the loading paradigm when the primary task consisted of addition problems and the secondary or load task involved remembering two or six randomly selected consonants. Adverse effects of math anxiety emerged in math performance only with a six-letter memory load. Calvo and Ramos (1989) reported similar findings with motor tasks. There are two limitations with these studies. First, they do not directly address the issue of which working memory components are most affected by anxiety. Second, it is assumed theoretically that anxiety affects the modality-free functions of the central executive. However, the primary and secondary tasks used by MacLeod and Donnellan (1993) and by Derakshan and Eysenck (1998) were both verbal. Thus, the key findings could be reexpressed as showing that anxiety impairs the ability to perform two demanding verbal tasks concurrently. Eysenck, Payne, and Derakshan (2005) addressed these issues. Participants low and high in trait anxiety performed a complex visuospatial task concurrently with various secondary tasks. As predicted, anxiety had an adverse effect on main-task performance when the secondary task required use of central executive processes. Also as predicted, anxiety did not impair main-task performance when the secondary task involved the phonological loop or the visuospatial sketchpad. In sum, anxiety reduces available central executive capacity. However, although the loading paradigm has proved useful in identifying the working memory component most adversely affected by anxiety, it has as yet failed to shed light on the central executive functions most involved. It remains for future research to clarify this issue. It has often been assumed that dual-task performance (including performance using the loading paradigm) reflects rapid task switching (e.g., Duncan, 1995). In that case, the finding that anxiety lowers performance when two attentionally demanding tasks are performed concurrently may be due to impaired attentional control in anxiety. However, in the absence of direct manipulation of demands on attentional control, the interpretation is equivocal. Hypothesis 3: Anxiety Impairs Attentional Control by Increasing the Influence of the Stimulus-Driven Attentional System The research discussed in this section focuses on general aspects of attentional control and the ways in which anxiety affects the stimulus-driven attentional system. According to attentional control theory, anxiety changes the balance between the goal-directed and the stimulus-driven attentional systems, increasing the impact of the latter system. More detailed findings concerning the effects of anxiety on components of attentional control are discussed in connection with Hypotheses 4 and 5. Attentional control: Questionnaire studies. The relationship between anxiety and attentional control has been assessed in several questionnaire studies. Such evidence is of relevance to attentional control theory. However, humans have only a limited ability to introspect about their own attentional control, and so questionnaire studies need to be supported by experimental data. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ANXIETY AND COGNITIVE PERFORMANCE All the main questionnaires assessing attentional control have treated it as a traitlike construct. Broadbent, Cooper, FitzGerald, and Parkes (1982) devised the Cognitive Failures Questionnaire to assess individual differences in minor everyday slips or errors mostly reflecting inadequate attentional control. Sample items are as follows: “Do you fail to notice signposts on the road?” and “Do you start doing something at home and get distracted into doing something else (unintentionally)?” Broadbent et al. claimed some validity for the Cognitive Failures Questionnaire by finding that self-report scores correlated moderately with ratings by others. They found that scores on the Cognitive Failures Questionnaire correlated .31 with trait anxiety. Friedman and Miyake (2004) used latent-variable analysis applied to the data from several tasks to identify an inhibition function. Scores on the Cognitive Failures Questionnaire correlated significantly with this inhibition function. More direct evidence has been reported in studies using the Attentional Control Scale (see Derryberry & Reed, 2002), which assesses attentional focusing and attentional shifting between tasks. Derryberry and Reed reported a correlation of ⫺.42 between trait anxiety and attentional control using this scale, and they referred to a correlation of ⫺.55 between those two variables in an unpublished study of theirs. Attentional control: Dual-task paradigm. In the typical dualtask paradigm used in anxiety research, a primary task is presented in the center of the visual field and a concurrent secondary task is presented in the periphery. According to attentional control theory, anxiety makes it difficult for the goal-directed attentional system to override the stimulus-driven attentional system. Thus, anxious individuals should generally attend to salient or conspicuous stimuli because such stimuli command attention from the stimulusdriven attentional system (Corbetta & Shulman, 2002). If the primary task stimuli are more salient than secondary task stimuli, anxiety should impair secondary task performance more than primary task performance. However, if the primary task stimuli are no more salient (or less salient) than the secondary task ones, then anxiety should not impair performance on the secondary task. The reason is that attentional processes in anxious individuals are more influenced by the stimulus-driven attentional system than those in nonanxious individuals. The predictions of attentional control theory can be compared with those of Easterbrook’s (1959) hypothesis, still considered the dominant theoretical position (e.g., Staal, 2004). According to Easterbrook’s hypothesis, anxiety narrows attention, creating a tunnel effect, with this attentional narrowing reflecting a relatively passive and automatic physiological process. As anxiety increases, attentional narrowing produces enhanced focusing on those task stimuli emphasized by the instructions, combined with decreased attention to all other stimuli. There is no mention of stimulus salience in Easterbrook’s theoretical approach. The key prediction from his approach is that the attentional narrowing produced by anxiety focuses attention on primary task stimuli and so impairs performance of the secondary task more than that of the primary task. There have been various reviews (e.g., Eysenck, 1982; Staal, 2004), and so the focus here is on key findings. Attentional control theory and Easterbrook’s (1959) hypothesis both predict that anxiety will produce impaired performance on the secondary task 343 when the primary task is cognitively demanding and secondary task stimuli are less salient than primary task ones. Most findings are consistent with this prediction (e.g., Janelle, Singer, & Williams, 1999; Murray & Janelle, 2003; Wachtel, 1968; Weltman, Smith, & Egstrom, 1971; J. M. Williams, Tonymon, & Andersen, 1990, 1991). In these studies, the primary task stimuli were more salient than the secondary task ones: The primary task was presented in the center of the visual field and required continuous performance, whereas secondary task stimuli were presented infrequently and in the periphery. In all these studies, anxiety was associated with impaired performance on the secondary task. Easterbrook’s (1959) hypothesis and attentional control theory lead to different predictions when the secondary or peripheral stimuli are at least as salient as those of the primary task. Easterbrook’s hypothesis continues to predict that anxiety should impair secondary task performance. In contrast, attentional control theory predicts that anxiety should not impair secondary task performance because the stimulus-driven attentional system has more influence on anxious than on nonanxious individuals, and this reduces the attentional focus on the primary task emphasized in the instructions. There are six relevant studies (Dusek, Kermis, & Mergler, 1975; Dusek, Mergler, & Kermis, 1976; Markowitz, 1969; Shapiro & Johnson, 1987; Shapiro & Lim, 1989; Solso, Johnson, & Schatz, 1968), all discussed in the following paragraphs. In the studies by Dusek et al. (1975, 1976), the secondary task stimuli (drawings of household objects) were comparable in salience to the primary task stimuli (drawings of animals) and were presented together. In both studies, participants high in test anxiety had significantly better recall of the secondary task stimuli than those low in test anxiety, with the opposite being the case for recall of the primary task stimuli. Markowitz’s (1969) primary task involved intentional learning of meaningless trigrams, whereas his secondary task involved incidental learning of words. The secondary task stimuli were salient in that they were more meaningful than the primary task stimuli and were presented immediately above those on the primary task. Participants high in trait anxiety performed significantly better on the secondary task under high-stress than low-stress conditions. In the studies by Shapiro and Johnson (1987) and Shapiro and Lim (1989), a stressful condition was created by presenting electric shocks or by presenting anxiety-creating music. When central and peripheral stimuli of equal salience were presented concurrently, the anxious participants in both studies were much less likely than the nonanxious ones to perceive the central stimulus first. Solso et al. (1968) presented seven items briefly for subsequent recall at varying distances from the fixation point. Recall of the items presented furthest from the fixation point was highly significantly greater for the high-anxious participants than for the lowanxious ones, but anxiety did not affect recall of items presented close to the fixation point. In conclusion, attentional control theory is more consistent with the findings than is Easterbrook’s (1959) hypothesis. The main reason is that the salience of secondary task stimuli (emphasized within attentional control theory but ignored within Easterbrook’s hypothesis) crucially influences effects of anxiety on secondary task performance. In addition, attentional control theory is closely This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 344 EYSENCK, DERAKSHAN, SANTOS, AND CALVO related to a major theoretical approach to attention (exemplified by Corbetta & Shulman, 2002; Posner & Petersen, 1990). Stimulus-driven attentional system: Performance enhancement. According to Hypothesis 3, anxiety increases the influence of the stimulus-driven attentional system relative to the goal-directed attentional system. Thus, performance on tasks in which the stimulus-driven attentional system is sufficient for performance is likely to be enhanced by anxiety. Anxiety produces preferential attention to threat-related stimuli (and to slow disengagement), and so beneficial effects of anxiety on performance are especially likely when the task involves responding to threat-related stimuli. Relevant research involving neutral stimuli is discussed first, followed by studies involving threat-related stimuli. Neutral stimuli are defined as those lacking emotional content. It is, of course, possible that neutral stimuli may produce anxiety in participants who perceive them as interfering with performance or as signaling a difficult task (e.g., those high in math anxiety confronted by a problem in math (Ashcraft & Kirk, 2001). The effects of anxiety on paired-associate learning were studied in the 1950s and 1960s (Spence, Farber, & McFann, 1956; Spence, Taylor, & Ketchel, 1956; Standish & Champion, 1960). In these studies, there were lists consisting of competitional and noncompetitional paired associates. Before list formation, pairs of items in which the response item was the strongest associate of the stimulus item were selected. The competitional lists involved repairing the stimulus and response items so the strongest associate of the stimulus item was associated with a different stimulus word. In contrast, the noncompetitional lists simply consisted of the original stimulus–response pairings. According to attentional control theory, the stimulus-driven attentional system should produce the correct responses on the noncompetitional lists, and so anxiety should enhance performance. In contrast, use of the stimulus-driven attentional system (combined with anxiety-related impairment in the inhibition of strong associates) would produce incorrect responses on competitional lists, and so anxiety should impair performance. This pattern was found in all three studies (Spence, Farber, & McFann, 1956; Spence, Taylor, & Ketchel, 1956; Standish & Champion, 1956). Thus, anxiety can enhance performance when the required responses primarily require use of the stimulus-driven attentional system. Several studies have assessed effects of anxiety on performance with threat-related stimuli (defined by their content). For example, Byrne and Eysenck (1995) used a task involving detection of angry faces in neutral crowds. The detection speed of a high-anxious group was significantly greater than that of a low-anxious group. Fox and Georgiou (2005) reviewed the findings from several experiments on detection of threat-related stimuli. Overall, there was a small reduction in detection time in participants high in trait anxiety. Evidence supporting Hypothesis 3 has come from studies of attentional bias (see Eysenck, 1992, for a review). Attentional bias is the tendency to attend to threat-related stimuli (or more often to show slow attentional disengagement from such stimuli) when presented concurrently with neutral stimuli. It is generally assessed by the dot-probe task on which participants respond rapidly when a dot is detected. When a threat-related stimulus and a neutral stimulus are presented concurrently, anxious individuals typically respond faster than nonanxious ones to the dot when it replaces the threat-related stimulus but respond slower when it replaces the neutral stimulus (e.g., Eysenck, MacLeod, & Mathews, 1987; Mogg et al., 2000; Pishyar, Harris, & Menzies, 2004; see Eysenck, 1997, 2004, for reviews). Fox et al. (2002) showed that the attentional bias associated with anxiety depends mainly on the difficulty anxious individuals have in disengaging from threatrelated stimuli. Findings on attentional bias support two assumptions of attentional control theory. First, the stimulus-driven attentional system in anxious individuals is more affected by threat-related stimuli than in nonanxious individuals. Second, and following from the first assumption, anxiety produces enhanced performance under the conditions predicted by the theory (i.e., when the task stimuli themselves are threat related). Hypothesis 4: Anxiety Impairs Efficiency (and Often Effectiveness) on Tasks Involving the Inhibition Function, Especially With Threat-Related Distractors According to Friedman and Miyake (2004), the inhibition function consists of two highly intercorrelated components: prepotent response inhibition and resistance to distractor interference. According to attentional control theory, anxiety reduces the efficiency of inhibition in the sense of reducing inhibitory control on incorrect prepotent or dominant responses and on attention to taskirrelevant stimuli. These adverse effects are greater with threatrelated than with neutral distracting stimuli because the bottom-up attentional system in anxious individuals is especially responsive to threat-related stimuli. Negative effects of anxiety on performance should be greater when overall processing demands are high and anxious individuals have insufficient processing capacity to regain attentional control. According to attentional control theory, the adverse effects of anxiety on the inhibition function mean that anxious individuals are more distracted than nonanxious ones by external taskirrelevant stimuli presented by the experimenter and by internal task-irrelevant stimuli (e.g., worrying thoughts; selfpreoccupation). There are very few studies in which the number of worrying thoughts has been manipulated systematically, and so we focus on the effects of external task-irrelevant stimuli. Prepotent response inhibition. Early studies on the effects of anxiety on prepotent response inhibition were reported in Spence, Farber, and McFann (1956); Spence, Taylor, and Ketchel (1956); and Standish and Champion (1960), already discussed. In these studies, participants low and high in trait anxiety learned lists of paired associates. In the relevant condition (competitional lists), the stimulus and response words were re-paired so that the strongest associate of each stimulus word was associated with a different stimulus word. Anxiety significantly impaired the pairedassociate learning on these competitional lists because anxious individuals had difficulty inhibiting the prepotent (but incorrect) responses (this could also involve conceptual inhibition). Pallak, Pittman, Heller, and Munson (1975) used the Stroop task with low- and high-anxious individuals. Anxiety adversely affected performance speed only in the condition requiring inhibition of prepotent responses (i.e., color naming of other color words). Hochman (1967, 1969) also used the Stroop task. In both studies, individuals in the high-stress condition performed significantly This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ANXIETY AND COGNITIVE PERFORMANCE worse than did the participants in the low-stress condition when the color name and the color word conflicted. Resistance to distractor interference. The effects of anxiety on resistance to distraction have been assessed using various paradigms. It is assumed that the adverse effects of anxiety on ability to resist distraction are mediated by attentional processes. It is thus predicted that anxious individuals will attend to distracting stimuli more than will nonanxious individuals. Effects of anxiety on susceptibility to distraction as assessed by eye movements away from the current task have been reported. Nottelman and Hill (1977) found children high in test anxiety glanced more often than those low in test anxiety at a distracting task. Alting and Markham (1993) found in an evaluative condition that individuals high in test anxiety spent longer than those low in test anxiety in off-task glancing only when a distractor was present. Janelle et al. (1999) used simulated car driving as their central task. When distracting stimuli were presented to the periphery, anxious participants had far more eye movements toward peripheral locations than did nonanxious participants. Dornic and Fernaeus (1981) compared neurotic introverted (high trait-anxious) and stable extraverted (low trait-anxious) individuals on three tasks. Distraction effects on main-task performance were significantly greater on each task for those who were neurotic introverts. Hopko et al. (1998) studied the effects of distraction (i.e., distracting phrases) on a reading task. The reading speed of individuals high in math anxiety was more adversely affected by the distracting phrases than that of individuals low in math anxiety. The effects of distraction were investigated by Eysenck and Graydon (1989) and Calvo and Eysenck (1996). Eysenck and Graydon found the performance of neurotic introverted (high trait-anxious) individuals on a letter-transformation task was more impaired by distracting stimuli resembling task stimuli than was the performance of stable extraverted (low trait-anxious) individuals. However, Keogh and French (1997) failed to replicate their key findings. Calvo and Eysenck (1996) investigated effects of distraction (meaningful speech) on text comprehension. The text was presented to minimize or maximize memory demands on working memory. Distraction had a significantly greater negative effect on the text comprehension performance of the high-anxious group than of the low-anxious group only when the comprehension task was highly demanding. The findings of Calvo and Eysenck in conjunction with those of Calvo and Castillo (1995) indicate that the greater susceptibility to distraction on a comprehension task of high-anxious individuals depends mainly on phonological interference. Wood, Mathews, and Dalgleish (2001) had participants decide whether a probe word was related to the meaning of a preceding sentence. In one condition, a homograph was presented in the sentence, and the probe word was related to a meaning of the homograph inappropriate within the sentence context (e.g., Ace following “He dug with a spade”). This task was performed on its own or concurrently with a demanding task (remembering strings of random digits). Individuals high in trait anxiety showed impaired inhibitory processing of irrelevant meanings of homographs relative to those low in trait anxiety (in terms of errors and latencies) only when there was a concurrent demanding task. Thus, individuals high in trait anxiety were less able to limit processing 345 of task-irrelevant or distracting information in conditions of high overall task demands. Inhibition: Threat-related stimuli versus neutral stimuli. The adverse effects of anxiety on task performance caused by taskirrelevant stimuli are greater when they are threat related rather than neutral. Much of the relevant research has involved the emotional Stroop task (see Williams, Mathews, & MacLeod, 1996, for a review). On this task, neutral or threat-related words are presented in color, and participants name the color as rapidly as possible. The prediction is that the effects of anxiety in slowing color-naming performance should be greater when the words are threat related; this is the emotional Stroop interference effect. Note that inhibition of color processing/naming is task relevant, and therefore slowed responses imply that there is insufficient inhibition. Mogg, Mathews, Bird, and MacGregor-Morris (1990) found that trait anxiety was positively associated with the magnitude of the emotional Stroop interference effect. Richards and French (1990) found with the emotional Stroop task that individuals high in trait anxiety had significantly longer naming latencies for anxiety-related words than for neutral words. There was no effect of anxiety on response times to happiness-related words, so the effects of anxiety centered on anxiety-related words rather than simply on emotional words. Mogg and Marden (1990) found that high trait-anxious participants were slower than low trait-anxious ones in color naming threat-related and emotionally positive words, suggesting that anxiety influences processing of all emotional words. Martin, Williams, and Clark (1991) found no effect of trait anxiety on color naming of threat-related words. Egloff and Hock (2001) reported that the emotional Stroop interference effect was determined interactively by trait anxiety and state anxiety, with the greatest interference effect being shown by individuals high in both trait and state anxiety. Several researchers have studied the emotional Stroop task under subliminal and supraliminal conditions. Mogg, Bradley, Williams, and Mathews (1993) found a slowing of performance in individuals high in trait anxiety only when threat-related stimuli were presented subliminally. In contrast, van den Hout, Tenney, Huygens, Merckelbach, and Kindt (1995) found that high state and trait anxiety were associated with slowed color naming of threatrelated words in both subliminal and supraliminal conditions. MacLeod and Hagan (1992) found in a stressful condition that trait and state anxiety were both associated with slowed color naming of threat-related words only with subliminal presentation. MacLeod and Rutherford (1992) compared performance on the emotional Stroop task under nonstressful and stressful conditions. Under subliminal conditions, however, individuals high in trait anxiety showed an interference effect with threat-related stimuli only in the stressful condition. There are other studies in which effects of neutral and threatrelated distractors on performance were compared. Eysenck and Byrne (1992) assessed performance on a reaction time task involving target-word detection in the presence of social-threat, physicalthreat, positive, and neutral words. Greater susceptibility to distraction among individuals high in trait anxiety than among those low in trait anxiety was found only with physical threat distractors. Byrne and Eysenck (1995) considered speed of detection of a happy face in the context of neutral faces or angry faces. Individuals high in trait anxiety took longer to detect happy faces when This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 346 EYSENCK, DERAKSHAN, SANTOS, AND CALVO the nontarget faces were angry rather than neutral, whereas distractor type had no effect on performance among low-anxious individuals. Keogh and French (2001) and Keogh, Bond, French, Richards, and Davis (2004) studied distraction effects on a reaction time task involving focused attention or selective search. With focused attention in evaluative conditions, the performance of individuals high in test anxiety was more adversely affected by threat-related distractors than was that of those low in test anxiety. Keogh et al. reported that the performance of individuals high in test anxiety was more adversely affected by threat-related than by nonthreat distractors, whereas individuals low in test anxiety were comparably affected by threat-related and nonthreat distractors. Contrary to prediction, however, these findings occurred mainly because individuals high in test anxiety were less affected by nonthreat distractors. The spatial cueing paradigm is also of relevance to distraction effects in anxiety. Participants are presented with valid cues (identifying the location at which the target will be presented) or invalid cues (providing misleading information). The cue can be regarded as a distracting stimulus on invalid trials, and effective attentional control would involve rapid disengagement from invalid cues. Anxious individuals found it harder than nonanxious ones to disengage from invalid cues (Poy, Eixarch, & Ávila, 2004). Using a similar paradigm, Fox et al. (2002) and Yiend and Mathews (2001) found anxious participants took longer than nonanxious ones to disengage only from invalid threat-related stimuli. Thus, the findings agree with those using other paradigms. Neurophysiological evidence was reported by Bishop, Duncan, Brett, and Lawrence (2004). In the experimental condition (in which many threat-related distracting stimuli were presented), Bishop et al. argued that participants would need increased attentional control to minimize the disruptive effects of the distractors. Participants high in state anxiety showed decreased activation of the lateral prefrontal cortex (associated with attentional control) in the experimental condition compared with a control condition involving few threat-related distractors, whereas those low in state anxiety showed increased activation. Bishop et al. concluded that “anxiety is associated with reduced top-down control over threatrelated distractors” (p. 184). Summary. According to the theory, anxiety should consistently impair the inhibition function and thus generally impair performance. This prediction has been supported. Anxiety had a significantly adverse effect on the performance of tasks assessing inhibition in 31 comparisons. Theoretically, the greater susceptibility to distraction shown by anxious individuals should be especially great when task demands are high. This prediction has been supported in several studies (e.g., Calvo & Eysenck, 1996; Eysenck & Graydon, 1989; Wood et al., 2001). The impaired efficiency of the inhibition function shown by anxious individuals compared with nonanxious ones should reduce performance effectiveness more when task-irrelevant stimuli are threat related rather than neutral. This should occur because anxious individuals are more responsive to threat-related distractors in a relatively automatic fashion via the stimulus-driven attentional system. The former prediction has received support in several studies (e.g., Egloff & Hock, 2001; Eysenck & Byrne, 1992; Keogh & French, 2001; MacLeod & Rutherford, 1992; Mogg et al., 2000, 1990; Mogg & Marden, 1990; Richards & French, 1990). The latter prediction would receive support if anxiety were associated with interference on the emotional Stroop task when threat-related stimuli are presented subliminally. Confirmatory findings have been reported in several studies (e.g., MacLeod & Hagan, 1992; MacLeod & Rutherford, 1992; Mogg et al., 1993; van den Hout et al., 1995). What awaits further research is to investigate whether the adverse effects of anxiety on the inhibition function are greater with respect to processing efficiency than to performance effectiveness. All the studies considered in this section involved external distracting stimuli. However, the same theoretical assumptions can be used to explain why internal distracting stimuli (especially threat-related ones such as worrying thoughts) attract attention away from the task and impair performance. Hypothesis 5: Anxiety Impairs Processing Efficiency (and Often Performance Effectiveness) on Tasks Involving the Shifting Function Miyake et al. (2000) identified switching as a basic control process or central executive function. They found that switching was assessed most validly in dual-task conditions in which there was experimenter-determined switching between tasks (e.g., alternating between addition and subtraction problems). Task switching involves the performance of two tasks in rapid succession. It is associated with costs (e.g., increased reaction times and/or errors) immediately after the switch, as compared with a control condition in which the same tasks are used but there is little or no switching between tasks (see Monsell, 2003, for a review). These switching costs are incurred in part because of the need to exert attentional control when one task is replaced by a second one (e.g., Monsell & Driver, 2000; Rogers & Monsell, 1995). On the assumption that the requirement to exert attentional control plays an important role in determining switching costs, anxiety should impair efficiency when task switching is necessary and will often impair performance. The shifting function is also often used in prospective memory studies, which generally involve two tasks. The primary task is performed almost continuously, and a concurrent prospective memory task is performed sporadically in response to some cue (e.g., auditory or visual signal). Failures on the prospective memory task occur when participants do not shift attention to that task when cued. Task switching. Miyake et al. (2000) found that the Wisconsin Card Sorting Task (which involves shifting sorting categories) primarily involves the shifting function of the central executive. Goodwin and Sher (1992) found that high-anxious individuals made more errors and took longer to complete this task than did low-anxious individuals. Santos and Eysenck (2006) used a task-switching paradigm resembling that used by Gopher, Armony, and Greenshpan (2000). A digit was presented on each trial, and there were three different tasks (odd vs. even; 1– 4 vs. 6 –9; or first letter A-R vs. S-Z) signaled by the location of the digit on a computer screen (top third, middle, or bottom third, respectively). Anxious participants were significantly slower than nonanxious participants on the first postswitch trial. Santos et al. (2006) carried out a more thorough investigation of the effects of anxiety on task-switching performance using the This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ANXIETY AND COGNITIVE PERFORMANCE same three tasks. In this study, there was no effect of state anxiety on performance. However, anxiety affected task-switching efficiency. Of particular significance was the finding of additional brain activation in the high-switch condition than in the no-switch one in anxious rather than nonanxious individuals in brain areas associated with central executive functioning (right BA 9/46). This is an area closely resembling the one found by Bishop et al. (2004) to be associated with effects of anxiety on attention. This additional brain activation was significantly greater in high-anxious than in low-anxious participants. The finding that impaired processing efficiency in high anxiety occurred when task switching and high levels of attentional control were required supports attentional control theory. The finding that brain areas associated with central executive functioning generally and shifting in particular were more activated in high anxiety than in low anxiety provides additional support for the theory. Prospective memory. Prospective memory tasks involve “identifying or recognizing cues as telltale signs of previously formed plans and intentions when they (the cues) occur as part of ongoing thoughts, actions, or situations” (Graf & Uttl, 2001, p. 442). As indicated previously, errors on prospective memory tasks reflect failures of attentional shifting. The cues signaling a task switch are low in salience, and high levels of performance (i.e., few errors) require an effective goal-directed plan. Thus, the adverse effects of anxiety on the goal-directed attentional system mean it should typically impair prospective memory performance. Cockburn and Smith (1994) assessed prospective memory by instructing participants to respond to hearing a timer by asking when they would see the experimenter again. There was a considerable delay between hearing those instructions and actually hearing the timer. Highly anxious participants had significantly more failures of prospective memory than did less anxious ones. Harris and Menzies (1999) used a demanding primary task (generating semantic associates to 60 spoken words and remembering the spoken words) in conjunction with a prospective memory task (placing an x beside items belonging to the categories of clothing or body parts). Performance on the prospective memory task was significantly negatively correlated with state anxiety. Similar findings were reported by Harris and Cumming (2003). Participants performed closely matched retrospective and prospective memory tasks, and the prospective memory task was carried out concurrently with a very demanding primary task. Individuals high in state anxiety performed significantly worse than those low in state anxiety on the prospective memory task. In sum, anxiety reliably impairs performance on prospective memory tasks (Cockburn & Smith, 1994; Harris & Cumming, 2003; Harris & Menzies, 1999). There is also suggestive evidence (Santos & Eysenck, 2006) that anxiety impairs task-switching performance on a task not involving prospective memory. As yet, the focus has been only on the main effect of anxiety. However, various predictions can be made from attentional control theory, based on the assumption that anxiety impairs the functioning of the goal-directed attentional system. The adverse effects of anxiety on prospective memory should be reduced or eliminated if it is made easier for anxious individuals to maintain an effective goaldirected plan (and thus attentional control) up until the time when prospective memory is tested. This could be done by making the cues for prospective memory more salient or conspicuous or by 347 shortening the time between the formation of the goal-directed plan and the testing of prospective memory. Hypothesis 6: Anxiety Impairs Processing Efficiency (and Sometimes Performance Effectiveness) on Tasks Involving the Updating Function Only Under Stressful Conditions The third function of the central executive identified by Miyake et al. (2000) is updating. According to attentional control theory, updating does not directly involve attentional control, and so anxiety does not impair the updating function under nonstressful conditions. Under stressful conditions, however, the overall demands on the central executive are increased. As a consequence, there is a reduction in processing efficiency, which may produce impaired performance on updating tasks. Two tasks assessing updating are reading span and operation span (discussed below). Miyake et al. (2000) found that the operation-span task primarily involves updating, and the readingspan task involves very similar processes (see Daneman & Merikle, 1996). Reading span is assessed by requiring participants to read a series of sentences for comprehension followed by recall of the last word in each sentence (Daneman & Carpenter, 1980). Reading span is defined as the maximum number of sentences for which all the last words can be recalled. In similar fashion, operation span involves presenting arithmetical problems, each followed by a word, and operation span is defined as the maximum number of items for which participants can remember all the last words (Turner & Engle, 1989). Reading-span and operation-span tasks differ from tasks used to assess inhibition and switching in three main ways. First, span tasks (and other tasks assessing updating) focus on memory rather than ongoing processing. Second, and related to the first point, the main dependent variable is a measure of memory capacity. According to Cowan et al. (2005), span measures such as reading and operation span provide relatively pure measures of memory capacity because task demands prevent rehearsal and grouping processes. Third, and most important, tasks used to assess reading and operation span impose few demands on attentional control. This is suggested by the pattern of findings across central executive functions reported by Miyake et al. (2000) and subsequently supported by Duff and Logie (2001) in a study on operation span. Suppose that attentional control is required to coordinate the processing of the two component tasks involved in operation span, namely, arithmetic verification and memory span. If so, then performance of the component tasks should be substantially impaired under dual-task compared with single-task conditions. In fact, there were very small impairment effects on each task when performed concurrently, indicating that operation span depends relatively little on attentional control. Similar findings were reported by Bunge, Klingberg, Jacobsen, and Gabrieli (2000). It might be argued that reading and operation span involve inhibition (e.g., of information on the primary task that is irrelevant to the memory task) and that reading span involves meaning-related inhibition. However, Friedman and Miyake (2004) found there was a small negative correlation between performance on reading span and the inhibition function based on latent-variable analyses. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 348 EYSENCK, DERAKSHAN, SANTOS, AND CALVO The effects of situational stress on reading span have been assessed in several studies. Darke (1988a) used a stressful situation (i.e., ego-threat instructions) and found reading span was significantly lower in high test-anxious participants than in low testanxious ones. Sorg and Whitney (1992) assessed reading span under nonstressful and stressful (i.e., videogame competition) conditions. High trait-anxious participants performed better than those low in trait anxiety under nonstressful conditions. Under stressful conditions, only the performance of the high-anxious group decreased. In a similar study, Santos and Eysenck (2005) investigated operation span under nonstressful (control) and stressful (i.e., close observation by experimenter; failure feedback) conditions. There were no differences in span performance between groups low and high in trait anxiety. Calvo and colleagues carried out three studies to assess the effects of test anxiety on reading span (Calvo & Eysenck, 1996; Calvo et al., 1994; Calvo, Ramos, & Estevez, 1992). In all three studies, there were nonsignificant effects of test anxiety on reading span under nonstressful conditions. In the only study including a stressful condition (Calvo et al., 1992), those high in test anxiety had lower reading span than those low in test anxiety. Dutke and Stöber (2001) used an updating task in which participants updated the number of occurrences of each of three target numbers, responding when any target had been presented three times. There were two conditions varying in the number of targets presented. In the first experiment, the main effect of anxiety was nonsignificant. However, high-anxious participants performed better than low-anxious ones when many targets were presented but worse when few targets were presented. In the second experiment, there was again no main effect of anxiety, and the high- and low-anxious groups did not differ in either condition. In sum, there are no effects of anxiety on the updating function assessed by reading or operation span when the conditions are nonstressful (Calvo & Eysenck, 1996; Calvo et al., 1994, 1992; Santos & Eysenck, 2005). With a different updating task, Dutke and Stöber (2001) found no overall effect of anxiety on performance in two experiments. When stressful conditions are used, the findings are inconsistent and difficult to interpret. Darke (1988a) and Calvo et al. (1992) found that high test anxiety was associated with impaired reading span under those conditions, Sorg and Whitney (1992) did not find clear differences between high- and low-test anxious individuals in stressful conditions, and Santos and Eysenck found no difference between individuals high and low in trait anxiety. Summary and Conclusions An important commonality between attentional control theory and processing efficiency theory is the assumption that the effects of anxiety on cognitive processing center on the central executive component of Baddeley’s (1986, 2001) working memory system. Much evidence (e.g., Derakshan & Eysenck, 1998; MacLeod & Donnellan, 1993) supports that assumption, with the clearest evidence having been reported by Eysenck et al. (2005). The advantages of attentional control theory over processing efficiency theory can be identified by reconsidering the four limitations of processing efficiency theory stated earlier. First, it was unclear within processing efficiency theory which functions of the central executive are most affected by anxiety. In contrast, atten- tional control theory identifies the basic central executive functions (i.e., shifting and inhibition) most affected by anxiety. The impaired functioning of these functions associated with attentional control increases the influence of the stimulus-driven attentional system. Second, it was simply assumed in processing efficiency theory that worry in anxious individuals reduces their processing efficiency. In attentional control theory, it is explained by the combination of impaired attentional control and preferential processing of threat-related stimuli. More generally, attentional control theory accounts for distraction effects in anxiety, and the distracting stimuli can be either external (as in most research) or internal (e.g., worry). Third, processing efficiency theory was not concerned with distraction effects, whereas such effects are regarded as important within attentional control theory. According to attentional control theory, anxiety impairs the inhibition function. The increased distractibility found in anxious individuals compared with nonanxious ones provides strong support for that assumption. Fourth, no predictions were made within processing efficiency theory concerning possible interactions between anxiety and type of stimulus (threat related vs. neutral). The effects of anxiety on attentional processes and performance depend on whether the stimuli presented are neutral or threat related (e.g., attentional bias). In contrast, attentional control theory predicts that adverse effects of anxiety on performance will be greater when taskirrelevant stimuli are threat related than when they are neutral, a prediction that has been confirmed several times. According to the theory, this prediction arises because the inhibitory function in anxious individuals is especially inefficient in the presence of threat-related distractors. Attentional control theory makes various predictions about effects of anxiety on susceptibility to distraction, dual-task performance, and task-switching performance. There is broad support for the notion that anxiety disrupts the functioning of the goal-directed attentional system, producing several effects including the following: (a) reduced ability to inhibit incorrect prepotent responses, (b) increased susceptibility to distraction, (c) impaired performance on secondary tasks in dual-task situations, and (d) impaired taskswitching performance. Attentional control theory also makes more specific predictions about the factors determining the effects of anxiety in all four areas, predictions supported by the available evidence. For example, distraction effects in high anxiety are predicted to be greater when the distracting stimuli are threat related or when the task is demanding of the resources of the central executive. In dual-task studies, attentional control theory predicts that adverse effects of anxiety on secondary task performance should occur mainly when secondary task stimuli are nonsalient or inconspicuous (e.g., presented in the periphery, less salient than primary task stimuli, or presented much less often than primary task stimuli). These predictions have all been confirmed. The predictions from attentional control theory regarding dual-task performance differ substantially from those following from Easterbrook’s (1959) hypothesis, generally regarded as the dominant theory of anxiety and attention. For example, Staal (2004, p. 33) concluded that “the majority of the field has converged on the notion that stress and workload reduce cue utilization, shrink the perceptive field, or reduce an individual’s environmental scan.” ANXIETY AND COGNITIVE PERFORMANCE Attentional control theory is based in part on attentional processes emphasized in contemporary theories of attention. It is preferable to base theories of anxiety and attention on the insights of cognitive psychologists into the nature of the human attentional system than to focus on theoretical ideas (e.g., automatic narrowing of attention) not forming part of current theories of attention. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Future Directions We consider four important future directions for research. First, there is a need for more research directly testing the theory. For example, it is assumed theoretically that the effects of anxiety on performance depend on the salience or conspicuousness of distracting stimuli, of secondary task stimuli in dual-task situations, and of the cues in prospective memory studies. However, this variable has not been systematically manipulated in these paradigms. More generally, certain assumptions of the theory have only been tested in a single paradigm and should be tested across other paradigms. For example, attentional bias in anxious individuals has been studied extensively in distraction paradigms, but has not been tested in dual-task or task-switching paradigms. According to attentional control theory, the effects of anxiety in these paradigms could be varied by manipulating the type of stimulus presented. Another theoretical assumption requiring more research is that anxiety impairs the inhibition and shifting functions of the central executive under nonstressful as well as stressful conditions, but generally impairs the updating function only under stressful conditions. There are very few studies investigating these functions in which situational stress was manipulated. Finally, there is much evidence indicating that anxiety has a greater adverse effect on processing efficiency than on performance effectiveness. However, most of the research indicating that anxiety has a greater adverse effect on processing efficiency than on performance effectiveness has used complex tasks involving various processes. Future research should consider efficiency and effectiveness in tasks providing relatively pure measures of inhibition and shifting. Second, neuroimaging offers considerable potential for testing predictions of attentional control theory because it provides a valuable way of assessing processing efficiency. Nearly all research concerned with performance effectiveness and processing efficiency has relied exclusively on behavioral evidence. With such evidence, the assessment of processing efficiency is typically indirect and inferential. In contrast, neuroimaging (e.g., fMRI), when combined with measures of performance effectiveness (e.g., Santos et al., 2006), permits an assessment of processing efficiency based on activation within brain areas associated with attention. In addition, neuroimaging may allow more precise measures of component executive systems that are very difficult to separate out with behavioral measures. Third, there is a need for more research focusing on the strategies used by anxious individuals when their processing becomes inefficient. Typically, they increase effort or motivation to maintain task performance. However, anxious individuals sometimes use other strategies. For example, they use a capacity-saving approach on analogical reasoning tasks, using suboptimal strategies that minimize demands on the central executive (Klein & Barnes, 1994; Tohill & Holyoak, 2000). Another strategy involves searching for elevated evidence requirements before responding. Thus, Geen (1985) found that anxious individuals set a more 349 stringent decision criterion than nonanxious ones for reporting signal detection of a signal. Tallis, Eysenck, and Mathews (1991) and Nichols-Hoppe and Beach (1990) obtained similar findings with different paradigms. As yet, there is insufficient knowledge of the factors determining the strategy used by anxious individuals on any given task. Fourth, according to the theory, anxious individuals have less available processing capacity in key functions of the central executive than nonanxious ones. If we compared the performance of anxious individuals on a given task with that of nonanxious individuals performing the same task while concurrently carrying out a task imposing demands on attentional control within the central executive, there should be a similar pattern of performance in both cases. This general strategy was adopted by Tohill and Holyoak (2000) and by Waltz, Lau, Grewal, and Holyoak (2000) in studies using the same test of analogical reasoning that permitted attributebased and relation-based responding. Tohill and Holyoak found that participants exposed to a stressful or anxiety-making procedure produced more attribute-based responses and fewer relationbased responses than those not exposed to it. They argued that this was because relational processing imposes greater demands on the central executive. Waltz et al. found that participants performing the analogical reasoning task concurrently with a second task requiring use of the central executive had more attribute-based and fewer relation-based responses than those not performing a second task. Thus, there were comparable effects on performance of anxiety and of increased demands on the central executive. In sum, the integration of processing efficiency theory with attentional control theory provides a reasonably comprehensive account of some cognitive processes and mechanisms determining the effects of anxiety on performance. The available evidence provides support for all of the theory’s major theoretical assumptions and indicates that it possesses some validity. It is for future research to investigate in more detail the cognitive processes altered by anxiety. References Alting, T., & Markham, R. (1993). Test anxiety and distractibility. Journal of Research in Personality, 27, 134 –137. Ashcraft, M. H., & Kirk, E. P. (2001). The relationship among working memory, math anxiety, and performance. Journal of Experimental Psychology: General, 130, 224 –237. Baddeley, A. D. (1986). Working memory. Oxford, England: Clarendon Press. Baddeley, A. D. (1996). Exploring the central executive. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 49A, 5–28. Baddeley, A. D. (2001). Is working memory still working? American Psychologist, 56, 851– 864. Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130, 553–573. Benjamin, M., McKeachie, W. J., Lin, Y. G., & Holinger, D. P. (1981). Test anxiety—Deficits in information processing. Journal of Educational Psychology, 73, 816 – 824. Bishop, S., Duncan, J., Brett, M., & Lawrence, A. D. (2004). Prefrontal cortical function and anxiety: Controlling attention to threat-related stimuli. Nature Neuroscience, 7, 184 –188. Blankstein, K. R., Flett, G. L., Boase, P., & Toner, B. B. (1990). Thought This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 350 EYSENCK, DERAKSHAN, SANTOS, AND CALVO listing and endorsement measures of self-referential thinking in test anxiety. Anxiety Research, 2, 103–111. Blankstein, K. R., Toner, B. B., & Flett, G. L. (1989). Test anxiety and the contents of consciousness: Thought listing and endorsement measures. Journal of Research in Personality, 23, 269 –286. Borkovec, T. (1994). The nature, functions and origins of worry. In G. Davey & F. Tallis (Eds.), Worrying: Perspectives on theory, assessment and treatment (pp. 5–34). Chichester, England: Wiley. Broadbent, D. E., Cooper, P. F., FitzGerald, P., & Parkes, K. R. (1982). The Cognitive Failures Questionnaire (CFQ) and its correlates. British Journal of Clinical Psychology, 21, 1–16. Bunge, S. A., Klingberg, T., Jacobsen, R. B., & Gabrieli, J. D. E. (2000). A resource model of the neural basis of executive working memory. Proceedings of the National Academy of Science USA, 97, 3573–3578. Byrne, A., & Eysenck, M. W. (1995). Trait anxiety, anxious mood, and threat detection. Cognition and Emotion, 9, 549 –562. Calvo, M. G. (1985). Effort, aversive representations and performance in test anxiety. Personality and Individual Differences, 6, 563–571. Calvo, M. G., Alamo, L., & Ramos, P. M. (1990). Test anxiety, motor performance and learning: Attentional and somatic interference. Personality and Individual Differences, 11, 29 –38. Calvo, M. G., Avero, P., & Jiménez, A. (1997). Ansiedad de evaluación: Correlatos psicológicos, conductuales y biológicos [Evaluation anxiety: Psychological, behavioral, and biological correlations]. Ansiedad y Estres, 3, 61–75. Calvo, M. G., & Cano, A. (1997). The nature of trait anxiety: Cognitive and biological vulnerability. European Psychologist, 2, 301–312. Calvo, M. G., & Carreiras, M. (1993). Selective influence of test anxiety on reading processes. British Journal of Psychology, 84, 375–388. Calvo, M. G., & Castillo, M. D. (1995). Phonological coding in reading comprehension: The importance of individual differences. European Journal of Cognitive Psychology, 7, 365–382. Calvo, M. G., & Eysenck, M. W. (1996). Phonological working memory and reading in test anxiety. Memory, 4, 289 –305. Calvo, M. G., Eysenck, M. W., Ramos, P. M., & Jiménez, A. (1994). Compensatory reading strategies in test anxiety. Anxiety, Stress, and Coping, 7, 99 –116. Calvo, M. G., & Jiménez, A. (1996). Test anxiety and integration processes in reading: Conflicting findings. Zeitschrift für Pädagogische Psychologie, 10, 67–76. Calvo, M. G., & Ramos, P. M. (1989). Effects of test anxiety on motor learning: The processing efficiency hypothesis. Anxiety Research, 2, 45–55. Calvo, M. G., Ramos, P., & Estevez, A. (1992). Test anxiety and comprehension efficiency: The role of prior knowledge and working memory deficits. Anxiety, Stress, and Coping, 5, 125–138. Calvo, M. G., Ramos, P., & Eysenck, M. W. (1993). Estres, ansiedad y lectura: Eficiencia vs. eficacia [Stress, anxiety, and reading: Efficiency vs. effectiveness]. Cognitiva, 5, 77–93. Calvo, M. G., Szabo, A., & Capafons, J. (1996). Anxiety and heart rate under psychological stress: The effects of exercise training. Anxiety, Stress, and Coping, 9, 321–337. Cockburn, J., & Smith, P. T. (1994). Anxiety and errors of prospective memory among elderly people. British Journal of Psychology, 85, 273– 282. Collette, F., & Van der Linden, M. (2002). Brain imaging of the central executive component of working memory. Neuroscience and Biobehavioral Reviews, 26, 105–125. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201–215. Cowan, N., Elliott, E. M., Saults, J. S., Morey, C. C., Mattox, S., Hismjatullina, A., & Conway, A. R. A. (2005). On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cognitive Psychology, 51, 42–100. Daneman, M., & Carpenter, P. A. (1980). Individual differences in working memory and reading. Journal of Verbal Learning and Verbal Behavior, 19, 450 – 466. Daneman, M., & Merikle, P. M. (1996). Working memory and language comprehension: A meta-analysis. Psychonomic Bulletin & Review, 3, 422– 433. Darke, S. (1988a). Anxiety and working memory capacity. Cognition and Emotion, 2, 145–154. Darke, S. (1988b). Effects of anxiety on inferential reasoning task performance. Journal of Personality and Social Psychology, 55, 499 –505. Derakshan, N., & Eysenck, M. W. (1998). Working memory capacity in high trait-anxious and repressor groups. Cognition and Emotion, 12, 697–713. Derryberry, D., & Reed, M. A. (2002). Anxiety-related attentional biases and their regulation by attentional control. Journal of Abnormal Psychology, 111, 225–236. Di Bartolo, P. M., Brown, T. A., & Barlow, D. H. (1997). Effects of anxiety on attentional allocation and task performance: An informationprocessing analysis. Behaviour Research and Therapy, 35, 1101–1111. Dornic, S. (1977). Mental load, effort, and individual differences (Report No. 509). Stockholm, Sweden: University of Stockholm, Department of Psychology. Dornic, S. (1980). Efficiency vs. effectiveness in mental work: The differential effect of stress (Report No. 568). Stockholm, Sweden: University of Stockholm, Department of Psychology. Dornic, S., & Fernaeus, S.-E. (1981). Individual differences in high-load tasks: The effect of verbal distraction (Report No. 569). Stockholm, Sweden: University of Stockholm, Department of Psychology. Duff, S. C., & Logie, R. H. (2001). Processing and storage in working memory span. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 54A, 31– 48. Duncan, J. (1995). Attention, intelligence, and the frontal lobes. In M. S. Gazzaniga (Ed.), The cognitive neurosciences (pp. 721–733). Cambridge, MA: MIT Press. Dusek, J. B., Kermis, M. D., & Mergler, N. L. (1975). Information processing in low- and high-test-anxious children as a function of grade level and verbal labeling. Developmental Psychology, 11, 651– 652. Dusek, J. B., Mergler, N. L., & Kermis, M. D. (1976). Attention, encoding, and information processing in low- and high-test-anxious children. Child Development, 47, 201–207. Dutke, S., & Stöber, J. (2001). Test anxiety, working memory, and cognitive performance: Supportive effects of sequential demands. Cognition and Emotion, 15, 381–389. Easterbrook, J. A. (1959). The effect of emotion on cue utilization and the organization of behavior. Psychological Review, 66, 187–201. Egloff, B., & Hock, M. (2001). Interactive effects of state anxiety and trait anxiety on emotional Stroop interference. Personality and Individual Differences, 31, 875– 882. Elliman, N. A., Green, M. W., Rogers, P. J., & Finch, G. M. (1997). Processing-efficiency theory and the working-memory system: Impairments associated with sub-clinical anxiety. Personality and Individual Differences, 23, 31–35. Eysenck, M. W. (1979). Anxiety, learning, and memory: A reconceptualization. Journal of Research in Personality, 13, 363–385. Eysenck, M. W. (1982). Attention and arousal: Cognition and performance. Berlin: Springer-Verlag. Eysenck, M. W. (1985). Anxiety and cognitive-task performance. Personality and Individual Differences, 6, 579 –586. Eysenck, M. W. (1989). Stress, anxiety, and intelligent performance. In D. Vickers & P. L. Smith (Eds.), Human information processing: Measures, mechanisms and models (pp. 525–534). Amsterdam: NorthHolland. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. ANXIETY AND COGNITIVE PERFORMANCE Eysenck, M. W. (1992). Anxiety: The cognitive perspective. Hove, England: Erlbaum. Eysenck, M. W. (1997). Anxiety and cognition: A unified theory. Hove, England: Psychology Press. Eysenck, M. W. (2004). Trait anxiety, repressors and cognitive biases. In J. Yiend (Ed.), Cognition, emotion and psychopathology: Theoretical, empirical and clinical directions (pp. 49 – 67). Cambridge, England: Cambridge University Press. Eysenck, M. W., & Byrne, A. (1992). Anxiety and susceptibility to distraction. Personality and Individual Differences, 13, 793–798. Eysenck, M. W., & Calvo, M. G. (1992). Anxiety and performance: The processing efficiency theory. Cognition and Emotion, 6, 409 – 434. Eysenck, M. W., & Graydon, J. (1989). Susceptibility to distraction as a function of personality. Personality and Individual Differences, 10, 681– 687. Eysenck, M. W., MacLeod, C., & Mathews, A. (1987). Cognitive functioning and anxiety. Psychological Research, 49, 189 –195. Eysenck, M. W., & Payne, S. (2006). Effects of anxiety on performance effectiveness and processing efficiency. Unpublished manuscript. Royal Holloway University of London, Egham, Surrey, UK. Eysenck, M. W., Payne, S., & Derakshan, N. (2005). Trait anxiety, visuospatial processing, and working memory. Cognition and Emotion, 19, 1214 –1228. Fox, E. (1993). Attentional bias in anxiety: A defective inhibition hypothesis. Cognition and Emotion, 8, 165–195. Fox, E., & Georgiou, G. A. (2005). The nature of attentional bias in human anxiety. In R. W. Engle, G. Sedek, U. von Hecker, & D. N. McIntosh (Eds.), Cognitive limitations in aging and psychopathology (pp. 249 – 274). Cambridge, England: Cambridge University Press. Fox, E., Russo, R., & Dutton, K. (2002). Attentional bias for threat: Evidence for delayed disengagement from emotional faces. Cognition and Emotion, 16, 355–379. Fox, E., Russo, R., & Georgiou, G. A. (2005). Anxiety modulates the degree of attentive resources required to process emotional faces. Cognitive, Affective, & Behavioral Neuroscience, 5, 396 – 404. Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control functions: A latent-variable analysis. Journal of Experimental Psychology: General, 133, 101–135. Geen, R. G. (1985). Test anxiety and visual vigilance. Journal of Personality and Social Psychology, 49, 963–970. Goodwin, A. H., & Sher, K. J. (1992). Deficits in set-shifting ability in non-clinical compulsive checkers. Journal of Psychopathology and Behavioral Assessment, 14, 81–92. Gopher, D., Armony, L., & Greenshpan, Y. (2000). Switching tasks and attention policies. Journal of Experimental Psychology: General, 129, 308 –339. Graf, P., & Uttl, B. (2001). Prospective memory: A new focus for research. Consciousness and Cognition, 10, 437– 450. Graydon, J., & Eysenck, M. W. (1989). Distraction and cognitive performance. European Journal of Cognitive Psychology, 1, 161–179. Hadwin, J., Brogan, J., & Stevenson, J. (2005). State anxiety and working memory in children: A test of processing efficiency theory. Educational Psychology, 25, 379 –393. Hamilton, V. (1978, December). The cognitive analysis of personality related to information-processing deficits with stress and anxiety. Paper presented at the meeting of the British Psychological Society, London. Harris, L. M., & Cumming, S. R. (2003). An examination of the relationship between anxiety and performance on prospective and retrospective memory tasks. Australian Journal of Psychology, 55, 51–55. Harris, L. M., & Menzies, R. G. (1999). Mood and prospective memory. Memory, 7, 117–127. Hochman, S. H. (1967). The effects of stress on Stroop color-word performance. Psychonomic Science, 9, 475– 476. 351 Hochman, S. H. (1969). Stress and response competition in children’s color-word performance. Perceptual and Motor Skills, 28, 115–118. Hopko, D. R., Ashcraft, M. H., Gute, J., Ruggiero, K. J., & Lewis, C. (1998). Mathematics anxiety and working memory: Support for the existence of a deficient inhibition mechanism. Journal of Anxiety Disorders, 12, 343–355. Ikeda, M., Iwanaga, M., & Seiwa, H. (1996). Test anxiety and working memory system. Perceptual and Motor Skills, 82, 1223–1231. Janelle, C. M., Singer, R. N., & Williams, A. M. (1999). External distraction and attentional narrowing: Visual search evidence. Journal of Sport and Exercise Psychology, 21, 70 –91. Keogh, E., Bond, F. W., French, C. C., Richards, A., & Davis, R. E. (2004). Test anxiety, susceptibility to distraction and examination performance. Anxiety, Stress, and Coping, 17, 241–252. Keogh, E., & French, C. C. (1997). The effects of mood manipulation and trait anxiety on susceptibility to distraction. Personality and Individual Differences, 22, 141–149. Keogh, E., & French, C. C. (2001). Test anxiety, evaluative stress, and susceptibility to distraction from threat. European Journal of Personality, 15, 123–141. Klein, K., & Barnes, D. (1994). The relationship of life stress to problemsolving—Task complexity and individual differences. Social Cognition, 12, 187–204. Lavie, N., Hirst, A., de Fockert, J. W., & Viding, E. (2004). Load theory of selective attention and cognitive control. Journal of Experimental Psychology: General, 133, 339 –354. MacLeod, C., & Donnellan, A. M. (1993). Individual differences in anxiety and the restriction of working memory capacity. Personality and Individual Differences, 15, 163–173. MacLeod, C., & Hagan, R. (1992). Individual differences in the selective processing of threatening information, and emotional responses to a stressful life event. Behaviour Research and Therapy, 30, 151–161. MacLeod, C., & Rutherford, E. M. (1992). Anxiety and the selective processing of emotional information: Mediating roles of awareness, trait and state variables, and personal relevance of stimulus materials. Behaviour Research and Therapy, 30, 479 – 491. Markham, R., & Darke, S. (1991). The effects of anxiety on verbal and spatial task performance. Australian Journal of Psychology, 43, 107– 111. Markowitz, A. (1969). Influence of the repression-sensitization dimension, affect value, and ego threat on incidental learning. Journal of Personality and Social Psychology, 11, 374 –380. Martin, M., Williams, R. M., & Clark, D. M. (1991). Does anxiety lead to selective processing of threat-related information? Behaviour Research and Therapy, 29, 147–160. Mathews, A., & Mackintosh, B. (1998). A cognitive model of selective processing in anxiety. Cognitive Therapy and Research, 22, 539 –560. Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24, 167–202. Miyake, A., Friedman, N. P., Emerson, M. J., Witzki, A. H., Howerter, A., & Wager, T. D. (2000). The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cognitive Psychology, 41, 49 –100. Mogg, K., & Bradley, B. P. (1998). A cognitive-motivational analysis of anxiety. Behaviour Research and Therapy, 36, 809 – 848. Mogg, K., Bradley, B. P., Dixon, C., Fisher, S., Twelftree, H., & McWilliams, A. (2000). Trait anxiety, defensiveness and selective processing of threat: An investigation using two measures of attentional bias. Personality and Individual Differences, 28, 1063–1077. Mogg, K., Bradley, B. P., Williams, R., & Mathews, A. (1993). Subliminal processing of emotional information in anxiety and depression. Journal of Abnormal Psychology, 102, 304 –311. Mogg, K., & Marden, B. (1990). Processing of emotional information in anxious subjects. British Journal of Clinical Psychology, 29, 227–229. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 352 EYSENCK, DERAKSHAN, SANTOS, AND CALVO Mogg, K., Mathews, A., Bird, C., & MacGregor-Morris, R. (1990). Effects of stress and anxiety on the processing of threat stimuli. Journal of Personality and Social Psychology, 59, 1230 –1237. Monsell, S. (2003). Task switching. Trends in Cognitive Sciences, 7, 134 –140. Monsell, S., & Driver, J. (2000). Control of cognitive processes: Attention and performance XVIII. Cambridge, MA: MIT Press. Murray, N. P., & Janelle, C. M. (2003). Anxiety and performance: A visual search examination of the processing efficiency theory. Journal of Sport and Exercise Psychology, 25, 171–187. Nichols-Hoppe, K. T., & Beach, L. R. (1990). The effects of test anxiety and task variables on pre-decisional information search. Journal of Research in Personality, 24, 163–172. Nigg, J. T. (2000). On inhibition/disinhibition in developmental psychopathology: Views from cognitive and personality psychology and a working inhibition taxonomy. Psychological Bulletin, 127, 571–598. Nottelman, E. D., & Hill, K. T. (1977). Test anxiety and off-task behavior in evaluative situations. Child Development, 48, 225–231. Pallak, M. S., Pittman, T. S., Heller, J. F., & Munson, P. (1975). The effect of arousal on Stroop color-word task performance. Bulletin of the Psychonomic Society, 6, 248 –250. Pashler, H., Johnston, J. C., & Ruthroff, E. (2001). Attention and performance. Annual Review of Psychology, 52, 629 – 651. Pishyar, R., Harris, L. M., & Menzies, R. G. (2004). Attentional bias for words and faces in social anxiety. Anxiety, Stress, and Coping, 17, 23–36. Posner, M. I., & Petersen, S. E. (1990). The attention system of the human brain. Annual Review of Neuroscience, 13, 25– 42. Power, M. J., & Dalgleish, T. (1997). Cognition and emotion: From order to disorder. Hove, England: Psychology Press. Poy, R., Eixarch, M. del C., & Ávila, C. (2004). On the relationship between attention and personality: Covert visual orienting of attention in anxiety and impulsivity. Personality and Individual Differences, 36, 1471–1481. Rapee, R. M. (1993). The utilization of working memory by worry. Behaviour Research and Therapy, 31, 617– 620. Richards, A., & French, C. C. (1990). Central versus peripheral presentation of stimuli in an emotional Stroop task. Anxiety Research, 3, 41– 49. Richards, A., French, C. C., Keogh, E., & Carter, C. (2000). Test anxiety, inferential reasoning and working memory load. Anxiety, Stress, and Coping, 13, 87–109. Rinck, M., & Becker, E. S. (2005). A comparison of attentional biases and memory biases in women with social phobia and major depression. Journal of Abnormal Psychology, 114, 62–74. Rogers, R. D., & Mansell, S. (1995). The costs of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207–231. Santos, R., & Eysenck, M. W. (2005). Effects of anxiety on dual-task performance. Unpublished manuscript. Royal Holloway University of London, Egham, Surrey, UK. Santos, R., & Eysenck, M. W. (2006). State anxiety, task switching, and performance. Unpublished manuscript. Royal Holloway University of London, Egham, Surrey, UK. Santos, R., Wall, M. B., & Eysenck, M. W. (2006). Anxiety and processing efficiency: fMRI evidence. Manuscript submitted for publication. Sarason, I. G. (1988). Anxiety, self-preoccupation and attention. Anxiety Research, 1, 3–7. Schönpflug, W. (1992). Anxiety and effort. In D. G. Forgas, T. Sosnowski, & K. Wrzesniewski (Eds.), Anxiety: Recent developments in health research (pp. 51– 62). Washington, DC: Hemisphere. Schwerdtfeger, A., & Kohlmann, C.-W. (2004). Repressive coping style and the significance of verbal-autonomic response dissociations. In U. Hentschel, G. Smith, J. G. Draguns, & W. Ehlers (Eds.), Defense mechanisms: Theoretical, research, and clinical perspectives (pp. 239 – 278). Amsterdam: Elsevier. Shapiro, K. L., & Johnson, T. L. (1987). Effects of arousal on attention to central and peripheral visual stimuli. Acta Psychologica, 66, 157–172. Shapiro, K. L., & Lim, A. (1989). The impact of anxiety on visual attention to central and peripheral visual stimuli. Behavior Research and Therapy, 27, 345–351. Smith, E. E., & Jonides, J. (1999, March 12). Storage and executive processes in the frontal lobes. Science, 283, 1657–1661. Smith, N. C., Bellamy, M., Collins, D. J., & Newell, D. (2001). A test of processing efficiency theory in a team sport. Journal of Sports Sciences, 19, 321–332. Solso, R. L., Johnson, J. E., & Schatz, G. C. (1968). Perceptual perimeters and generalized drive. Psychonomic Science, 3, 71–72. Sorg, B. A., & Whitney, P. (1992). The effect of trait anxiety and situational stress on working memory capacity. Journal of Research in Personality, 26, 235–241. Spence, K. W., Farber, I. E., & McFann, H. H. (1956). The relation of anxiety (drive) level to performance in competitional and noncompetitional paired-associates learning. Journal of Experimental Psychology, 52, 296 –305. Spence, K. W., Taylor, J., & Ketchel, R. (1956). Anxiety (drive) level and degree of competition in paired-associates learning. Journal of Experimental Psychology, 52, 306 –310. Spielberger, C. D., Gonzalez, H. P., Taylor, C. J., Anton, W. D., Algaze, B., Ross, G. R., & Westberry, L. G. (1980). Test Anxiety Inventory: Preliminary professional manual. Palo Alto, CA: Consulting Psychologists Press. Spielberger, C. D., Gorsuch, R. L., Lushene, R., Vagg, P. R., & Jacobs, G. A. (1983). Manual for the State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press. Staal, M. A. (2004). Stress, cognition, and human performance: A literature review and conceptual framework. Hanover, MD: National Aeronautics & Space Administration. Standish, R. R., & Champion, R. A. (1960). Task difficulty and drive in verbal learning. Journal of Experimental Psychology, 59, 361–365. Tallis, F., Eysenck, M. W., & Mathews, A. (1991). Elevated evidence requirements and worry. Personality and Individual Differences, 12, 21–27. Tohill, J. M., & Holyoak, K. J. (2000). The impact of anxiety on analogical reasoning. Thinking and Reasoning, 6, 27– 40. Turner, M. L., & Engle, R. W. (1989). Is working-memory capacity task dependent? Journal of Memory & Language, 28, 127–154. van den Hout, M., Tenney, N., Huygens, K., Merckelbach, H., & Kindt, M. (1995). Responding to subliminal threat cues is related to trait anxiety and emotional vulnerability: A successful replication of MacLeod and Hagan (1992). Behaviour Research and Therapy, 33, 451– 454. Wachtel, P. L. (1968). Anxiety, attention, and coping with threat. Journal of Abnormal Psychology, 73, 137–143. Wager, T. D., Jonides, J., & Reading, S. (2004). Neuroimaging studies of shifting attention: A meta-analysis. NeuroImage, 22, 1679 –1693. Watson, D., & Clark, L. A. (1984). Negative affectivity: The disposition to experience aversive emotional states. Psychological Bulletin, 96, 465– 490. Weltman, G., Smith, J. E., & Egstrom, G. H. (1971). Perceptual narrowing in novice divers. Human Factors, 8, 499 –506. Williams, A. M., Vickers, J., & Rodrigues, S. (2002). The effects of anxiety on visual search, movement kinematics, and performance in table tennis: A test of Eysenck and Calvo’s processing efficiency theory. Journal of Sport and Exercise Psychology, 24, 438 – 455. Williams, J. M., Tonymon, P., & Andersen, M. B. (1990). Effects of life-stress on anxiety and peripheral narrowing. Behavioral Medicine, 16, 174 –181. Williams, J. M., Tonymon, P., & Andersen, M. B. (1991). The effects of ANXIETY AND COGNITIVE PERFORMANCE This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. stressors and coping resources on anxiety and peripheral narrowing. Journal of Applied Sport Psychology, 3, 126 –141. Williams, J. M. G., Mathews, A., & MacLeod, C. (1996). The emotional Stroop task and psychopathology. Psychological Bulletin, 120, 3–24. Williams, J. M. G., Watts, F. N., MacLeod, C., & Mathews, A. (1997). Cognitive psychology and emotional disorders (2nd ed.). Chichester, England: Wiley. Wilson, E., & MacLeod, C. (2003). Contrasting two accounts of anxietylinked attentional bias: Selective attention to varying levels of stimulus threat intensity. Journal of Abnormal Psychology, 112, 212–218. Wood, J., Mathews, A., & Dalgleish, T. (2001). Anxiety and cognitive inhibition. Emotion, 1, 166 –181. 353 Yantis, S. (1998). Control of visual attention. In H. Pashler (Ed.), Attention (pp. 223–256). Hove, England: Psychology Press. Yiend, J., & Mathews, A. (2001). Anxiety and attention to threatening pictures. Quarterly Journal of Experimental Psychology: Human Experimental Psychology, 54A, 665– 681. Received February 7, 2006 Revision received July 31, 2006 Accepted October 16, 2006 䡲
REVIEWS Effects of stress throughout the lifespan on the brain, behaviour and cognition Sonia J. Lupien*, Bruce S. McEwen‡, Megan R. Gunnar § and Christine Heim|| Abstract | Chronic exposure to stress hormones, whether it occurs during the prenatal period, infancy, childhood, adolescence, adulthood or aging, has an impact on brain structures involved in cognition and mental health. However, the specific effects on the brain, behaviour and cognition emerge as a function of the timing and the duration of the exposure, and some also depend on the interaction between gene effects and previous exposure to environmental adversity. Advances in animal and human studies have made it possible to synthesize these findings, and in this Review a model is developed to explain why different disorders emerge in individuals exposed to stress at different times in their lives. Programming When an environmental factor that acts during a sensitive developmental period affects the structure and function of tissues, leading to effects that persist throughout life. *Université de Montréal, Mental Health Research Centre, Fernand Seguin Hôpital Louis‑H Lafontaine, Montreal, Quebec, H1N 3V2, Canada. ‡ Laboratory of Neuroendocrinology, The Rockefeller University, 1230 York Avenue, New York, New York 10021, USA. § Institute of Child Development, University of Minnesota, Minneapolis, Minnesota 55455, USA. || Department of Psychiatry, Emory University, 101 Woodruff Circle, Suite 4000, Atlanta, Georgia 30307, USA. Correspondence to S.J.L. e‑mail: sonia.lupien@ doi:10.1038/nrn2639 Published online 29 April 2009 Every day, parents observe the growing behavioural repertoires of their infants and young children, and the corresponding changes in cognitive and emotional functions. These changes are thought to relate to normal brain development, particularly the development of the hippocampus, the amygdala and the frontal lobes, and the complex circuitry that connects these brain regions. At the other end of the age spectrum, we observe changes in cognition that accompany aging in our parents. These changes are related to both normal and pathological brain processes associated with aging. Studies in animals and humans have shown that during both early childhood and old age the brain is particularly sensitive to stress, probably because it undergoes such important changes during these periods. Furthermore, research now relates exposure to early-life stress with increased reactivity to stress and cognitive deficits in adulthood, indicating that the effects of stress at different periods of life interact. Stress triggers the activation of the hypothalamuspituitary-adrenal (HPA) axis, culminating in the production of glucocorticoids by the adrenals (FIG. 1) . Receptors for these steroids are expressed throughout the brain; they can act as transcription factors and so regulate gene expression. Thus, glucocorticoids can have potentially long-lasting effects on the functioning of the brain regions that regulate their release. This Review describes the effects of stress during prenatal life, infancy, adolescence, adulthood and old age on the brain, behaviour and cognition, using data from animal (BOX 1) and human studies. Here, we propose a model that integrates the effects of stress across the lifespan, along with future directions for stress research. Prenatal stress Animal studies. In animals, exposure to stress early in life has ‘programming’ effects on the HPA axis and the brain1. A single or repeated exposure of a pregnant female to stress2 or to glucocorticoids3 increases maternal glucocorticoid secretion. A portion of these glucocorticoids passes through the placenta to reach the fetus, increasing fetal HPA axis activity and modifying brain development 4. In rats prenatal stress leads to long-term increases in HPA axis activity 5. Controlling glucocorticoid levels in stressed dams by adrenalectomy and hormone replacement prevents these effects, indicating that elevations in maternal glucocorticoids mediate the prenatal programming of the HPA axis6. Glucocorticoids are important for normal brain maturation: they initiate terminal maturation, remodel axons and dendrites and affect cell survival7; both suppressed and elevated glucocorticoid levels impair brain development and functioning. For example, administration of synthetic glucocorticoids to pregnant rats delays the maturation of neurons, myelination, glia and vasculature in the offspring, significantly altering neuronal structure and synapse formation and inhibiting neurogenesis4. Furthermore, juvenile and adult rats exposed to prenatal stress have decreased numbers of mineralocorticoid receptors (MRs) and glucocorticoid receptors (GRs) in the hippocampus, possibly because of epigenetic effects on gene transcription8. The hippocampus 434 | junE 2009 | VoluME 10 © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS Mineralocorticoid receptor A receptor that is activated by mineralocorticoids, such as aldosterone and deoxycorticosterone, as well as glucocorticoids, such as cortisol and cortisone. It also responds to progestins. Glucocorticoid receptor A receptor that is activated by cortisol, corticosterone and other glucocorticoids and is expressed in almost every cell in the body. It regulates genes controlling development, metabolism and the immune response. inhibits HPA axis activity (FIG. 1), and a prenatal stressinduced reduction in hippocampal MRs and GRs could decrease this inhibition, with a resulting increase in basal and/or stress-induced glucocorticoid secretion. In rhesus monkeys, prenatal treatment with the synthetic GR agonist dexamethasone causes a dose-dependent degeneration of hippocampal neurons, leading to a reduced hippocampal volume at 20 months of age9. Effects on other brain regions are also apparent. Rats exposed to stress during the last week of gestation have significantly decreased dendritic spine density in the anterior cingulate gyrus and orbitofrontal cortex 10. Furthermore, prenatal exposure to glucocorticoids leads to increased adult corticotropin-releasing hormone (CRH) levels in the central nucleus of the amygdala, a key region in the regulation of fear and anxiety 11. Exposure to prenatal stress has three major effects on adult behaviour: learning impairments, especially in aging rats12; enhanced sensitivity to drugs of abuse13; and increases in anxiety- and depression-related behaviours14. The impaired learning is thought to result from the effects of prenatal stress on hippocampal function15, whereas the effects on anxiety are thought to be mediated by prenatal stress-induced increases in CRH in the amygdala11. Prenatal glucocorticoid exposure affects the developing dopaminergic system, which is involved in reward- or drug-seeking behaviour 16, and it has been Frontal cortex GRs Hippocampus MRs and GRs Amygdala Hypothalamus GRs CRH AVP GRs Anterior pituitary ACTH Adrenal cortex Glucocorticoids nATuRE REVIEwS | NeuroscieNce suggested that the increased sensitivity to drugs of abuse is related to the interaction between prenatal stress, glucocorticoids and dopaminergic neurons16. Human studies. In agreement with animal data, findings from retrospective studies on children whose mothers experienced psychological stress or adverse events or received exogenous glucocorticoids during pregnancy suggest that there are long-term neurodevelopmental effects17. First, maternal stress or anxiety 18, depression19 and glucocorticoid treatment during pregnancy 17 have been linked with lower birthweight or smaller size (for gestational age) of the baby. More importantly, maternal stress, depression and anxiety have been associated with increased basal HPA axis activity in the offspring at different ages, including 6 months20, 5 years21 and 10 years22. Disturbances in child development (both neurological and cognitive) and behaviour have been associated with maternal stress23 and maternal depression during pregnancy 24, and with fetal exposure to exogenous glucocorticoids in early pregnancy 25. These behavioural alterations include unsociable and inconsiderate behaviours, attention deficit hyperactivity disorder and sleep disturbances as well as some psychiatric disorders, including depressive symptoms, drug abuse and mood and anxiety disorders. Very few studies have measured Figure 1 | The stress system. When the brain detects a threat, a coordinated physiological response involving autonomic, neuroendocrine, metabolic and immune system components is activated. A key system in the stress response that has been extensively studied is the hypothalamus-pituitary-adrenal (HPA) axis. Neurons in the medial parvocellular region of the paraventricular nucleus of the hypothalamus release corticotropinreleasing hormone (CRH) and arginine vasopressin (AVP). This triggers the subsequent secretion of adrenocorticotropic hormone (ACTH) from the pituitary gland, leading to the production of glucocorticoids by the adrenal cortex. In addition, the adrenal medulla releases catecholamines (adrenaline and noradrenaline) (not shown). The responsiveness of the HPA axis to stress is in part determined by the ability of glucocorticoids to regulate ACTH and CRH release by binding to two corticosteroid receptors, the glucocorticoid receptor (GR) and the mineralocorticoid receptor (MR). Following activation of the system, and once the perceived stressor has subsided, feedback loops are triggered at various levels of the system (that is, from the adrenal gland to the hypothalamus and other brain regions such as the hippocampus and the frontal cortex) in order to shut the HPA axis down and return to a set homeostatic point. By contrast, the amygdala, which is involved in fear processing142, activates the HPA axis in order to set in motion the stress response that is necessary to deal with the challenge. Not shown are the other major systems and factors that respond to stress, including the autonomic nervous system, the inflammatory cytokines and the metabolic hormones. All of these are affected by HPA activity and, in turn, affect HPA function, and they are also implicated in the pathophysiological changes that occur in response to chronic stress, from early experiences into adult life. Nature Reviews | Neuroscience © 2009 Macmillan Publishers Limited. All rights reserved VoluME 10 | junE 2009 | 435 REVIEWS changes in the brain as a function of prenatal stress in humans. However, a recent study showed that low birthweight combined with lower levels of maternal care was associated with reduced hippocampal volume in adulthood26. This finding is consistent with evidence that effects of prenatal stress in humans are often moderated by the quality of postnatal care, which in turn is consistent with the protracted postnatal development of the human brain. Postnatal stress Animal studies. Although in rodents the postnatal period is relatively hyporesponsive to stress (BOX 2), one of the most potent stressors for pups is separation from the dam. long separation periods (3 h or more each day) activate the pups’ HPA axis, as evidenced by increased plasma levels of adrenocorticotropic hormone and glucocorticoids27. Protracted maternal separation also reduces pituitary CRH binding sites28, and low levels of maternal care reduce GR levels in the hippocampus29. The effects of maternal deprivation extend beyond the HPA axis. Early prolonged maternal separation in rats increases the density of CRH binding sites in the prefrontal cortex, amygdala, hypothalamus, hippocampus and cerebellum, as measured post-infancy 28. In the hippocampus CRH mediates stress-related loss of branches and spines30, and in the amygdala and hypothalamus elevated CRH levels are associated with increased anxiety and HPA axis activity, respectively 31. Thus, the increase in CRH-binding sites induced by maternal separation might have negative effects over time. The longterm effects of prolonged separation depend on the age Box 1 | Models to study stress in animals and humans The hypothalamus-pituitary-adrenal axis can be activated by a wide variety of stressors. Some of the most potent are psychological or processive stressors (that is, stressors that involve higher-order sensory cognitive processing), as opposed to physiological or systemic stressors. Many psychological stressors are anticipatory in nature — that is, they are based on expectation as the result of learning and memory (for example, conditioned stimuli in animals and the anticipation of threats, real or implied, in humans) or on species-specific predispositions (for example, avoidance of open space in rodents or the threat of social rejection and negative social evaluations in humans). Animal studies allow the development of experimental protocols in which animals are submitted to acute and/or chronic stress and the resulting effects on brain and behaviour are studied. Experimental stressful ‘early-life’ manipulations in animals can be broadly split into prenatal and postnatal manipulations. Prenatal manipulations involve maternal stress, exposing the mother to synthetic glucocorticoids or maternal nutrient restriction. Postnatal manipulations include depriving the animal of maternal contact, modifying maternal behaviour and exposing the animal to synthetic glucocorticoids. In these protocols, the cause–effects relationship between stress and its impact on the brain can be demonstrated. By contrast, and because of ethical issues, the cause–effects impact of stress on the brain cannot be studied in humans, and most human studies are correlational by nature. However, there are some ‘experiments of nature’ that can be used to inform scientists about the effects of chronic exposure to early adversity on brain development and of adulthood and late-life stress effects on the brain. Intrauterine under-growth and low birth weight are considered indices of prenatal stress (including malnutrition) in humans. In terms of postnatal stress, low socio-economic status, maltreatment and war are considered adverse events. In adults and older adults, studies of chronic caregivers (spouses of patients with brain degenerative disorders, parents of chronically sick children and health-care professionals) provide a human model of the impact of chronic stress on the brain, behaviour and cognition. of the pup and the duration of the deprivation, with the effects noted above generally being greater when these separations occur earlier in infancy and last for longer durations32. Although the rodent work provides a rich framework for conceptualizing the impact of early-life stress, the fact that the rodent brain is much less developed at birth than the primate brain makes translation of the findings to humans somewhat challenging (BOX 3). nonhuman primates have more human-like brain maturation at birth and patterns of parent–offspring relations, and so provide an important bridge in the translation of the rodent findings. Studies in monkeys have shown that repeated, unpredictable separations from the mother 33, unpredictable maternal feedings34 or spontaneous maternal abusive behaviour 35 increases CRH concentrations in the cerebrospinal fluid and alters the diurnal activity of the HPA axis for months or even years after the period of adversity: cortisol levels are lower than normal early in the morning (around wake-up) and slightly higher than normal later in the day, an effect that seems to reverse over time in the absence of continued, ongoing psychosocial stress35. These diurnal effects have not been noted in rodents, but the effects on higher brain regions seem to be comparable to the rodent findings and include heightened fear behaviour 36, exaggerated startle responses33, hippocampal changes such as an increase in the intensity of non-phosphorylated neurofilament protein immunoreactivity in the dentate gyrus granule cell layer 37, and atypical development of prefrontal regions involved in emotion and behaviour control38. Human studies. A human equivalent of the rodent maternal separation paradigms might be studies of children who attend full-day, out-of-home day care centres. Studies have reported that glucocorticoid levels rise in these children over the day, more so in toddlers than in older preschool-aged children39,40. However, it is important to note that the elevated glucocorticoid levels observed are less pronounced than those observed in rodents and monkeys exposed to maternal separation. Moreover, although age accounts for most of the variation in the rise in glucocorticoid levels by late afternoon, the quality of care is also important, with less supportive care producing larger increases, especially for children who are more emotionally negative and behaviourally disorganized39. So far, there is no evidence that the elevated glucocorticoid levels associated with being in day care affect development; however, children who are exposed to poor care for long hours early in development have an increased risk of behaviour problems later in development 41. Parent–child interactions and the psychological state of the mother also influence the child’s HPA axis activity. Beginning early in the first year, when the HPA system of the infant is quite labile, sensitive parenting is associated with either smaller increases in or less prolonged activations of the HPA axis to everyday perturbations42. Maternal depression often interferes with sensitive and supportive care of the infant and young child; there is increasing evidence that offspring of depressed mothers, 436 | junE 2009 | VoluME 10 © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS Box 2 | The stress hyporesponsive period: from animals to humans Despite there being clear evidence that corticotropin-releasing hormone-containing neurons are present in the fetal rat139, in rodents noxious stimuli evoke only a subnormal hypothalamus-pituitary-adrenal (HPA) axis response during the first 2 weeks of life140. During this so-called stress hyporesponsive period (SHRP), baseline plasma glucocorticoid levels are lower than normal and are only minimally increased by exposure to a noxious stressor140. The SHRP is due to a rapid regression of the HPA axis after birth140 and may have evolved in rodents to protect the rapidly developing brain from the impact of elevated glucocorticoids. Evidence is accumulating that in children there may be a comparable hyporesponsive period that emerges in infancy and extends throughout most of childhood141. At birth, glucocorticoid levels increase sharply in response to various stressors, such as a physical examination or a heel lance. However, over the course of the first year the HPA axis becomes more insensitive to stressors. No study has assessed the exact period over which this human SHRP may occur, but in adolescents glucocorticoid levels can become elevated in response to a psychosocial stressor141, which suggests that the SHRP could extend throughout childhood. In rodents the SHRP is maintained primarily by maternal care (that is, the presence of the dam seems to suppress HPA axis activity); indeed, maternal separation is a potent inducer of a stress response, even during the SHRP. Similarly, in humans the apparent hyporesponsivity of the HPA axis might reflect the fact that during the first year of life the HPA axis comes under strong social regulation or parental buffering141. Here again, stressors that involve a lack of parental care or social contact can induce a stress response in children. especially those who were clinically depressed in the child’s early years, are at risk of heightened activity of the HPA axis43 or of developing depression during adolescence (controlling for maternal depression during adolescence)44. However, it should be noted that it can be difficult to exclude potentially confounding genetic factors in such studies. Furthermore, preschool-aged children of depressed mothers exhibit electroencephalographic alterations in frontal lobe activity that correlate with diminished empathy and other behavioural problems45. In contrast to findings of elevated glucocorticoid levels in conditions of low parental care, studies in human children exposed to severe deprivation (for example, in orphanages or other institutions), neglect or abuse report lower basal levels of glucocorticoids, similar to what has been observed in primates39. one proposed mechanism for the development of hypocortisolism is downregulation of the HPA axis at the level of the pituitary in response to chronic CRH drive from the hypothalamus46, whereas a second possible mechanism is target tissue hypersensitivity to glucocorticoids47. Importantly, this hypocortisolism in humans in response to severe stress may not be permanent: sensitive and supportive care of fostered children normalizes their basal glucocorticoid levels after only 10 weeks48. Another important finding comes from a recent study which showed that exposure to early adversity is associated with epigenetic regulation of the GR receptor, as measured in the post-mortem brains of suicide victims49. Stress in adolescence Animal studies. In rodents the period of adolescence has three stages: a prepubescent or early adolescent period from day 21 to 34, a mid-adolescent period from day 34 to 46 and a late adolescent period from day 46 to 59 (ReF. 50). In humans, adolescence is often considered to demarcate the period of sexual maturation (that is, starting with menarche in girls). Although adolescence is a time of significant brain development, particularly in the frontal lobe51, there has been relatively little research on stress during this period in rodents. In adolescent rodents, HPA function is characterized by a prolonged activation in response to stressors compared with adulthood. Moreover, prepubertal rats have a delayed rise of glucocorticoid levels and prolonged glucocorticoid release in response to several types of stressors compared with adult rats52, owing to incomplete maturation of negative-feedback systems53. In contrast to adult rats, which show a habituation of the stress response with repeated exposure to the same stressor 54, juvenile rats have a potentiated release of adrenocorticotropic hormone and glucocorticoids after repeated exposure to stress55, suggesting that the HPA axis responses to acute and chronic stress depend on the developmental stage of the animal. Compared with exposure to stress in adulthood alone, exposure to stress as both a juvenile and an adult increases basal anxiety levels in the adult 56. Moreover, exposure to juvenile stress results in greater HPA axis activation than a double exposure to stress during adulthood56, and this effect is long-lasting. These results suggest that repeated stress in adolescence leads to greater exposure of the brain to glucocorticoids than similar experiences in adulthood. The fact that the adolescent brain undergoes vigorous maturation and the fact that, in rats, the hippocampus continues to grow until adulthood suggest that the adolescent brain may be more susceptible to stressors and the concomitant exposure to high levels of glucocorticoids than the adult brain. Consistent with this hypothesis are findings that increased glucocorticoid levels before but not after puberty alter the expression of genes for nMDA (N-methyl-d-aspartate) receptor subunits in the hippocampus57. In addition, chronic, variable stress during the peripubertal juvenile period results in reduced hippocampal volume in adulthood, which is accompanied by impairments in Morris water maze navigation and delayed shutdown of the HPA response to acute stress58. These differences became evident only in adulthood58, suggesting that stress in adolescence reduces hippocampal growth. Finally, the effects of juvenile stress are long-lasting: adult rats exposed to juvenile stress exhibit reduced exploratory behaviour and poor avoidance learning 59. Moreover, stress in adolescence increases susceptibility to drugs of abuse during the adolescent period60 and in adulthood61. Human studies. Interestingly, studies in human adolescents also suggest that the adolescent period is associated with heightened basal and stress-induced activity of the HPA axis62. This could be related to the dramatic changes in sex steroid levels during this period, as these steroids influence HPA axis activity 50. However, studies of stress in adolescent rats cannot be translated directly to humans because the brain areas that are undergoing development during adolescence differ between rats and humans: although the rodent hippocampus continues to nATuRE REVIEwS | NeuroscieNce VoluME 10 | junE 2009 | 437 © 2009 Macmillan Publishers Limited. All rights reserved REVIEWS develop well into adulthood, in humans it is fully developed by 2 years of age63. The frontal cortex and amygdala continue to develop in both species, but humans have larger ontogenic bouts of development in frontal regions than do rodents (BOX 3). There are indications that the adolescent human brain might be especially sensitive to the effects of elevated levels of glucocorticoids and, by extension, to stress. Recent studies on the ontogeny of MR and GR expression show that GR mRnA levels in the prefrontal cortex are high in adolescence and late adulthood compared with in infancy, young adulthood and senescence64. This suggests that the cognitive and emotional processes that are regulated by these brain areas might be sensitive to GR-mediated regulation by glucocorticoids in an agedependent manner. Various forms of psychopathology, including depression and anxiety, increase in prevalence in adolescence65,66. Periods of heightened stress often precede the first episodes of these disorders, raising the possibility that heightened HPA reactivity during adolescence increases sensitivity to the onset of stress-related mental disorders. Adolescence is also a period in which the longlasting effects of earlier exposures to stress become evident. Adolescents who grew up in poor economic conditions have higher baseline glucocorticoid levels67, as do adolescents whose mothers were depressed in the early postnatal period44. High early-morning glucocorticoid levels that vary markedly from day to day during the transition to adolescence are not associated with depressive symptoms at that time, but they predict increased risk for depression by age 16 (ReF. 44). Although early-life stress impairs hippocampal development in rodents, there is currently little evidence Box 3 | Stress effects on the brain: timing is crucial In animals that give birth to relatively mature young (for example, primates, sheep and guinea pigs), maximal brain growth and most of the neuroendocrine maturation occurs in utero. However, in rats, rabbits and mice the mother gives birth to immature young and most of the neuroendocrine development occurs in the postnatal period17. In humans the hypothalamus-pituitary-adrenal axis is highly responsive at birth, but brain development is not finished. The volume of the hippocampal formation increases sharply until the age of 2 years, whereas amygdala volume continues to increase slowly until the late 20s63. By contrast, the development of the frontal cortex in humans takes place mostly between 8 and 14 years of age63. The late increase in prefrontal volumes is consistent with data showing that this region develops latest in terms of myelination and synaptic density in humans136. Prenatal and postnatal stress can both thus have contrasting effects in different species because perinatal manipulations will affect different stages of development as a function of the species studied. Consequently, stress in the first week of the rodent’s life is often developmentally equated with stress during the last trimester of human gestation. Significant decreases in brain volume have been reported in aged animals and humans, although most of the studies performed are cross-sectional. In men the volume of the hippocampus starts to decrease by the second decade of life, whereas in women this decrease is delayed until around 40 years of age, possibly owing to the protective effects of oestrogen137. By contrast, amygdala volume decreases around the sixth decade of life in humans63. In the frontal cortex, different subregions are differentially affected by aging. For example, aging is associated with shrinking of the dorsolateral and inferior frontal cortices, but no age effects have been reported for the anterior cingulate cortex, the frontal pole or the precentral gyrus138. of comparable effects in humans. Children exposed to physical or sexual abuse early in life do not exhibit reduced hippocampal volume (relative to whole-brain size) as adolescents, although adults with these histories do show volume reductions68. This finding holds even when the abused children have been selected for chronic post-traumatic stress disorder (PTSD), and even though in some cases they exhibit overall reductions in brain volume69. By contrast, alterations in grey matter volume and the neuronal integrity of the frontal cortex, and reduced size of the anterior cingulate cortex, have been reported in adolescents exposed to early (and continued) adversity 70. Together, these results suggest that in humans the frontal cortex, which continues to develop during adolescence, might be particularly vulnerable to the effects of stress during adolescence. By contrast, the hippocampus, which develops mainly in the first years of life, might be less affected by exposure to adversity in adolescence. Stress in adulthood Animal studies. Studies on adult stress in rodents have delineated the effects of acute versus chronic stress on brain and behaviour. The impact of acute stressors depends on the level of glucocorticoid elevations, with small increases resulting in enhanced hippocampusmediated learning and memory, and larger, prolonged elevations impairing hippocampal function 71. The inverted-u-shaped effects of acute glucocorticoid elevations might serve adaptive purposes by increasing vigilance and learning processes during acute challenges. The mechanism that underlies the acute biphasic actions of glucocorticoids on cognition involves the adrenergic system in the basolateral nucleus of the amygdala. By enhancing noradrenergic function in the amygdala, glucocorticoids have a permissive effect on the priming of long-term potentiation in the dentate gyrus by the basolateral nucleus72. This modulation of noradrenergic function by glucocorticoids has been linked to the enhanced memory for emotional events that occur under stress73. Chronic stress or chronic exogenous administration of glucocorticoids in rodents causes dendritic atrophy in hippocampal CA3 pyramidal neurons74. However, these changes take several weeks to develop and are reversed by 10 days after the cessation of the stressor 75. Chronic stress in adult rats also inhibits neurogenesis in the dentate gyrus76 and causes hippocampal volume loss77. Importantly, this volume decrease is not associated with reduced neuron numbers and is not limited to the dentate gyrus78, suggesting that reduced neurogenesis might not be the only contributing factor. The morphological changes that take place in the hippocampus after chronic stress have been related to changes in spatial learning 79, which are reversed following 21 days of withdrawal from stress80. Here, it is interesting to note that in contrast to the effects of chronic or severe stress on the brain and behaviour earlier in life, which are longlasting, effects of adulthood stress — even chronic stress — are reversed after a few weeks of non-stress. These differences between the effects of early and adulthood 438 | junE 2009 | VoluME 10 © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS stress might be related to differences in the severity of stressors to which pups and adult rats are exposed or in the development of the hippocampus at the time of exposure. Pyramidal neurons in layers II/III of the prefrontal cortex also show dendritic retraction and a reduction in spine number 81 in response to chronic stress in adulthood — this can be observed 24 h after a single forced-swim stress82 — but remodelling occurs after cessation of the stressor 83. In accordance with these findings, glucocorticoid hypersecretion is associated with reduced volume of at least the right anterior cingulate cortex in rodents84. Contrary to the reduction in hippocampal and frontal volumes, chronic stress in adult rodents leads to dendritic hypertrophy in the basolateral amygdala85. Moreover, a recent study showed that even a single acute administration of glucocorticoids caused dendritic hypertrophy in this area 12 days later 86. The dendritic hypertrophy was correlated with anxiety in both the acute86 and the chronic85 administration paradigms. Human studies. In humans, studies of the effects of acute stress confirm animal studies and report the presence of an inverted-u-shaped relationship between glucocorticoid levels and cognitive performance87. However, contrary to animal studies, in which most laboratory tests for learning and memory involve a fear and/or an emotional process88, tests of learning and memory in humans can differentiate the effects of glucocorticoids on the processing of neutral versus emotional information. Most studies to date have shown that acute glucocorticoid elevations significantly increase memory for emotional information, whereas they impair the retrieval of neutral information89. only a few reports suggest that there is an association between exposure to chronic stress and reduced hippocampal volume in individuals not suffering from mental health disorders (for a review see ReF. 90). However, a recent study reported that low self-esteem, a potent predictor of increased reactivity to stress in humans91, is associated with reduced hippocampal volume92. Most of the studies of chronic-stress effects on the adult human brain have concentrated either on stressrelated psychopathologies or on the impact of early-life stress on adult psychopathology. A large number of studies have reported elevated basal glucocorticoid levels in individuals with some forms of depression93, whereas reduced basal glucocorticoid concentrations have been reported in patients with PTSD94, although this finding has been controversial95. Given that low glucocorticoid concentrations seem to develop in early childhood in response to neglect or trauma, it is possible that low cortisol predicts vulnerability to developing PTSD in response to trauma in adulthood. Studies of adults who suffered childhood abuse also reveal hyper-reactivity of the HPA axis in abused, depressed individuals96 and hypoactivity in those with PTSD94. The changes in abused, depressed adults have been associated with CRH-induced ‘escape’ of glucocorticoid secretion from suppression by treatment with dexamethasone97, suggesting that the glucocorticoid feedback of the HPA axis is impaired under conditions of increased hypothalamic drive. Thus, a decreased capacity of glucocorticoids to inhibit the HPA axis when it is stimulated could further accentuate CnS responses to stressors. In agreement with this suggestion, increased cerebrospinal fluid CRH levels have been reported in individuals who reported childhood stress98 and childhood abuse99. Decreased hippocampal volume and function are landmark features of depression and PTSD100,101. one cross-sectional study 102 found that a smaller hippocampus in women with major depression was associated with experiences of childhood trauma, whereas depressed women without such trauma had hippocampal volumes similar to healthy controls. This supports the notion that certain brain changes in patients with depression or PTSD could represent markers of vulnerability for the disorder rather than markers of the disorder itself. This finding is in line with results from a twin study of Vietnam veterans103 which showed that decreased hippocampal volume is not a consequence of combat exposure or PTSD: decreased volume was also present in unexposed co-twins, and thus it might be a pre-existing risk factor for PTSD that could be genetic or rooted early in life. Stress in aging Animal studies. Approximately 30% of aged rats have basal glucocorticoid hypersecretion, which is correlated with memory impairments and reduced hippocampal volume104. If a middle-aged rat is exposed for a long period to high levels of exogenous glucocorticoids, it will develop memory impairments and hippocampal atrophy 105 similar to those observed in these 30% of aged rats. Conversely, artificially keeping glucocorticoid levels low in middle-aged rats prevents the emergence of both memory deficits and hippocampal atrophy in old age106. Several groups have also found that chronic stress in aged rats can accelerate the appearance of biomarkers of hippocampal aging (for example, frequency potentiation and synaptic excitability thresholds) and that excess endogenous or exogenous glucocorticoids induce hippocampal dendritic atrophy and inhibit neurogenesis107. Finally, in aged monkeys108 chronic glucocorticoid treatment can increase amyloid-β pathology, similar to that reported in Alzheimer’s disease. These results have given rise to the glucocorticoid cascade hypothesis109, which suggests that there is a relationship between cumulative exposure to high glucocorticoid levels and hippocampal atrophy. It was recently renamed the neurotoxicity hypothesis103, because the proposed explanation for this relationship is that prolonged exposure to stress hormones reduces the ability of neurons to resist insults, thus increasing the rate at which they are damaged by other toxic challenges or ordinary attrition109. Glucocorticoids might have a similar neurotoxic effect in the prefrontal cortex. A study demonstrated an enhanced elevation of extracellular glutamate levels post-stress in the hippocampus and medial prefrontal cortex of aged rats compared with young rats110. nATuRE REVIEwS | NeuroscieNce VoluME 10 | junE 2009 | 439 © 2009 Macmillan Publishers Limited. All rights reserved REVIEWS Prenatal stress Postnatal stress Birth 2 Stress in adolescence 8 Stress in adulthood 18 30 Stress in aging 60 90 Amygdala Amygdala Frontal cortex Frontal cortex Hippocampus Hippocampus Effect on HPA axis Programming effects Differentiation effects Potentiation/ incubation effects Maintenance/ manifestation effects Maintenance/ manifestation effects Outcome ↑ Glucocorticoids ↑ Glucocorticoids (maternal separation) ↑↑ Glucocorticoids ↑ Glucocorticoids (depression) ↑ Glucocorticoids (cognitive decline) ↓ Glucocorticoids (severe trauma) ↓↓ Glucocorticoids ↓ Glucocorticoids (PTSD) ↓ Glucocorticoids (PTSD) Figure 2 | The life cycle model of stress. How the effects of chronic or repeated exposure to stress (or a single exposure to severe stress) at different stages in life depend on the brain areas that are developing or declining the time of the Natureat Reviews | Neuroscience exposure. Stress in the prenatal period affects the development of many of the brain regions that are involved in regulating the hypothalamus-pituitary-adrenal (HPA) axis — that is, the hippocampus, the frontal cortex and the amygdala (programming effects). Postnatal stress has varying effects: exposure to maternal separation during childhood leads to increased secretion of glucocorticoids, whereas exposure to severe abuse is associated with decreased levels of glucocorticoids. Thus, glucocorticoid production during childhood differentiates as a function of the environment (differentiation effects). From the prenatal period onwards, all developing brain areas are sensitive to the effects of stress hormones (broken blue bars); however, some areas undergo rapid growth during a particular period (solid blue bars). From birth to 2 years of age the hippocampus is developing; it might therefore be the brain area that is most vulnerable to the effects of stress at this time. By contrast, exposure to stress from birth to late childhood might lead to changes in amygdala volume, as this brain region continues to develop until the late 20s. During adolescence the hippocampus is fully organized, the amygdala is still developing and there is an important increase in frontal volume. Consequently, stress exposure during this period should have major effects on the frontal cortex. Studies show that adolescents are highly vulnerable to stress, possibly because of a protracted glucocorticoid response to stress that persists into adulthood (potentiation/incubation effects). In adulthood and during aging the brain regions that undergo the most rapid decline as a result of aging (red bars) are highly vulnerable to the effects of stress hormones. Stress during these periods can lead to the manifestation of incubated effects of early adversity on the brain (manifestation effects) or to maintenance of chronic effects of stress (maintenance effects). PTSD, post-traumatic stress disorder. Increased glutamate levels after stress, and perhaps other neurotoxic insults, might thus increase the vulnerability of the aging brain to neuronal damage. Human studies. Aging, healthy humans exhibit higher mean diurnal levels of cortisol than younger individuals111, and a longitudinal study has found that elevated plasma glucocorticoid levels over years in older adults negatively correlates with hippocampal volume and memory 112. Given that aged individuals with Alzheimer’s disease present both memory impairments and hippocampal atrophy, studies have assessed basal glucocorticoid levels in this population and found that they are higher than in controls113. In addition, chronic glucocorticoid treatment has been shown to worsen cognition in people with Alzheimer’s disease114. The frontal lobe also seems to be sensitive to glucocorticoid effects during human aging. using a novel in vitro post-mortem tracing method on human brain slices, Dai et al.115 found an inverted-u-shaped effect of glucocorticoids on axonal transport in prefrontal neurons with, in most cases, a stimulating effect at low concentrations and a depressing effect at high concentrations. Given that axonal transport plays a crucial part in neuronal survival and function, these results suggest that glucocorticoids potentially have negative effects on prefrontal cortex neurons’ survival and/or function. A model of stress effects throughout life The data obtained in animals and humans suggest that chronic or repeated exposure to stress has enduring effects on the brain, through activation of the HPA axis and the release of glucocorticoids, with the highest impact on those structures that are developing at the time of the stress exposure (in young individuals) and those that are undergoing age-related changes (in adult and aged individuals). Stress in the prenatal period affects the development of many of the brain regions that have a role in regulating the HPA axis — that is, the hippocampus, the frontal cortex and the amygdala (programming effects (FIG. 2)). During childhood the hippocampus — which continues to develop after birth — might be the brain region that is most vulnerable to the effects of chronic stress, possibly through a process of increased CRH drive in the hippocampus116. Because it modulates HPA axis activity, altered functioning of the hippocampus 440 | junE 2009 | VoluME 10 © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS might cause glucocorticoid hyposecretion in cases of severe abuse, or increased basal cortisol levels in cases of maternal deprivation (differentiation effects (FIG. 2)). By contrast, in adolescence the frontal cortex, which undergoes major development at this stage, may be most vulnerable to the effects of stress, possibly leading to a protracted glucocorticoid response to stress that persists into adulthood (potentiation/incubation effects (FIG. 2)). In adulthood and old age the brain regions that undergo the most rapid decline as a result of aging are highly vulnerable to the effects of stress hormones. For example, in the hippocampus glucocorticoids affect neurogenesis, neuronal survival rate and dendritic arborization (manifestation/maintenance effects (FIG. 2)). The neurotoxicity and vulnerability hypotheses. The data obtained in adults and older animals and humans have led to the neurotoxicity hypothesis109, which suggests that prolonged exposure to glucocorticoids reduces the ability of neurons to resist insults, increasing the rate at which they are damaged by other toxic challenges or ordinary attrition109. This hypothesis implies that a reduced hippocampal size is the end product of years or decades of PTSD, depressive symptoms or chronic stress. Although the neurotoxicity hypothesis has been confirmed by various animal and human studies, it does not explain the hyposecretion of glucocorticoids that occurs in patients suffering from PTSD, who also present reduced hippocampal volume. Data obtained in children, adolescents or adult animals and humans exposed to acute or early-life trauma have led to the vulnerability hypothesis103. In contrast to the neurotoxicity hypothesis, the vulnerability hypothesis suggests that reduced hippocampal volume in adulthood is not a consequence of chronic exposure to PTSD, depression or chronic stress, but is a pre-existing risk factor for stress-related disorders that is induced by genetics and/or early exposure to stress117. unlike the neurotoxicity hypothesis, the vulnerability hypothesis can explain glucocorticoid hyposecretion in patients with PTSD. Indeed, studies in children facing significant adversity, such as abuse, report the development of glucocorticoid hyposecretion39, which might last until adulthood and confer vulnerability to developing PTSD as a result of trauma. we think that the two hypotheses are not mutually exclusive when viewed from a developmental perspective. Indeed, the data summarized in this Review suggest that there might be early windows of vulnerability (or sensitive periods68) during which specific regions of the developing brain are most susceptible to environmental influences, through a neurotoxicity process. Exposure to stress and/or adversity during these key vulnerable periods might slow the development of those brain regions for the duration of the adversity. when measured in adulthood, the reduced volumes of these brain regions could be a strong marker of the time of exposure to early adversity rather than of the effects of specific traumas on various brain regions. These windows of vulnerability could also be used to predict the nature of the psychopathology that will result from exposure to stress at different ages. Exposure to adversity at the time of hippocampal development could lead to hippocampusdependent emotional disorders, which would be different from disorders arising from exposure to adversity at times of frontal cortex development. Two recent studies support this hypothesis. The first reported that women who experienced trauma before the age of 12 years had increased risk for major depression, whereas women who experienced trauma between 12 and 18 years of age more frequently developed PTSD118. The second study reported that repeated episodes of sexual abuse were associated with reduced hippocampal volume if the abuse occurred early in childhood, but with reduced prefrontal cortex volume if the abuse occurred during adolescence119. These results suggest that, similar to what has been observed in animals120, there may be distinct structural, neuropsychological and neuropsychiatric sequelae of early abuse, depending in part on the age or developmental stage of the brain when the insult occurred. Besides slowing down the development of the brain during the time of adversity, leading to reduced brain volumes in adulthood, stress in early life could modify the developmental trajectory of the brain. The potential immediate benefit of such modifications is that they might increase acute survival probability, but they could have negative long-term effects. During childhood and adolescence the brain undergoes a period of overproduction and pruning of synapses121. one of the brain regions that shows the slowest development over the lifespan is the amygdala (BOX 3). It is interesting to note that contrary to the hippocampus and the frontal lobe — which show volume reduction as a result of chronic stress — the amygdala increases in volume under chronic stress, owing to increased dendritic arborization. Given that the amygdala plays a significant part in the detection of fear and threat, it is possible that throughout evolution increases in amygdala volume in response to stress might have improved the detection of threatening information and so increased survival probability. If this is indeed the case, young children exposed to adversity should also have increased amygdala volume, but no study has yet examined this important question. This acute effect of adversity on brain organization could have negative long-term consequences. Stress at key periods of synaptic organization could modify the trajectories of connections, leading to an incubation period, such that the effects of stress would not be apparent at the time of adversity but would emerge later, when the synaptic organization has been completed. Studies showing protracted effects of early-life stress that emerge at puberty support this suggestion44. Furthermore, although depression is the most extensively documented outcome of exposure to chronic sexual abuse in adults, it is not a common occurrence in children suffering abuse. Indeed, the average time from the onset of abuse to the emergence of clinical depression is 11.5 years, with the first major episode occurring during adolescence122. It is thus conceivable that in susceptible individuals exposure to early adversity during a window of vulnerability sets into motion a series of events that lead to a heterotypic reorganization of synaptic development, resulting in a protracted expression of depression or PTSD. nATuRE REVIEwS | NeuroscieNce VoluME 10 | junE 2009 | 441 © 2009 Macmillan Publishers Limited. All rights reserved REVIEWS This same process could also explain the development of resilience in face of adversity. Environmental enrichment in rodents is a potent inducer of changes in neurogenesis and/or dendritic arborization in the hippocampus, and has been documented to lead to increases in hippocampal volume123. In children facing early adversity, forms of environmental enrichment, such as support from a family member, enriched day care or school environment or social support from members of the community, could induce a similar heterotypic reorganization of synaptic development, programming of neurotrophic factors or changes in gene expression that could lead to resilience later in life. If this is the case, it could be suggested that any type of intervention performed during the early years could not only have a tremendous effect in preventing the deleterious impact of chronic stress and/or early abuse on the developing brain, but could also help to prevent effects on the brain of chronic stress occurring in adulthood or during aging. Conclusions and future directions Although studies on stress have provided a wealth of data delineating the effects of acute and chronic stress on the developing brain, much remains to be done to fully understand how the brain develops pathology or resilience in the face of adversity. we believe that three main factors should receive special consideration in future studies on stress in both animals and humans. The first factor is sex and gender. Sex refers to the biological differences between males and females, whereas gender refers to the different roles (gender role and gender identity) that men and women may have during their lifetime. Both sex and gender might have potent influences on stress reactivity in humans of all ages. However, most studies of the effects of stress on the brain, behaviour and cognition have tested only male animals or humans. This is a major issue considering that studies in both animals50 and humans124 report sex differences in response to stress, and considering the gender gap ratio (two girls for one boy) that emerges in early adolescence for the risk of depression125. To this day, a consistent finding in the endocrine literature is that the risk of depression in adolescent girls increases with decreasing age at menarche126. An increased sensitivity of girls to environmental and/or family adversity, along with interactions between glucocorticoids and gonadal steroids, could be a potential explanation for the increased risk of depressive disorders in females. Recent results showing an earlier age at menarche in girls exposed to early adversity 127 support this suggestion. The second factor that should be considered in future studies is exposure to environmental toxins. Today, children in many cities are chronically exposed, at background levels, to a range of common toxins that are environmentally persistent and that tend to be lipophilic and bioaccumulate, such as lead and bisphenol A 128. These agents reach humans mainly through food and food additives, and they can be transferred to the fetus through the placenta and to infants through maternal milk129. They have been shown to affect the endocrine system in laboratory animals and in wildlife, and consequently have been called ‘endocrine-disrupting chemicals’ (ReF. 130). A recent study showed that prenatal and postnatal exposure to lead is associated with increased glucocorticoid responses to acute stress in children131. Also, perinatal exposure to endocrine-disrupting chemicals is associated with an earlier age at menarche among girls132. Taken together, these results suggest that both the timing of sexual maturation and stress reactivity may be sensitive to relatively low levels of endocrine-disrupting chemicals in the environment. The third factor that should receive greater attention is circadian rhythmicity. Sleep deprivation, shift work and jet lag all disrupt normal biological rhythms and have major impacts on health. Interestingly, circadian disorganization is often observed in stress-related disorders such as depression133 and PTSD134. The discovery of the molecular clock that is responsible for the generation of circadian rhythms135 provides new insights into how rhythm abnormalities might lead to greater vulnerability to stress at various ages. Most studies performed in animals and humans do not measure the circadian fluctuations in glucocorticoid levels, but rather concentrate on specific time points across the day. Although such measurements are easier, they do not provide the full spectrum of circadian variations, which could inform us about specific changes in circadian organization in response to chronic stress across the lifespan. Consequently, studies assessing multiple time points for glucocorticoid secretion across a whole day or several days are needed in order to document the complex relationships that exist between reactivity to stress and circadian (dis)organization. Animal and human studies have provided a wealth of results showing the negative effects of chronic exposure to stress and/or adversity on the developing brain. However, stress is not and should not be considered as a negative concept only. Stress is a physiological response that is necessary for the survival of the species. The stress response that today can have negative consequences for brain development and mental health may have conferred the necessary tools to our ancestors in prehistorical times for surviving in the presence of predators. Studies of modern individuals who have developed resilience by facing significant adversity should inform us about the physiological and psychological mechanisms at the basis of vulnerability or resilience to stress. understanding these mechanisms, which are possibly rooted in genes and modulated by the family environment, is extremely important if one wants to provide interventions early enough to individuals who are the most likely to respond to them. This article has reviewed the potential for early intervention to prevent the deleterious effects of stress on the brain, behaviour and cognition. After more than 30 years of research on the negative effects of stress on the brain, it is now time to turn our attention to the potential positive impact of early interventions on brain development. These results could help us to develop social policies that treat the problem of early-life stress at its root — that is, in the family home. 442 | junE 2009 | VoluME 10 © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. Barker, D. J. The foetal and infant origins of inequalities in health in Britain. J. Public Health Med. 13, 64–68 (1991). Cadet, R., Pradier, P., Dalle, M. & Delost, P. Effects of prenatal maternal stress on the pituitary adrenocortical reactivity in guinea-pig pups. J. Dev. Physiol. 8, 467–475 (1986). Dean, F. & Matthews, S. G. Maternal dexamethasone treatment in late gestation alters glucocorticoid and mineralocorticoid receptor mRNA in the fetal guinea pig brain. Brain Res. 846, 253–259 (1999). Seckl, J. R. Glucocorticoids, developmental ‘programming’ and the risk of affective dysfunction. Prog. Brain Res. 167, 17–34 (2008). A superb review that summarized prenatal work and linked it to clinical implications. Koehl, M. et al. Prenatal stress alters circadian activity of hypothalamo-pituitary-adrenal axis and hippocampal corticosteroid receptors in adult rats of both gender. J. Neurobiol. 40, 302–315 (1999). Barbazanges, A., Piazza, P. V., Le Moal, M. & Maccari, S. Maternal glucocorticoid secretion mediates long-term effects of prenatal stress. J. Neurosci. 16, 3943–3949 (1996). Meyer, J. S. Early adrenalectomy stimulates subsequent growth and development of the rat brain. Exp. Neurol. 82, 432–446 (1983). Weaver, I. C. et al. Epigenetic programming by maternal behavior. Nature Neurosci. 7, 847–854 (2004). The first paper to show that early experience has epigenetic effects, altering methylation patterns. Uno, H. et al. Brain damage induced by prenatal exposure to dexamethasone in fetal rhesus macaques. I. Hippocampus. Brain Res. Dev. Brain Res. 53, 157–167 (1990). Murmu, M. S. et al. Changes of spine density and dendritic complexity in the prefrontal cortex in offspring of mothers exposed to stress during pregnancy. Eur. J. Neurosci. 24, 1477–1487 (2006). Cratty, M. S., Ward, H. E., Johnson, E. A., Azzaro, A. J. & Birkle, D. L. Prenatal stress increases corticotropinreleasing factor (CRF) content and release in rat amygdala minces. Brain Res. 675, 297–302 (1995). Vallee, M. et al. Long-term effects of prenatal stress and postnatal handling on age-related glucocorticoid secretion and cognitive performance: a longitudinal study in the rat. Eur. J. Neurosci. 11, 2906–2916 (1999). Deminiere, J. M. et al. Increased locomotor response to novelty and propensity to intravenous amphetamine self-administration in adult offspring of stressed mothers. Brain Res. 586, 135–139 (1992). Vallee, M. et al. Prenatal stress induces high anxiety and postnatal handling induces low anxiety in adult offspring: correlation with stress-induced corticosterone secretion. J. Neurosci. 17, 2626–2636 (1997). Lemaire, V., Koehl, M., Le Moal, M. & Abrous, D. N. Prenatal stress produces learning deficits associated with an inhibition of neurogenesis in the hippocampus. Proc. Natl Acad. Sci. USA 97, 11032–11037 (2000). Piazza, P. V. & Le Moal, M. L. Pathophysiological basis of vulnerability to drug abuse: role of an interaction between stress, glucocorticoids, and dopaminergic neurons. Annu. Rev. Pharmacol. Toxicol. 36, 359–378 (1996). Kapoor, A., Petropoulos, S. & Matthews, S. G. Fetal programming of hypothalamic-pituitary-adrenal (HPA) axis function and behavior by synthetic glucocorticoids. Brain Res. Rev. 57, 586–595 (2008). Hedegaard, M., Henriksen, T. B., Sabroe, S. & Secher, N. J. Psychological distress in pregnancy and preterm delivery. BMJ 307, 234–239 (1993). Orr, S. T. & Miller, C. A. Maternal depressive symptoms and the risk of poor pregnancy outcome. Review of the literature and preliminary findings. Epidemiol. Rev. 17, 165–171 (1995). Lyons-Ruth, K., Wolfe, R. & Lyubchik, A. Depression and the parenting of young children: making the case for early preventive mental health services. Harv. Rev. Psychiatry 8, 148–153 (2000). Gutteling, B. M., de Weerth, C. & Buitelaar, J. K. Prenatal stress and children’s cortisol reaction to the first day of school. Psychoneuroendocrinology 30, 541–549 (2005). O’Connor, T. G. et al. Prenatal anxiety predicts individual differences in cortisol in pre-adolescent children. Biol. Psychiatry 58, 211–217 (2005). Glover, V. Maternal stress or anxiety in pregnancy and emotional development of the child. Br. J. Psychiatry 171, 105–106 (1997). 24. Stott, D. H. Follow-up study from birth of the effects of prenatal stresses. Dev. Med. Child. Neurol. 15, 770–787 (1973). 25. Trautman, P. D., Meyer-Bahlburg, H. F., Postelnek, J. & New, M. I. Effects of early prenatal dexamethasone on the cognitive and behavioral development of young children: results of a pilot study. Psychoneuroendocrinology 20, 439–449 (1995). 26. Buss, C. et al. Maternal care modulates the relationship between prenatal risk and hippocampal volume in women but not in men. J. Neurosci. 27, 2592–2595 (2007). 27. Levine, S. & Wiener, S. G. Psychoendocrine aspects of mother-infant relationships in nonhuman primates. Psychoneuroendocrinology 13, 143–154 (1988). 28. Anisman, H., Zaharia, M. D., Meaney, M. J. & Merali, Z. Do early-life events permanently alter behavioral and hormonal responses to stressors? Int. J. Dev. Neurosci. 16, 149–164 (1998). 29. Liu, D. et al. Maternal care, hippocampal glucocorticoid receptors, and hypothalamic-pituitary-adrenal responses to stress. Science 277, 1659–1662 (1997). 30. Fenoglio, K. A., Brunson, K. L. & Baram, T. Z. Hippocampal neuroplasticity induced by early-life stress: functional and molecular aspects. Front. Neuroendocrinol. 27, 180–192 (2006). 31. Schulkin, J., Gold, P. W. & McEwen, B. S. Induction of corticotropin-releasing hormone gene expression by glucocorticoids: implication for understanding the states of fear and anxiety and allostatic load. Psychoneuroendocrinology 23, 219–243 (1998). 32. de Kloet, E. R. & Oitzl, M. S. Who cares for a stressed brain? The mother, the kid or both? Neurobiol. Aging 24 (Suppl. 1), S61–S65; discussion S67–S68 (2003). 33. Sanchez, M. M. et al. Alterations in diurnal cortisol rhythm and acoustic startle response in nonhuman primates with adverse rearing. Biol. Psychiatry 57, 373–381 (2005). 34. Coplan, J. D. et al. Persistent elevations of cerebrospinal fluid concentrations of corticotropinreleasing factor in adult nonhuman primates exposed to early-life stressors: implications for the pathophysiology of mood and anxiety disorders. Proc. Natl Acad. Sci. USA 93, 1619–1623 (1996). 35. Sanchez, M. M. The impact of early adverse care on HPA axis development: nonhuman primate models. Horm. Behav. 50, 623–631 (2006). 36. Rosenblum, L. A. et al. Differing concentrations of corticotropin-releasing factor and oxytocin in the cerebrospinal fluid of bonnet and pigtail macaques. Psychoneuroendocrinology 27, 651–660 (2002). 37. Siegel, S. J. et al. Effects of social deprivation in prepubescent rhesus monkeys: immunohistochemical analysis of the neurofilament protein triplet in the hippocampal formation. Brain Res. 619, 299–305 (1993). 38. Sanchez, M. M., Ladd, C. O. & Plotsky, P. M. Early adverse experience as a developmental risk factor for later psychopathology: evidence from rodent and primate models. Dev. Psychopathol. 13, 419–449 (2001). 39. Gunnar, M. R. & Donzella, B. Social regulation of the cortisol levels in early human development. Psychoneuroendocrinology 27, 199–220 (2002). 40. Geoffroy, M. C., Cote, S. M., Parent, S. & Seguin, J. R. Daycare attendance, stress, and mental health. Can. J. Psychiatry 51, 607–615 (2006). 41. NICHD Early Child Care Research Network. Early child care and children’s development prior to school entry: results from the NICHD Study of Early Child Care. Am. Educ. Res. J. 39, 133–164 (2002). 42. Albers, E. M., Riksen-Walraven, J. M., Sweep, F. C. & de Weerth, C. Maternal behavior predicts infant cortisol recovery from a mild everyday stressor. J. Child. Psychol. Psychiatry 49, 97–103 (2008). 43. Lupien, S. J., King, S., Meaney, M. J. & McEwen, B. S. Child’s stress hormone levels correlate with mother’s socioeconomic status and depressive state. Biol. Psychiatry 48, 976–980 (2000). 44. Halligan, S. L., Herbert, J., Goodyer, I. & Murray, L. Disturbances in morning cortisol secretion in association with maternal postnatal depression predict subsequent depressive symptomatology in adolescents. Biol. Psychiatry 62, 40–46 (2007). Provided some of the first evidence that adverse early life experiences in humans, in this case rearing by a mother suffering from post-partum depression, are associated with heightened HPA activity years later, and that the HPA axis hyperactivity mediates the association between early risk exposure and later psychiatric symptoms. nATuRE REVIEwS | NeuroscieNce 45. Jones, N. A., Field, T. & Davalos, M. Right frontal EEG asymmetry and lack of empathy in preschool children of depressed mothers. Child. Psychiatry Hum. Dev. 30, 189–204 (2000). 46. Fries, E., Hesse, J., Hellhammer, J. & Hellhammer, D. H. A new view on hypocortisolism. Psychoneuroendocrinology 30, 1010–1016 (2005). 47. Yehuda, R., Yang, R. K., Buchsbaum, M. S. & Golier, J. A. Alterations in cortisol negative feedback inhibition as examined using the ACTH response to cortisol administration in PTSD. Psychoneuroendocrinology 31, 447–451 (2006). 48. Gunnar, M. R. & Quevedo, K. M. Early care experiences and HPA axis regulation in children: a mechanism for later trauma vulnerability. Prog. Brain Res. 167, 137–149 (2008). 49. McGowan, P. O. et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nature Neurosci. 12, 342–348 (2009). This study examined epigenetic differences in a neuron-specific glucocorticoid receptor (NR3C1) promoter between post-mortem hippocampus obtained from suicide victims with a history of childhood abuse and hippocampus from either suicide victims with no childhood abuse or controls. It found decreased levels of glucocorticoid receptor mRNA, as well as mRNA transcripts bearing the glucocorticoid receptor 1F splice variant and increased cytosine methylation of an NR3C1 promoter in suicide victims with early abuse. 50. McCormick, C. M. & Mathews, I. Z. HPA function in adolescence: role of sex hormones in its regulation and the enduring consequences of exposure to stressors. Pharmacol. Biochem. Behav. 86, 220–233 (2007). A very good review on the acute and chronic effects of stress during adolescence. 51. O’Donnell, S., Noseworthy, M. D., Levine, B. & Dennis, M. Cortical thickness of the frontopolar area in typically developing children and adolescents. Neuroimage 24, 948–954 (2005). 52. Vazquez, D. M. & Akil, H. Pituitary-adrenal response to ether vapor in the weanling animal: characterization of the inhibitory effect of glucocorticoids on adrenocorticotropin secretion. Pediatr. Res. 34, 646–653 (1993). 53. Goldman, L., Winget, C., Hollingshead, G. W. & Levine, S. Postweaning development of negative feedback in the pituitary-adrenal system of the rat. Neuroendocrinology 12, 199–211 (1973). 54. Girotti, M. et al. Habituation to repeated restraint stress is associated with lack of stress-induced c-fos expression in primary sensory processing areas of the rat brain. Neuroscience 138, 1067–1081 (2006). 55. Romeo, R. D. et al. Stress history and pubertal development interact to shape hypothalamic-pituitary-adrenal axis plasticity. Endocrinology 147, 1664–1674 (2006). 56. Avital, A. & Richter-Levin, G. Exposure to juvenile stress exacerbates the behavioural consequences of exposure to stress in the adult rat. Int. J. Neuropsychopharmacol. 8, 163–173 (2005). 57. Lee, P. R., Brady, D. & Koenig, J. I. Corticosterone alters N-methyl-d-aspartate receptor subunit mRNA expression before puberty. Brain Res. Mol. Brain Res. 115, 55–62 (2003). 58. Isgor, C., Kabbaj, M., Akil, H. & Watson, S. J. Delayed effects of chronic variable stress during peripubertaljuvenile period on hippocampal morphology and on cognitive and stress axis functions in rats. Hippocampus 14, 636–648 (2004). One of the first papers to show protracted effects of adolescent stress on adulthood stress reactivity in rodents. 59. Tsoory, M. & Richter-Levin, G. Learning under stress in the adult rat is differentially affected by ‘juvenile’ or ‘adolescent’ stress. Int. J. Neuropsychopharmacol. 9, 713–728 (2006). 60. Kabbaj, M., Isgor, C., Watson, S. J. & Akil, H. Stress during adolescence alters behavioral sensitization to amphetamine. Neuroscience 113, 395–400 (2002). 61. McCormick, C. M., Robarts, D., Gleason, E. & Kelsey, J. E. Stress during adolescence enhances locomotor sensitization to nicotine in adulthood in female, but not male, rats. Horm. Behav. 46, 458–466 (2004). 62. Gunnar, M. R., Wewerka, S., Frenn, K., Long, J. D. & Griggs, C. Developmental changes in hypothalamuspituitary-adrenal activity over the transition to adolescence: normative changes and associations with puberty. Dev. Psychopathol. 21, 69–85 (2009). VoluME 10 | junE 2009 | 443 © 2009 Macmillan Publishers Limited. All rights reserved REVIEWS 63. Giedd, J. N. et al. Quantitative magnetic resonance imaging of human brain development: ages 4–18. Cereb. Cortex 6, 551–560 (1996). 64. Perlman, W. R., Webster, M. J., Herman, M. M., Kleinman, J. E. & Weickert, C. S. Age-related differences in glucocorticoid receptor mRNA levels in the human brain. Neurobiol. Aging 28, 447–458 (2007). 65. Dahl, R. E. Adolescent brain development: a period of vulnerabilities and opportunities. Keynote address. Ann. NY Acad. Sci. 1021, 1–22 (2004). 66. Paus, T., Keshavan, M. & Giedd, J. N. Why do many psychiatric disorders emerge during adolescence? Nature Rev. Neurosci. 9, 947–957 (2008). A very interesting review on the state of research into why adolescents have a greater vulnerability to mental health disorders. 67. Evans, G. W. & English, K. The environment of poverty: multiple stressor exposure, psychophysiological stress, and socioemotional adjustment. Child Dev. 73, 1238–1248 (2002). 68. Andersen, S. L. & Teicher, M. H. Stress, sensitive periods and maturational events in adolescent depression. Trends Neurosci. 31, 183–191 (2008). 69. De Bellis, M. D. et al. A. E. Bennett Research Award. Developmental traumatology. Part II: brain development. Biol. Psychiatry 45, 1271–1284 (1999). One of the first clear demonstrations that, in children who were physically healthy at birth, severe abuse in the early years of life is associated with reduced brain volume. The reduction correlates negatively with the age of onset and positively with the duration of the maltreatment. 70. Cohen, R. A. et al. Early life stress and morphometry of the adult anterior cingulate cortex and caudate nuclei. Biol. Psychiatry 59, 975–982 (2006). 71. Diamond, D. M., Bennett, M. C., Fleshner, M. & Rose, G. M. Inverted-U relationship between the level of peripheral corticosterone and the magnitude of hippocampal primed burst potentiation. Hippocampus 2, 421–430 (1992). 72. Vouimba, R. M., Yaniv, D. & Richter-Levin, G. Glucocorticoid receptors and β-adrenoceptors in basolateral amygdala modulate synaptic plasticity in hippocampal dentate gyrus, but not in area CA1. Neuropharmacology 52, 244–252 (2007). 73. Roozendaal, B., Brunson, K. L., Holloway, B. L., McGaugh, J. L. & Baram, T. Z. Involvement of stressreleased corticotropin-releasing hormone in the basolateral amygdala in regulating memory consolidation. Proc. Natl Acad. Sci. USA 99, 13908–13913 (2002). 74. Magarinos, A. M. & McEwen, B. S. Stress-induced atrophy of apical dendrites of hippocampal CA3c neurons: involvement of glucocorticoid secretion and excitatory amino acid receptors. Neuroscience 69, 89–98 (1995). 75. Conrad, C. D., LeDoux, J. E., Magarinos, A. M. & McEwen, B. S. Repeated restraint stress facilitates fear conditioning independently of causing hippocampal CA3 dendritic atrophy. Behav. Neurosci. 113, 902–913 (1999). 76. Gould, E., McEwen, B. S., Tanapat, P., Galea, L. A. & Fuchs, E. Neurogenesis in the dentate gyrus of the adult tree shrew is regulated by psychosocial stress and NMDA receptor activation. J. Neurosci. 17, 2492–2498 (1997). 77. McEwen, B. S. Effects of adverse experiences for brain structure and function. Biol. Psychiatry 48, 721–731 (2000). 78. Pham, K., Nacher, J., Hof, P. R. & McEwen, B. S. Repeated restraint stress suppresses neurogenesis and induces biphasic PSA-NCAM expression in the adult rat dentate gyrus. Eur. J. Neurosci. 17, 879–886 (2003). 79. McEwen, B. S. Plasticity of the hippocampus: adaptation to chronic stress and allostatic load. Ann. NY Acad. Sci. 933, 265–277 (2001). 80. Luine, V., Villegas, M., Martinez, C. & McEwen, B. S. Repeated stress causes reversible impairments of spatial memory performance. Brain Res. 639, 167–170 (1994). 81. Joels, M., Karst, H., Krugers, H. J. & Lucassen, P. J. Chronic stress: implications for neuronal morphology, function and neurogenesis. Front. Neuroendocrinol. 28, 72–96 (2007). 82. Izquierdo, A., Wellman, C. L. & Holmes, A. Brief uncontrollable stress causes dendritic retraction in infralimbic cortex and resistance to fear extinction in mice. J. Neurosci. 26, 5733–5738 (2006). 83. Shansky, R. M., Hamo, C., Hof, P. R., McEwen, B. S. & Morrison, J. H. Stress-induced dendritic remodeling in the prefrontal cortex is circuit specific. Cereb. Cortex 4 Feb 2009 (doi:10.1093/cercor/bhp003). 84. Cerqueira, J. J. et al. Corticosteroid status influences the volume of the rat cingulate cortex - a magnetic resonance imaging study. J. Psychiatr. Res. 39, 451–460 (2005). 85. Mitra, R., Jadhav, S., McEwen, B. S., Vyas, A. & Chattarji, S. Stress duration modulates the spatiotemporal patterns of spine formation in the basolateral amygdala. Proc. Natl Acad. Sci. USA 102, 9371–9376 (2005). 86. Mitra, R. & Sapolsky, R. M. Acute corticosterone treatment is sufficient to induce anxiety and amygdaloid dendritic hypertrophy. Proc. Natl Acad. Sci. USA 105, 5573–5578 (2008). This interesting study addressed endocrine effects on the brain, with a focus on the amygdala and anxiety (rather than on hippocampus and memory). Of note, a single dose of glucocorticoids was sufficient to induce changes in amygdala structure 10 days later, which might be useful to model in animals PTSD. 87. Lupien, S. J. & McEwen, B. S. The acute effects of corticosteroids on cognition: integration of animal and human model studies. Brain Res. Brain Res. Rev. 24, 1–27 (1997). 88. Roozendaal, B. Glucocorticoids and the regulation of memory consolidation. Psychoneuroendocrinology 25, 213–238 (2000). 89. Lupien, S. J. et al. Stress hormones and human memory function across the lifespan. Psychoneuroendocrinology 30, 225–242 (2005). 90. Lupien, S. J. et al. Hippocampal volume is as variable in young as in older adults: implications for the notion of hippocampal atrophy in humans. Neuroimage 34, 479–485 (2007). This study showed that ~25% of young adults present hippocampal volumes as small as those of older adults. The presence of small hippocampal volumes in healthy young individuals supports the vulnerability hypothesis. 91. Pruessner, J. C., Lord, C., Meaney, M. & Lupien, S. Effects of self-esteem on age-related changes in cognition and the regulation of the hypothalamic-pituitary-adrenal axis. Ann. NY Acad. Sci. 1032, 186–194 (2004). 92. Pruessner, J. C. et al. Self-esteem, locus of control, hippocampal volume, and cortisol regulation in young and old adulthood. Neuroimage 28, 815–826 (2005). 93. Burke, H. M., Davis, M. C., Otte, C. & Mohr, D. C. Depression and cortisol responses to psychological stress: a meta-analysis. Psychoneuroendocrinology 30, 846–856 (2005). 94. Yehuda, R., Golier, J. A. & Kaufman, S. Circadian rhythm of salivary cortisol in Holocaust survivors with and without PTSD. Am. J. Psychiatry 162, 998–1000 (2005). 95. Meewisse, M. L., Reitsma, J. B., de Vries, G. J., Gersons, B. P. & Olff, M. Cortisol and post-traumatic stress disorder in adults: systematic review and metaanalysis. Br. J. Psychiatry 191, 387–392 (2007). This paper presented the first meta-analysis of cortisol findings in PTSD, to elucidate the determinants of hypocortisolism and resolve the inconsistency in findings. 96. Heim, C. et al. Pituitary-adrenal and autonomic responses to stress in women after sexual and physical abuse in childhood. JAMA 284, 592–597 (2000). 97. Heim, C., Mletzko, T., Purselle, D., Musselman, D. L. & Nemeroff, C. B. The dexamethasone/corticotropinreleasing factor test in men with major depression: role of childhood trauma. Biol. Psychiatry 63, 398–405 (2008). 98. Carpenter, L. L. et al. Cerebrospinal fluid corticotropinreleasing factor and perceived early-life stress in depressed patients and healthy control subjects. Neuropsychopharmacology 29, 777–784 (2004). 99. Heim, C., Newport, D. J., Mletzko, T., Miller, A. H. & Nemeroff, C. B. The link between childhood trauma and depression: insights from HPA axis studies in humans. Psychoneuroendocrinology 33, 693–710 (2008). A crucially important review which documents that the disturbances in the HPA axis that are observed in many adults with depression may be specific to those who experienced trauma or maltreatment in childhood. 100. Videbech, P. & Ravnkilde, B. Hippocampal volume and depression: a meta-analysis of MRI studies. Am. J. Psychiatry 161, 1957–1966 (2004). 444 | junE 2009 | VoluME 10 101. Smith, M. E. Bilateral hippocampal volume reduction in adults with post-traumatic stress disorder: a metaanalysis of structural MRI studies. Hippocampus 15, 798–807 (2005). 102. Vythilingam, M. et al. Childhood trauma associated with smaller hippocampal volume in women with major depression. Am. J. Psychiatry 159, 2072–2080 (2002). 103. Gilbertson, M. W. et al. Smaller hippocampal volume predicts pathologic vulnerability to psychological trauma. Nature Neurosci. 5, 1242–1247 (2002). The first paper to study whether the reduced hippocampal volume observed in PTSD patients is due to the disorder, to trauma exposure or to a pre-existing factor. 104. Issa, A. M., Rowe, W., Gauthier, S. & Meaney, M. J. Hypothalamic-pituitary-adrenal activity in aged, cognitively impaired and cognitively unimpaired rats. J. Neurosci. 10, 3247–3254 (1990). 105. Landfield, P. W., Waymire, J. C. & Lynch, G. Hippocampal aging and adrenocorticoids: quantitative correlations. Science 202, 1098–1102 (1978). 106. Landfield, P. W., Baskin, R. K. & Pitler, T. A. Brain aging correlates: retardation by hormonalpharmacological treatments. Science 214, 581–584 (1981). The first study to show that chronic exposure to high levels of glucocorticoids in rodents is associated with memory impairments and reduced hippocampal volume. 107. Landfield, P. W., Blalock, E. M., Chen, K. C. & Porter, N. M. A new glucocorticoid hypothesis of brain aging: implications for Alzheimer’s disease. Curr. Alzheimer Res. 4, 205–212 (2007). 108. Kulstad, J. J. et al. Effects of chronic glucocorticoid administration on insulin-degrading enzyme and amyloid-β peptide in the aged macaque. J. Neuropathol. Exp. Neurol. 64, 139–146 (2005). 109. Sapolsky, R. M., Krey, L. C. & McEwen, B. S. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocr. Rev. 7, 284–301 (1986). The first paper to present the glucocorticoid cascade hypothesis, now referred to as the neurotoxicity hypothesis. 110. Lowy, M. T., Wittenberg, L. & Yamamoto, B. K. Effect of acute stress on hippocampal glutamate levels and spectrin proteolysis in young and aged rats. J. Neurochem. 65, 268–274 (1995). 111. Raskind, M. A., Peskind, E. R. & Wilkinson, C. W. Hypothalamic-pituitary-adrenal axis regulation and human aging. Ann. NY Acad. Sci. 746, 327–335 (1994). 112. Lupien, S. J. et al. Cortisol levels during human aging predict hippocampal atrophy and memory deficits. Nature Neurosci. 1, 69–73 (1998). 113. Giubilei, F. et al. Altered circadian cortisol secretion in Alzheimer’s disease: clinical and neuroradiological aspects. J. Neurosci. Res. 66, 262–265 (2001). 114. Aisen, P. S. et al. A randomized controlled trial of prednisone in Alzheimer’s disease. Alzheimer’s Disease Cooperative Study. Neurology 54, 588–593 (2000). 115. Dai, J., Buijs, R. & Swaab, D. Glucocorticoid hormone (cortisol) affects axonal transport in human cortex neurons but shows resistance in Alzheimer’s disease. Br. J. Pharmacol. 143, 606–610 (2004). 116. Chen, Y., Dube, C. M., Rice, C. J. & Baram, T. Z. Rapid loss of dendritic spines after stress involves derangement of spine dynamics by corticotropinreleasing hormone. J. Neurosci. 28, 2903–2911 (2008). 117. Charney, D. S. & Manji, H. K. Life stress, genes, and depression: multiple pathways lead to increased risk and new opportunities for intervention. Sci. STKE 2004, re5 (2004). 118. Maercker, A., Michael, T., Fehm, L., Becker, E. S. & Margraf, J. Age of traumatisation as a predictor of post-traumatic stress disorder or major depression in young women. Br. J. Psychiatry 184, 482–487 (2004). 119. Teicher, M. H., Tomoda, A. & Andersen, S. L. Neurobiological consequences of early stress and childhood maltreatment: are results from human and animal studies comparable? Ann. NY Acad. Sci. 1071, 313–323 (2006). 120. Hall, F. S. Social deprivation of neonatal, adolescent, and adult rats has distinct neurochemical and behavioral consequences. Crit. Rev. Neurobiol. 12, 129–162 (1998). © 2009 Macmillan Publishers Limited. All rights reserved f o c u S o nR ESVt IREEW SS 121. Andersen, S. L. Trajectories of brain development: point of vulnerability or window of opportunity? Neurosci. Biobehav. Rev. 27, 3–18 (2003). A superb review paper which suggested that trauma at different time points during early development might be associated with different outcomes, depending on the brain structure that was affected at the time of exposure to adversity. 122. Widom, C. S., DuMont, K. & Czaja, S. J. A prospective investigation of major depressive disorder and comorbidity in abused and neglected children grown up. Arch. Gen. Psychiatry 64, 49–56 (2007). 123. Clayton, N. S. & Krebs, J. R. Hippocampal growth and attrition in birds affected by experience. Proc. Natl Acad. Sci. USA 91, 7410–7414 (1994). 124. Kudielka, B. M., Buske-Kirschbaum, A., Hellhammer, D. H. & Kirschbaum, C. HPA axis responses to laboratory psychosocial stress in healthy elderly adults, younger adults, and children: impact of age and gender. Psychoneuroendocrinology 29, 83–98 (2004). 125. Kessler, R. C. Epidemiology of women and depression. J. Affect. Disord. 74, 5–13 (2003). 126. Harlow, B. L., Cohen, L. S., Otto, M. W., Spiegelman, D. & Cramer, D. W. Early life menstrual characteristics and pregnancy experiences among women with and without major depression: the Harvard study of moods and cycles. J. Affect. Disord. 79, 167–176 (2004). 127. Zabin, L. S., Emerson, M. R. & Rowland, D. L. Childhood sexual abuse and early menarche: the direction of their relationship and its implications. J. Adolesc. Health 36, 393–400 (2005). 128. Jones, K. C. & de Voogt, P. Persistent organic pollutants (POPs): state of the science. Environ. Pollut. 100, 209–221 (1999). 129. Centers for Disease Control and Prevention. Second National Report on Human Exposure to Environmental Chemicals. (CDC, Atlanta, Georgia, 2003). 130. Daston, G. P., Cook, J. C. & Kavlock, R. J. Uncertainties for endocrine disrupters: our view on progress. Toxicol. Sci. 74, 245–252 (2003). 131. Gump, B. B. et al. Low-level prenatal and postnatal blood lead exposure and adrenocortical responses to acute stress in children. Environ. Health Perspect. 116, 249–255 (2008). 132. Denham, M. et al. Relationship of lead, mercury, mirex, dichlorodiphenyldichloroethylene, hexachlorobenzene, and polychlorinated biphenyls to timing of menarche among Akwesasne Mohawk girls. Pediatrics 115, e127–e134 (2005). 133. Turek, F. W. From circadian rhythms to clock genes in depression. Int. Clin. Psychopharmacol. 22 (Suppl. 2), S1–S8 (2007). 134. Lamarche, L. J. & De Koninck, J. Sleep disturbance in adults with posttraumatic stress disorder: a review. J. Clin. Psychiatry 68, 1257–1270 (2007). 135. Antoch, M. P. et al. Functional identification of the mouse circadian Clock gene by transgenic BAC rescue. Cell 89, 655–667 (1997). 136. Yakovlev, P. L. & Lecours, A. R. in Regional Development of the Brain in Early Life (ed. Minkowski, A.) 3–70 (Blackwell, Oxford, 1967). 137. Pruessner, J. C. et al. Volumetry of hippocampus and amygdala with high-resolution MRI and threedimensional analysis software: minimizing the discrepancies between laboratories. Cereb. Cortex 10, 433–442 (2000). nATuRE REVIEwS | NeuroscieNce 138. Tisserand, D. J. et al. Regional frontal cortical volumes decrease differentially in aging: an MRI study to compare volumetric approaches and voxelbased morphometry. Neuroimage 17, 657–669 (2002). 139. Insel, T. R., Battaglia, G., Fairbanks, D. W. & De Souza, E. B. The ontogeny of brain receptors for corticotropin-releasing factor and the development of their functional association with adenylate cyclase. J. Neurosci. 8, 4151–4158 (1988). 140. Levine, S. The ontogeny of the hypothalamic-pituitary-adrenal axis. The influence of maternal factors. Ann. NY Acad. Sci. 746, 275–288; discussion 289–293 (1994). 141. Gunnar, M. R. & Cheatham, C. L. Brain and behavior interfaces: stress and the developing brain. Infant Ment. Health J. 24, 195–211 (2003). A superb paper that summarized the effects of stress during development and how this knowledge can be used to develop effective interventions. 142. LeDoux, J. E. The Emotional Brain: The Mysterious Underpinnings of Emotional Life (Simon & Schuster, New York, 1996). Acknowledgements Sonia Lupien holds a Research Chair on Gender and Mental Health by the Canadian Institutes of Health Research. FURTHER INFORMATION Sonia J. Lupien’s homepage: All liNks Are AcTive iN The oNliNe pdf VoluME 10 | junE 2009 | 445 © 2009 Macmillan Publishers Limited. All rights reserved
Behavioral Neuroscience 2007, Vol. 121, No. 2, 257–263 Copyright 2007 by the American Psychological Association 0735-7044/07/$12.00 DOI: 10.1037/0735-7044.121.2.257 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Effects of Anticipatory Stress on Decision Making in a Gambling Task S. D. Preston T. W. Buchanan University of Michigan University of Iowa College of Medicine R. B. Stansfield A. Bechara University of Michigan University of Iowa College of Medicine and University of Southern California Recent research has highlighted the fact that emotion that is intrinsic to a task benefits decision making. The authors tested the converse hypothesis, that unrelated emotion disrupts decision making. Participants played the Iowa Gambling Task, during which only experimental participants anticipated giving a public speech (A. Bechara, D. Tranel, & H. Damasio, 2000). Experimental participants who were anticipating the speech learned the contingencies of the choices more slowly, and there was a gender interaction later in the game, with stressed female participants having more explicit knowledge and more advantageous performance and stressed male participants having poorer explicit knowledge and less advantageous performance. Effects of anticipatory stress on decision making are complex and depend on both the nature of the task and the individual. Keywords: decision making, stress, emotion, gambling, prefrontal cortex (al’Absi, Hugdahl, & Lovallo, 2002; Domes, Heinrichs, Reichwald, & Hautzinger, 2002; Het, Ramlow, & Wolf, 2005; Kudielkaa, Buske-Kirschbaumb, Hellhammer, & Kirschbaum, 2004; Parfitt, Hardy, & Pates, 1995; Wolf, Convit, et al., 2001; Wolf, Schommer, Hellhammer, McEwen, & Kirschbaum, 2001). Most of the work on stress and decision making has been in human factors (e.g., Garvey & Klein, 1993; Klein, 1996), but laboratory research has found that stress impairs decision making when it causes individuals to feel “frazzled” (Arnsten, 1998), such as when participants are stressed by the threat of a shock (Keinan, 1987) or made anxious by a secondary task (Cumming & Harris, 2001). One gambling study found that unrelated stress from exams or negative affect from pictures caused participants to favor less rewarding short-term choices (Gray, 1999). To study the effects of stress on decision making, we told participants that they would have to give a public speech at the end of the experiment, which has been shown to be a reliable method for inducing stress in the laboratory (e.g., Kudielkaa et al., 2004; Levenson, Sher, Grossman, Newman, & Newlin, 1980; Steele & Josephs, 1988). While anticipating the speech, participants performed the Iowa Gambling Task (IGT), which has been shown repeatedly to rely on an intact somatic marker system, which is affective or emotional in nature. When triggered during the pondering of decisions, these somatic signals help provide internal information about the costs and benefits of alternatives and thus help bias decision making in an advantageous direction (e.g., Bechara, Damasio, & Damasio, 2000; Bechara, Damasio, Damasio, & Lee, 1999; Bechara, Tranel, & Damasio, 2000). Because the stressor was unrelated to the decision task at hand, we hypothesized that the speech anticipation stress would impair performance by creating interference with the task-related emotion necessary to guide advantageous choices. Although a rationale for this hypothesis based on theoretical grounds has been previously Considerable work has outlined the beneficial effects of intrinsic emotions on decision making, referred to as somatic states (Bechara, Damasio, & Damasio, 2000) or affect heuristics (Finucane, Alhakami, Slovic, & Johnson, 2000). However, in other instances the emotion may be unrelated to the decision-making task; that is, it either existed before the decision-making task or was triggered during the decision-making task through unrelated means. Stress is often incidentally present during decision making, and so this study was designed to test whether incidental, anticipatory stress would be beneficial or disruptive to decision making. Most of the work on stress and cognition has looked at memory, finding that intrinsic emotion affects memory in an inverted-Ushaped fashion (e.g., Cahill & McGaugh, 1996; Joseph, 1999; Van Londen et al., 1998). In contrast, incidental effects of stress on memory are complex and depend on the task, phase of memory, age and gender of the participants, and even the time of day S. D. Preston, Department of Psychology, University of Michigan; T. W. Buchanan, Department of Neurology, University of Iowa College of Medicine; R. B. Stansfield, Department of Medical Education, University of Michigan; A. Bechara, Department of Neurology, University of Iowa College of Medicine, and Brain and Creativity Institute and Department of Psychology, University of Southern California. This research was supported by National Institutes of Health Grant PO1 NS19632 (from the National Institute of Neurological Disorders and Stroke) and National Science Foundation Grant IIS 04-42586 (from other agencies of the U.S. Government). We thank Emily Recknor for help in collection of the data and Steven Wengrovitz for help in preparation of the manuscript. Correspondence concerning this article should be addressed to S. D. Preston, University of Michigan, Department of Psychology, 3040 East Hall, 530 Church Street, Ann Arbor, MI 48109. E-mail: 257 PRESTON, BUCHANAN, STANSFIELD, AND BECHARA 258 provided (Bechara & Damasio, 2005), no empirical evidence has been obtained that would support or refute such a hypothesis. This was the primary aim of this study. Method This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Participants Participants were healthy adults (mean age ⫽ 32.38 years, SD ⫽ 10.69), recruited from and tested at the University of Iowa Hospitals and Clinics. None had neurological or psychiatric illness or were on medication. There were 20 men and 20 women; half of each gender were randomly assigned to the control or experimental group. The experiment was approved by the University of Iowa’s Institutional Review Board, and all participants gave informed consent in compliance with federal and institutional guidelines. Procedures Briefly, all participants completed baseline questionnaires as well as the original version of the IGT, which served in this study as a practice game. Experimental participants, but not control participants, were told about the public speech. Finally, all participants performed a repeat test version of the IGT, which served as a test game. The State–Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1977) was used to assess anxiety, and the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988) was used to asses changes in positive and negative affect. The STAI–Trait was administered when the participants first arrived; the STAI–State and PANAS were administered when participants first arrived and after the 50th pick in the practice and test games. After the baseline questionnaires, psychophysiological electrodes were attached inferior to the costal margin and anterior to the sternocleidomastoid muscle to record heart rate using Lead II electrocardiogram in an Astro-Med polygraph (Warwick, RI) at 1000 Hz and simultaneously converted from analog to digital format and transferred to a computer using MP100WS (Biopac Systems, Santa Barbara, CA). The recorded data were analyzed in beats per minute with AcqKnowledge III (Biopac Systems). Heart rate data were collected throughout the practice game, the test game, and the time between the two games, hereafter referred to as the reveal period, when the participants were being told about the speech. The effect of the speech manipulation on heart rate response for each participant is referred to hereafter as c, the proportionate change in the reveal period relative to baseline: c ⫽ ([mean heart rate for reveal period – mean heart rate for practice game] / mean heart rate for practice game). The method for administering the IGT was the same as described in prior studies (Bechara, Tranel, & Damasio, 2000) except where noted. Participants were seated at a computer showing four card decks and were instructed to pick one card at a time from any deck and to win as much money as possible. In the IGT, each pick results in both a win and a loss (all monetary changes are hypothetical). Two of the decks yield relatively larger monetary gains (rewards) but also occasional larger monetary losses (punishments), resulting in a net loss if chosen too often. The other two decks had relatively smaller immediate gains (rewards), but the losses (punishments) were also relatively smaller, resulting in a net gain. All participants first performed the original version of the gambling task (the “practice game,” also known as Version ABCD, with each letter corresponding to one of the four decks, each with different contingencies). After the practice game, experimental participants were told about the public speaking task awaiting them after the next game (the “test game,” also known as the repeat test Version KLMN, with the four new letters referring to the four new contingencies), while control participants sat quietly. After the practice game and before the test game, only experimental participants were told in detail about the speech to be delivered after the test game. The topic of the speech was “What I dislike about my body and physical appearance,” after Levenson et al. (1980). The experimental participants were explicitly told that they would have to speak on the topic for 5 min and that they would be judged on “clarity, organization, articulation, openness, and defensiveness.” With the participant watching, a video camera and a microphone were set up and directed toward the participant, a two-way mirror was revealed behind a wooden roll-up screen, and a timer was placed next to the participant’s computer screen. The experimenter told the participant that there could be people watching the speech from behind the two-way mirror. The timer was set to 20 min, and the participant was told that the timer would count down to zero during the second gambling task, at which time they would have to give the speech (as a reminder, they were told that 20 min was more than sufficient to finish the task). After the test game and all self-report questions, experimental participants were told that the speech was voluntary and that they would not have to give the speech if they opted not to at that point in the study (cf. Steele & Josephs, 1988). No participants chose to actually give the speech at the end. Procedures were identical for control participants, except there was no description of a speech before the test game. Participants sat quietly, and the experimenter talked to them briefly to occupy the same amount of time. The camera, microphone, timer, and mirror were hidden from view. The repeat test version of the IGT (Version KLMN), which was used as the test game in this study, was designed to follow the original IGT (Version ABCD), which was used as the practice game in this study. As such, the contingencies of the four card decks were intentionally made more difficult in order to counteract the effects of practice and learning when individuals play the game in the ABCD version (for more information, see Hernandez & Bechara, 2006; Hernandez, Lu, Hyonggin, & Bechara, 2006). The increased difficulty of the repeat test version, or the test game in this case, introduces variance between the two games, and including both games uses more degrees of freedom, which would decrease the power of the experiment overall. Therefore, in order to increase the statistical power, rather than using a full-factorial, pretest–posttest design we used a between-subjects design on the test game data only. We analyzed gambling performance by subtracting the sum of picks from the two disadvantageous decks from the sum of picks from the two advantageous decks (i.e., [(K ⫹ M) – (L ⫹ N)]). A separate net score was calculated for each block (20 picks in each of five blocks, totaling 100 picks). Previous research has revealed that IGT performance improves significantly between the first, second, and third blocks, t(212) ⬍ –2.71, p ⬍ .01, when scores reach the asymptote, and that there are This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. STRESS AND DECISION MAKING no differences between the fourth and fifth blocks, t(212) ⬎ –1.87, ns (Stansfield, Preston, & Bechara, 2003). Thus, to increase the sensitivity of our dependent measures, after an omnibus analysis of variance (ANOVA) comparing across all blocks, we divided the game into the learning phase (Blocks 1, 2, 3—i.e., Trials 1– 60) and the performance phase (Blocks 4 and 5—i.e., Trials 61–100) to discriminate anticipatory stress effects on learning the contingencies versus on risk biases after the contingencies have been learned. To determine the level of explicit knowledge participants had about the four decks, the experimenter interrupted participants after they picked from a disadvantageous deck (and experienced a loss) between Picks 80 and 90 of the test so that participants could mark on a horizontal line how good (to the right) or bad (to the left) they thought each deck was. This is referred to as explicit knowledge, measured as the distance in centimeters the mark was to the right for the sum of the advantageous decks minus the sum of the disadvantageous decks ([K ⫹ M] – [L ⫹ N]). Debates about this technique for assessing explicit knowledge (Maia & McClelland, 2004) are less problematic this late in the game. 259 Results There were no differences at baseline between the control and experimental participants on state or trait anxiety using the STAI, or positive or negative affect using the PANAS, F(1, 38) ⬍ 1.99, ns; baseline affect data were also not related to any other measures and so are not discussed further. Six participants (1 male control, 3 female controls, 1 male experimental, and 1 female experimental) had missing heart rate data; these participants were excluded from all analyses involving c. Means and standard deviations of heart rate across the whole experiment are provided in Table 1. Experimental participants had greater increases in heart rate from the speech anticipation stress (c) than control participants, t(32) ⫽ 3.28, p ⫽ .003 (Figure 1); the interaction with gender was not significant, F(1, 30) ⫽ 1.82, ns. Means and standard deviations of self-report measures across the whole experiment are provided in Table 2. Experimental participants were more anxious during the test game, STAI–State: t(38) ⫽ 2.06, p ⫽ .047, and showed a trend toward exhibiting less positive emotion, PANAS–Positive Affect: t(38) ⫽ 1.93, p ⫽ .061, than control participants. Both groups had similar levels of negative emotion, PANAS–Negative Affect: t(38) ⫽ –1.13, ns. Means and standard deviations of gambling performance for each group across the five blocks of the test game are included in Table 3. There was a significant main effect of block, F(4, 35) ⫽ 2.82, p ⫽ .04; a post hoc least-squares-differences test found net gambling scores in Block 1 to be significantly lower than in Blocks 2, 3, and 4. There was not a significant difference between the control and experimental participants overall, F(1, 38) ⫽ 0.65, ns, or a Block ⫻ Condition interaction, F(4, 35) ⫽ 1.11, ns. Although there were no omnibus interactions in the test game, there were differences within the learning phase. The speech anticipation stress retarded the learning of experimental participants in the initial blocks (1, 2, and 3). Control participants had the usual steep increase in their performance from Block 1 to Block 2 that leveled off by Block 3, whereas experimental participants failed to improve performance by Block 2, but their delayed learning emerged as they went from Block 2 to Block 3: interaction term, F(1, 38) ⫽ 4.12, p ⫽ .049 (Figure 2). In the performance phase there were no significant differences between control and experimental participants in net gambling scores, F(1, 38) ⫽ 0.07, ns, or explicit knowledge about the decks, F(1, 38) ⫽ 0.46, ns, suggesting that the stressor did not unilaterally affect the performance phase in experimental participants. The speech anticipation stress (c) was also not related to these mea- Analyses Analyses were performed using SPSS Version 11.0.4 for Macintosh. Manipulation checks were conducted on c, STAI, and PANAS using Student’s t test to confirm that there were no differences among participants at baseline and to confirm that experimental participants were more stressed. A repeated measures ANOVA was used to compare differences in performance across the five blocks of the test game between control and experimental participants. Because the stressor could affect participants differently in the learning and performance phases of the game, additional analyses were conducted on the learning and performance phases separately in case the manipulation affected those phases differently. Control and experimental participants were compared in the learning phase (Blocks 1, 2, and 3) using an F test to look for differences between control and experimental participants in the rate of improvement between the early blocks (3–2 and 2–1); they were again compared in the performance phase (Blocks 4 and 5) using an F test to look for differences in average performance in the last two blocks and for explicit knowledge about the decks after the 80th pick. The alpha level for all comparisons was set at .05. Table 1 Mean Heart Rate (and Standard Deviation) by Condition Across Blocks and During Reveal Block Game Condition 1 2 3 4 5 Reveal c Practice Control Experimental Control Experimental 74.52 (11.49) 74.75 (12.27) 72.18 (18.39) 74.52 (13.59) 73.45 (12.13) 73.12 (12.64) 71.35 (14.00) 75.47 (13.53) 73.44 (11.73) 74.80 (12.89) 71.74 (13.23) 76.24 (13.28) 73.11 (11.91) 74.49 (13.03) 70.14 (14.11) 76.28 (13.71) 74.07 (12.31) 74.53 (12.94) 72.20 (14.29) 76.76 (13.54) 75.09 (11.71) 81.03 (13.99) .030 (.045) .091 (.061) Test Note. Values of c were calculated from the difference between the reveal period (when subjects heard about the speech) and the baseline average of the practice game. PRESTON, BUCHANAN, STANSFIELD, AND BECHARA 260 experimental female participants and negative r values for experimental male participants (performance phase: female, r2 ⫽ .160, ns; male, r2 ⫽ .165, ns; explicit knowledge: female, r2 ⫽ .204, ns; male, r2 ⫽ .259, ns) (Figure 3). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Discussion Figure 1. Experimental participants were physiologically aroused by the manipulation. Graph shows percentage change in heart rate for control and experimental participants from the baseline to the reveal period (c, calculated as [mean heart rate for reveal period – mean heart rate for practice game] / mean heart rate for practice game) when the experimental participants learned about the speech. Error bars show standard errors. * p ⬍ .01. sures in participants overall, or within experimental participants (r2 ⬍ .031, ns). These null results may be due to differences in the way that male and female experimental participants responded to the manipulation. Experimental female participants tended to have higher responses to the speech anticipation stress than male participants, t(16) ⫽ –1.79, p ⫽ .09, but their mean performance phase scores were actually elevated (M ⫽ 4.30, SD ⫽ 5.31) as compared with control female participants (M ⫽ 2.60, SD ⫽ 9.92); whereas the mean performance phase scores were lower in experimental male participants (M ⫽ 2.00, SD ⫽ 10.38) as compared with control male participants (M ⫽ 5.10, SD ⫽ 8.54). This interaction was not significant, F(1, 36) ⫽ 0.749, ns, most likely owing to low power in an experiment not designed to test for gender differences, but there was a similar, nonsignificant pattern in the predictions of performance phase scores and explicit knowledge by speech anticipation stress (c), with positive r values for The anticipation of giving a public speech was effective as a stressor; it increased anxiety and heart rate only for participants in the anticipatory stress condition and only after the stressor was introduced. Our main finding was that participants in the experimental condition were slower to learn the task, meaning that it took them longer to shift toward advantageous decision making. Because the main effect was observed in the learning phase and associated with increased arousal and anxiety and decreased positive affect (but not increased negative affect), it is likely that the effect of anticipatory stress on decision making, in the learning phase of this experiment, was mediated by competition between the primary card game and the unrelated speech stressor for limited working memory resources. That is, the experimental participants were initially distracted by thoughts of the pending speech and, thus, either were slower to learn the contingencies of the four card decks or were simply not paying attention to the contingencies until after the second block. Indeed, evidence shows that increased working memory load during the IGT prevents participants from developing the somatic markers associated with the contingencies of the four decks, thus impairing decision making (Hinson, Jameson, & Whitney, 2002), and that this effect is due specifically to a disruption in the executive component of working memory and not to competition for the verbal buffer (Jameson, Hinson, & Whitney, 2004). Both human and nonhuman studies have found that stress compromises executive functions, and especially working memory (al’Absi et al., 2002; Arnsten, 1998; Arnsten & Goldman-Rakic, 1998; Hartley & Adams, 1974; Hockey, 1970), which in turn impairs cognitive performance, such as mental arithmetic (al’Absi et al., 2002; Veltman & Gaillard, 1993). In addition, because neurochemicals released in response to stress, such as glucocorticoids (Roozendaal, McReynolds, & McGaugh, 2004; Sapolsky, 1992) and dopamine (Adler et al., 2000; Koepp et al., 1998; Pappata et al., 2002), all have receptors in the prefrontal cortex (PFC), it is reasonable to hypothesize that decision-making processes that rely on orbitofrontal portions of the PFC can also be directly affected by stress. Such a system is beneficial for shifting Table 2 Mean Scores (and Standard Deviations) for Self-Report Scales by Condition and Time Point Measure STAI–Trait STAI–State PANAS–PA PANAS–NA Condition Baseline Practice Test Control Experimental Control Experimental Control Experimental Control Experimental 34.9 (6.67) 38.21 (9.12) 33.85 (9.84) 36.15 (7.68) 30.80 (6.86) 27.15 (7.30) 12.25 (3.19) 13.25 (3.78) 36.40 (9.64) 38.35 (8.94) 32.05 (8.94) 27.20 (8.45) 12.80 (2.93) 14.45 (5.22) 38.75 (9.33) 44.90 (9.57) 30.70 (10.08) 24.80 (9.27) 14.20 (4.76) 16.05 (5.58) Note. STAI ⫽ State–Trait Anxiety Inventory; PANAS ⫽ Positive and Negative Affect Schedule; PA ⫽ Positive Affect; NA ⫽ Negative Affect. STRESS AND DECISION MAKING 261 Table 3 Mean Scores (and Standard Deviations) for Gambling Performance by Condition and Gender Across Blocks Block Condition Control This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Experimental Gender 1 2 3 4 5 Male Female Male Female ⫺3.11 (4.26) 1.00 (7.32) 2.80 (9.85) ⫺1.60 (2.27) 1.78 (4.41) 5.20 (8.80) 2.40 (7.82) 0.20 (3.71) 3.78 (7.38) 4.20 (7.39) 5.20 (9.76) 3.40 (6.87) 6.44 (8.82) 3.20 (10.08) 3.40 (12.22) 3.40 (4.53) 5.33 (9.49) 2.00 (11.23) 0.60 (9.52) 5.20 (7.73) processing under stress away from slower, more deliberative processes that depend on the PFC (Gabrieli, Poldrack, & Desmond, 1998) and toward more automatic, reflexive processes that depend on subcortical areas like the amygdala (Kensinger & Corkin, 2004). Data indicated that male and female participants might have been differentially affected by the stressor, with female participants performing better under anticipatory stress and male participants performing worse. Given that males normally perform slightly better than females on the task (Bechara, Damasio, Tranel, & Damasio, 1997; Reavis & Overman, 2001) and are more affected by competition and achievement (Ennis, Kelly, & Lambert, 2001; Holt-Lunstad, Clayton, & Uchino, 2001), the effect seems parsimoniously explained by an inverted-U-shaped function of arousal and performance (Yerkes & Dodson, 1908). It is unlikely that any improvement in female performance can be explained simply by a reduced effect of the speech anticipation stress, because the trend was in the opposite direction; that is, experimental female participants were more aroused by the stressor than experimental male participants. One could hypothesize that female participants would be more aroused by a speech having to do with one’s body and physical appearance; however, the evidence does not support this hypothesis. The original Levenson et al. (1980) Figure 2. Changes in net gambling score in the learning phase of the test game (during the first three blocks) after the manipulation was introduced in experimental participants. To represent the rate of learning, the previous block is subtracted from the subsequent block (plus or minus one standard error). ** p ⬍ .05. study, using this same speech anticipation stressor, was conducted only with male participants and found significant increases in stress from the speech, with heart rate responses equivalent to those produced by an electrical shock stressor. Moreover, Steele and Josephs (1988) used this same speech anticipation stressor Figure 3. Relationship between speech anticipation stress (c, calculated as [mean heart rate for reveal period – mean heart rate for practice game] / mean heart rate for practice game) on the x-axis and performance phase scores on the y-axis (top graph) and between speech anticipation stress on the x-axis and explicit knowledge about the goodness of the decks (bottom graph). Male participants are represented by filled circles, female participants by open squares. The thick solid line is the regression fit for all experimental participants with complete heart rate data (n ⫽ 18); the thin, solid line is the regression fit for males only (n ⫽ 9); and the thin, dotted line is the regression fit for females only (n ⫽ 9). M HR ⫽ mean heart rate. PRESTON, BUCHANAN, STANSFIELD, AND BECHARA This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 262 with male and female participants, and they did not find any effect of gender. A gender difference in stress and decision making is consistent with a rapidly growing body of literature in brain and behavior research. Using the IGT, Stout and colleagues (Stout, Rock, Campbell, Busemeyer, & Finn, 2005) found that drug-abusing males performed worse on the task than control males, whereas drugabusing females performed better than control females. Males generally have greater physiological and psychological responses to public speaking tasks than females (Matthews, Gump, & Owens, 2001; Sauro, Jorgensen, & Pedlow, 2003; Steiner, Ryst, Berkowitz, Gschwendt, & Koopman, 2002; Wolf, Schommer, et al., 2001). These gender differences are also mirrored by differential brain responses, with males relying more on the right hemisphere and females relying more on the left hemisphere. This pattern of hemispheric asymmetry has been found in the amygdala in a study of memory for arousing stimuli (Tranel, Damasio, Denburg, & Bechara, 2005) as well as in the PFC in a behavioral study of socioemotional and decision-making performance in ventromedial PFC patients (Bechara, Damasio, & Damasio, 2000) and in an imaging study of the IGT in intact participants (Bolla, Eldreth, Matochik, & Cadet, 2004). From our research, it seems reasonable to conclude that emotion that is related to the task is advantageous in guiding decisions, providing access to implicit and explicit knowledge about contingencies and likely outcomes. By contrast, emotion that is unrelated to the task may disrupt prefrontal functioning and impair an individual’s ability during learning to determine the costs and benefits of their choices. These results are preliminary and based on a small sample; thus, they need to be replicated in a larger sample, one that is especially designed to test for gender differences in the effects of stress on performance. Differential effects of gender in investigations of emotion and performance (memory or decision making) are increasingly common, pointing at psychological differences in the way stressors are appraised and at biological differences in the way stimuli are processed and the way stress hormones affect the brain. Future research can systematically parse the effects of emotion on the learning and performance phases of decision making and compare effects on males and females so that definitive statements can be made about the social and biological effects of emotion on behavioral decisions. References Adler, C. M., Elman, I., Weisenfeld, N., Kestler, L., Pickar, D., & Breier, A. (2000). Effects of acute metabolic stress on striatal dopamine release in healthy volunteers. Neuropsychopharmacology, 22, 545–550. al’Absi, M., Hugdahl, K., & Lovallo, W. R. (2002). Adrenocortical stress responses and altered working memory performance. Psychophysiology, 39, 95–99. Arnsten, A. F. (1998, June 12). The biology of being frazzled. Science, 280, 1711–1712. Arnsten, A. F., & Goldman-Rakic, P. S. (1998). Noise stress impairs prefrontal cortical cognitive function in monkeys: Evidence for a hyperdopaminergic mechanism. Archives of General Psychiatry, 55, 362–368. Bechara, A., & Damasio, A. R. (2005). The somatic marker hypothesis: A neural theory of economic decision. Games and Economic Behavior, 552, 336 –372. Bechara, A., Damasio, H., & Damasio, A. R. (2000). Emotion, decision making and the orbitofrontal cortex. Cerebral Cortex, 10, 295–307. Bechara, A., Damasio, H., Damasio, A. R., & Lee, G. P. (1999). Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. Journal of Neuroscience, 19, 5473–5481. Bechara, A., Damasio, H., Tranel, D., & Damasio, A. R. (1997, February 28). Deciding advantageously before knowing the advantageous strategy. Science, 275, 1293–1294. Bechara, A., Tranel, D., & Damasio, H. (2000). Characterization of the decision-making deficit of patients with ventromedial prefrontal cortex lesions. Brain, 123, 2189 –2202. Bolla, K. I., Eldreth, D. A., Matochik, J. A., & Cadet, J. L. (2004). Sex-related differences in a gambling task and its neural correlates. Cerebral Cortex, 14, 1226 –1232. Cahill, L., & McGaugh, J. L. (1996). Modulation of memory storage. Current Opinion in Neurobiology, 6, 237–242. Cumming, S. R., & Harris, L. M. (2001). The impact of anxiety on the accuracy of diagnostic decision-making. Stress & Health, 17, 281–286. Domes, G., Heinrichs, M., Reichwald, U., & Hautzinger, M. (2002). Hypothalamic–pituitary–adrenal axis reactivity to psychological stress and memory in middle-aged women: High responders exhibit enhanced declarative memory performance. Psychoneuroendocrinology, 27, 843– 853. Ennis, M., Kelly, K. S., & Lambert, P. L. (2001). Sex differences in cortisol excretion during anticipation of a psychological stressor: Possible support for the tend-and-befriend hypothesis. Stress & Health, 17, 253–261. Finucane, M. L., Alhakami, A., Slovic, P., & Johnson, S. M. (2000). The affect heuristic in judgments of risks and benefits. Journal of Behavioral Decision Making, 13, 1–17. Gabrieli, J. D., Poldrack, R. A., & Desmond, J. E. (1998). The role of left prefrontal cortex in language and memory. Proceedings of the National Academy of Sciences, USA, 95, 906 –913. Garvey, B. J., & Klein, K. (1993). Relationship of life stress and body consciousness to hypervigilant decision making. Journal of Personality and Social Psychology, 64, 267–273. Gray, J. R. (1999). A bias toward short-term thinking in threat-related negative emotional states. Personality and Social Psychology Bulletin, 25, 65–75. Hartley, L. R., & Adams, R. G. (1974). Effect of noise on the Stroop test. Journal of Experimental Psychology, 102, 62– 66. Hernandez, M., & Bechara, A. (2006, April). Ventromedial prefrontal damage consistently impairs performance on parallel versions of the Iowa Gambling Task. Paper presented at the Cognitive Neuroscience Society 13th Annual Meeting, San Francisco, CA. Hernandez, M., Lu, X., Hyonggin, A., & Bechara, A. (2006). Development of parallel versions of the Iowa Gambling Task for repeat testing. Journal of the International Neuropsychological Society, 12(Suppl. 1), 44. Het, S., Ramlow, G., & Wolf, O. T. (2005). A meta-analytic review of the effects of acute cortisol administration on human memory. Psychoneuroendocrinology, 30, 771–784. Hinson, J. M., Jameson, T. L., & Whitney, P. (2002). Somatic markers, working memory, and decision making. Cognitive, Affective & Behavioral Neuroscience, 2, 341–353. Hockey, G. R. J. (1970). Effect of loud noise on attentional selectivity. Quarterly Journal of Experimental Psychology, 22, 28 –36. Holt-Lunstad, J., Clayton, C. J., & Uchino, B. N. (2001). Gender differences in cardiovascular reactivity to competitive stress: The impact of gender of competitor and competition outcome. International Journal of Behavioral Medicine, 8, 91–102. Jameson, T. L., Hinson, J. M., & Whitney, P. (2004). Components of working memory and somatic markers in decision making. Psychonomic Bulletin & Review, 11, 515–520. Joseph, R. (1999). The neurology of traumatic “dissociative” amnesia: Commentary and literature review. Child Abuse & Neglect, 23, 715–727. Keinan, G. (1987). Decision making under stress: Scanning of alternatives This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. STRESS AND DECISION MAKING under controllable and uncontrollable threats. Journal of Personality and Social Psychology, 52, 639 – 644. Kensinger, E. A., & Corkin, S. (2004). Two routes to emotional memory: Distinct neural processes for valence and arousal. Proceedings of the National Academy of Sciences, USA, 101, 3310 –3315. Klein, G. (1996). The effect of acute stressors on decision making. In J. E. Driskell & E. Salas (Eds.), Stress and human performance (pp. 49 – 88). Mahwah, NJ: Erlbaum. Koepp, M. J., Gunn, R. N., Lawrence, A. D., Cunningham, V. J., Dagher, A., Jones, T., et al. (1998, May 21). Evidence for striatal dopamine release during a video game. Nature, 393, 266 –268. Kudielkaa, B. M., Buske-Kirschbaumb, A., Hellhammer, D. H., & Kirschbaum, C. (2004). HPA axis responses to laboratory psychosocial stress in healthy elderly adults, younger adults, and children: Impact of age and gender. Psychoneuroendocrinology, 29, 83–98. Levenson, R. W., Sher, K. J., Grossman, L. M., Newman, J., & Newlin, D. B. (1980). Alcohol and stress response dampening: Pharmacological effects, expectance, and tension reduction. Journal of Abnormal Psychology, 89, 528 –538. Maia, T. V., & McClelland, J. L. (2004). A reexamination of the evidence for the somatic marker hypothesis: What participants really know in the Iowa Gambling Task. Proceedings of the National Academy of Sciences, USA, 101, 16075–16080. Matthews, K. A., Gump, B. B., & Owens, J. F. (2001). Chronic stress influences cardiovascular and neuroendocrine responses during acute stress and recovery, especially in men. Health Psychology, 20, 403– 410. Pappata, S., Dehaene, S., Poline, J. B., Gregoire, M. C., Jobert, A., Delforge, J., et al. (2002). In vivo detection of striatal dopamine release during reward: A PET study with [(11)C]raclopride and a single dynamic scan approach. NeuroImage, 16, 1015–1027. Parfitt, G., Hardy, L., & Pates, J. (1995). Somatic anxiety and physiological arousal: Their effects upon a high anaerobic, low memory demand task. International Journal of Sport Psychology, 26, 196 –213. Reavis, R., & Overman, W. H. (2001). Adult sex differences on a decisionmaking task previously shown to depend on the orbital prefrontal cortex. Behavioral Neuroscience, 115, 196 –206. Roozendaal, B., McReynolds, J. R., & McGaugh, J. L. (2004). The basolateral amygdala interacts with the medial prefrontal cortex in regulating glucocorticoid effects on working memory impairment. Journal of Neuroscience, 24, 1385–1392. Sapolsky, R. M. (1992). Stress, the aging brain, and the mechanisms of neuron death. Cambridge, MA: MIT Press. 263 Sauro, M. D., Jorgensen, R. S., & Pedlow, C. T. (2003). Stress, glucocorticoids, and memory: A meta-analytic review. Stress, 6, 235–245. Spielberger, C. D., Gorsuch, R. L., Lushene, R. E., Vagg, P. R., & Jacobs, G. A. (1977). State–Trait Anxiety Inventory for Adults. Redwood City, CA: Mind Garden. Stansfield, R. B., Preston, S., & Bechara, A. (2003). Normative gambling task data: Analysis summary. Unpublished manuscript, University of Iowa. Steele, C. M., & Josephs, R. A. (1988). Drinking your troubles away: II. An attention-allocation model of alcohol’s effect on psychological stress. Journal of Abnormal Psychology, 97, 196 –205. Steiner, H., Ryst, E., Berkowitz, J., Gschwendt, M. A., & Koopman, C. (2002). Boys’ and girls’ responses to stress: Affect and heart rate during a speech task. Journal of Adolescent Health, 30(4, Suppl.), 14 –21. Stout, J. C., Rock, S. L., Campbell, M. C., Busemeyer, J. R., & Finn, P. R. (2005). Psychological processes underlying risky decisions in drug abusers. Psychology of Addictive Behaviors, 19, 148 –157. Tranel, D., Damasio, H., Denburg, N. L., & Bechara, A. (2005). Does gender play a role in functional asymmetry of ventromedial prefrontal cortex? Brain, 128, 2872–2881. Van Londen, L., Goekoop, J. G., Zwinderman, A. H., Lanser, J. B. K., Wiegant, V. M., & De Wied, D. (1998). Neuropsychological performance and plasma cortisol, arginine vasopressin and oxytocin in patients with major depression. Psychological Medicine, 28, 275–284. Veltman, J. A., & Gaillard, A. W. (1993). Indices of mental workload in a complex task environment. Neuropsychobiology, 28(1–2), 72–75. Watson, D., Clark, L., & Tellegen, A. (1988). Development and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54, 1063–1070. Wolf, O. T., Convit, A., McHugh, P. F., Kandil, E., Thorn, E. L., De Santi, S., et al. (2001). Cortisol differentially affects memory in young and elderly men. Behavioral Neuroscience, 115, 1002–1011. Wolf, O. T., Schommer, N. C., Hellhammer, D. H., McEwen, B. S., & Kirschbaum, C. (2001). The relationship between stress induced cortisol levels and memory differs between men and women. Psychoneuroendocrinology, 26, 711–720. Yerkes, R. M., & Dodson, J. D. (1908). The relation of strength of stimulus to rapidity of habit formation. Journal of Comparative Neurology and Psychology, 18, 31–39. Received June 19, 2006 Revision received September 22, 2006 Accepted November 16, 2006 䡲
Emotion 2011, Vol. 11, No. 5, 1248 –1254 © 2011 American Psychological Association 1528-3542/11/$12.00 DOI: 10.1037/a0023524 BRIEF REPORT Attentional Selection Is Biased Toward Mood-Congruent Stimuli Mark W. Becker and Mallorie Leinenger This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Michigan State University One can exert significant volitional control over the attentional filter so that stimuli that are consistent with one’s explicit goals are more likely to receive attention and become part of one’s conscious experience. Here we pair a mood induction procedure with an inattentional blindness task to show that one’s current mood has a similar influence on attention. A positive, negative, or neutral mood manipulation was followed by an attentionally demanding multiple-object tracking task. During the tracking task, participants were more likely to notice an unexpected face when its emotional expression was congruent with participants’ mood. This was particularly true for the frowning face, which was detected almost exclusively by participants in the sad mood induction condition. This attentional bias toward mood-congruent stimuli provides evidence that one’s temporary mood can influence the attentional filter, thereby affecting the information that one extracts from, and how one experiences the world. Keywords: inattentional blindness, mood, attention, faces, emotion filter that allows objects that have perceptual features consistent with one’s current goal to pass through the filter, while blocking from further processing objects that have features inconsistent with the goal (Folk, Remington, & Johnston, 1992; Most, Scholl, Clifford, & Simons, 2005). For example, Most et al. (2005) found that people who were asked to attend to a number of moving black objects while ignoring moving white objects often detected when an unexpected black object appeared in the display, but failed to notice when the unexpected object was white. This finding demonstrates that people’s immediate goals create an “attentional-set” that tunes their attentional filters so that objects that are consistent with their goals are more likely to be passed through the attentional filter (Folk et al., 1992; Most et al., 2005). Here we ask a slightly different question. We investigate whether one’s current emotional state or mood creates an “emotional set” that influences the attentional filter. That is, does one’s mood help tune the attentional filter, thereby influencing the types of objects that are likely to capture attention and become part of one’s conscious experience? To investigate this question, a mood induction task was followed by an IB task in which the unexpected object was an emotional face. By systematically varying the congruency between participants’ mood and the valence of the face, we hoped to determine how mood influences attentional capture. We hypothesized that the people would be more likely to notice the unanticipated additional object when its valence was congruent with the observer’s mood. This conjecture was based on ample evidence suggesting biases for negative stimuli in participants that were selected for anxiety (Mogg & Bradley, 2005) or depression (Eizenman et al., 2003). The finding of a bias toward negative stimuli in these disorders that are associated with increased negative affect (Watson, Clark, & Carey, 1988) is broadly consistent with a mood-congruent attentional bias (Bar-Haim, Lamy, Pergamin, Bakermans-Kranenburg, & van Ijzendoorn, 2007; Koster, De Raedt, Goeleven, Franck, & Crombez, 2005; Koster, De Raedt, Attention is capacity-limited (Becker & Pashler, 2005; James, 1890; Pashler, 1998) and necessary for the explicit recognition of objects in our environment (O’Regan, Deubel, Clark, & Rensink, 2000; Simons & Rensink, 2005). If something appears in one’s environment, but is not attended, it often goes unnoticed (Rensink, O’Regan, & Clark, 1997; Simons & Chabris, 1999). Inattentional blindness (IB), the failure to detect an unanticipated object when one’s attentional capacity is consumed by an ongoing task, highlights the important role that attention plays in conscious recognition (Mack, 2003; Mack & Rock, 1998; Simons & Chabris, 1999). For example, experienced pilots using flight simulators failed to notice when a jumbo jet appeared on the runway on which they were landing. This failure resulted even though the plane was clearly visible through the windshield, and presumably occurred because the pilots’ attentional capacity was consumed by the other tasks required to successfully land a plane (Haines, 1991). This example and many others demonstrate that early attentional filtering can have a profound effect on the information that one extracts from and, thus, how one experiences the world. As a result, there has been a great deal of recent research investigating the processes that guide the allocation of attention. One clear finding from this work is that a person can exert significant top-down control over the attentional filter, forming a This article was published Online First May 23, 2011. Mark W. Becker and Mallorie Leinenger, Department of Psychology, Michigan State University. We thank Brooke Ingersoll and Tim Pleskac for assistance with the preparation and editing of the manuscript, Sam Gergans for her contributions to the initial conceptualization of the experiment, and the many undergraduate researchers who assisted with data collection. This work was funded by a Michigan State University IRGP grant. Correspondence concerning this article should be addressed to Mark W. Becker, Department of Psychology, Michigan State University, East Lansing, MI 48824. E-mail: 1248 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. MOOD AND INATTENTIONAL BLINDNESS Leyman, & De Lissnyder, 2010; MacLeod, Mathews, & Tata, 1986). By contrast, most experiments that have investigated attentional biases for emotional stimuli in nonselect populations or populations that are or selected to be low in anxiety or depression, have either failed to find an emotional bias (Bar-Haim et al., 2007) or have found a bias away from negative stimuli (Frewen, Dozois, Joanisse, & Neufeld, 2008). Given that most people report having more positive than negative affect (Watson, Clark, & Tellegen, 1988), the finding of a bias away from negative and toward positive in these experiments is consistent with a mood congruent bias. Although this line of reasoning led us to predict a detection benefit when the unexpected object was congruent with a person’s mood, we were not at all certain of this outcome. Our uncertainty was driven by two factors. First, there are substantial and substantive differences between the tasks previously used to support a mood congruent attentional bias and the inattentional blindness task we used. Most previous research (see Yiend, 2010, for review) used either modified cuing paradigms (see Frewen et al., 2008 for review; MacLeod et al., 1986), or visual search (Matsumoto, 2010; Rinck, Becker, Kellermann, & Roth, 2003; Rinck, Reinecke, Ellwart, Heuer, & Becker, 2005) paradigms to demonstrate that attention is biased toward an emotionally charged stimulus. In both of these methods the location and/or identity of the critical target object is unknown and the participant must find the target among multiple possible objects or locations. As such, one could describe these tasks as divided attention tasks (see Pashler, 1998 Chapter 3, for a review) that evaluate the relative weighting of an emotional object in the competition for attention when the attentional system is attempting to shift attention to a new object. By contrast, in the inattentional blindness procedure one is not attempting to find a new object to which to attend, but instead already has a prespecified attentional task and is attempting to maintain attentional focus on that task. As such, this task is represents a selective attention task (see Pashler, 1998 Chapter 2, for a review) in which the detection of the unexpected object represents the interruption of an ongoing attentional task, and thus indicates how well a stimulus captures attention away from an ongoing task (Most et al., 2005) or causes an interrupt signal that interrupts ongoing attentional control (Corbetta & Shulman, 2002). In addition to the above theoretical concerns, there are also a number of published reports that suggest that we might not find a mood congruent advantage. For instance, there are claims that people should exhibit a mood-incongruent attentional bias to maintain homeostasis (Derryberry & Tucker, 1994; Gawronski, Deutsch, & Strack, 2005; Rothermund, Wentura, & Bak, 2001). Others have suggested that positive mood results in a wider attentional focus (Rowe, Hirsh, Anderson, & Smith, 2007), which would predict that people placed in a positive mood should be more likely to detect an unexpected stimulus regardless of its valence. Finally, there are suggestions that a negative mood leads to more drifts in attention (Smallwood, Fitzgerald, Miles, & Phillips, 2009), which would predict that people placed in a negative mood should be more likely to detect an unexpected stimulus regardless of its valence. In short, while there is consensus for mood-congruent attentional biases among people who are depressed or anxious, there is less of a consensus that the same bias exists in nonselect populations. In addition, none of the previous research has used an inattentional blindness task to evaluate whether a mood congruent stimulus can capture attention away from on an ongoing focused attentional task. 1249 Method Participants Two hundred thirty-eight university undergraduates with normal or corrected to normal vision participated for course credit. Procedure All participants were run individually in dimmed, sound attenuated booths that had a PC and 19 in. CRT running at 100 Hz. All surveys, the mood manipulation and experimental displays were programmed in Macromedia Director and the data was automatically saved into texts files for off-line analysis. Participants completed the State Trait Anxiety Inventory questionnaire before participating in the experiment. During the first phase of the experiment, participants were randomly assigned to receive a positive, negative, or neutral mood induction procedure. In the neutral condition, participants wrote about the route that they took to arrive at the lab. In the positive and negative conditions (see Appendix) participants were asked to write descriptive words about an emotional life event (Richter & Gendolla, 2009; Westermann, Spies, Stahl, & Hesse, 1996). Participants could journal for up to 4 min, or could self-terminate the induction task at any point after the first minute. On average participants wrote for over 2.5 min (M ⫽ 152 s, SE ⫽ 4.08 s) and the amount of time journaling did not vary by mood induction condition [F(2, 233) ⬍ 1]. Immediately after the mood induction task, participants performed a variation of Most et al.’s (Most et al., 2001) IB paradigm. Participants were shown six stationary white disks that appeared on a black display window surrounded by a gray boarder. Each ball had a diameter of 2.4 degrees of visual angle and the display window was a 15 ⫻ 11° rectangle in the center of the computer display. Three balls were empty while the other three contained the scrambled features of a schematic face (see Figure 1). After 2 s, each disk began to move in an independent, pseudorandom path. Periodically, the disks occluded one another, changed directions, and changed speeds (between two speeds: ⬃2 and ⬃3 deg/s). When a disk reached the edge of the black display window, it bounced off the edge. Participants were asked to track the three empty balls and count how many times they bounced off the side of the display. The entire tracking phase lasted for 16 s, after which subjects reported the number of bounces that the empty disk made. Consistent with previous experiments (Mack & Rock, 1998; Most et al., 2001), trials one and two had no unexpected event. In trials three through five, an unanticipated seventh ball appeared 7 s into the tracking task. This disk contained the same features as the distractor disks, but the features were unscrambled so that the disk appeared to contain a schematic, emotional face. For a given participant, the face was either smiling or frowning for all three trials in which it appeared. The unexpected disk drifted into the display window from the upper right corner and drifted across the screen for 6 s, exiting on the lower left. If the unexpected disk crossed paths with any other disk, it occluded the other disk. Trial three, the first trial in which the face appeared, was the critical IB trial. After participants reported the number of bounces they detected in trial three, they completed a brief mood manipulation check that consisted of four questions that they rated on a 7-point Likert-type scale (see Appendix). Two of these questions BECKER AND LEINENGER 1250 4.03, p ⬍ .001, than the sad group (M ⫽ 4.92, SE ⫽ .16). The neutral group (M ⫽ 5.36, SE ⫽ .15) rated their moods marginally lower than the happy group, t(150) ⫽ 1.95, p ⫽ .053, and significantly higher than the sad group, t(158) ⫽ 2.02, p ⫽ .045. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. STAI Survey To verify that our assignment to conditions did not produce groups that differed in anxiety we ran two 3 ⫻ 2 ANOVAs, one with state and one with trait anxiety as the dependent variable. Each ANOVA had three levels of mood induction and two levels of stimulus valence. Both main effects and the interaction were nonsignificant for both measure of anxiety (all F ⬍ 1). Distractors Unexpected Target Frowning Stimuli Smiling Stimuli Figure 1. IB method. The top panel is a schematic of the stimuli used in the IB task. Participants tracked the three empty disks for 16 s as they moved in a pseudorandom fashion occasionally bouncing off the side of the display window. Participants’ task was to count how many times the empty disks bounced off the sides. In Trials 3–5, an unanticipated seventh ball appeared 7 s into the tracking task. It could display a happy or sad face and drifted into the screen on the upper right and traversed across the screen, exiting on the lower left. It was visible for a total of 6 s. After Trials 3–5, participants were asked whether they noticed anything odd during the tracking task. The bottom panel shows the odd item and the to-be-ignored disks for each facial emotion condition. assessed the participants’ mood and two assessed arousal level. One of the two questions assessing a given variable was reverse coded. Following the manipulation check, participants were asked if they had seen anything odd during the tracking task. If they selected “no,” they advanced to trial four. If they selected “yes,” a text box appeared and they typed a description of the odd event before proceeding to Trial 4. These descriptions were used to verify that participants had detected the unexpected ball. Because participants were asked about the presence of an odd item in trial three, the fourth trial was considered a divided attention task in which participants might be monitoring the display for odd items (Most et al., 2001). In the fifth trial, participants were instructed to no longer count the ball bounces and simply watch the display. This trial was used to verify that the face stimuli were highly visible if attention was not otherwise engaged. Only two participants failed to detect the face in this trial and their data were eliminated from the analyses. The IB Trial In Trial 3, we found fairly high rates of IB; across all conditions the unanticipated disk was detected by only 23.3% of the participants (see Table 1). These high IB rates are surprising given prior reports that face stimuli are relatively immune from IB (Devue, Laloyaux, Feyers, Theeuwes, & Brédart, 2009; Mack & Rock, 1998). More importantly, the data suggest that people were more likely to notice a face that was congruent with their mood than one that was incongruent (see Figure 2). To evaluate this congruency effect, we used binary logistical regression to model the detection responses. A model with only the mood induction term and the facial emotion term was no better than the base model without them, ␹2(1) ⫽ .89, p ⬎ .3. However, including an interaction term in the regression model that coded whether the faces and mood induction were incongruent (sad/smiling and happy/frowning), were neutral (neutral mood/smiling and neutral mood/frowning), or were congruent (sad/frowning and happy/smiling) significantly improved the model fit, ␹2(1) ⫽ 5.314, p ⫽ .021, and the interaction term was significant, p ⫽ .024. Subsequent chi-square tests indicate the interaction resulted because participants were significantly more likely to detect a frowning face when they received the sad mood induction than the neutral, ␹2(1) ⫽ 4.02, p ⫽ .045, or happy, ␹2(1) ⫽ 4.77, p ⫽ .029, mood induction. While there was a trend for people to detect more smiling faces in the happy mood condition than the neutral or sad mood condition, the effect did not approach significance, both p ⬎ .4. Table 1 Number of Participants Who Detected and Missed the Unexpected Event Results Manipulation Check The manipulation check confirmed that the mood induction procedure was effective; it influenced mood, F(2, 233) ⫽ 8.066, p ⬍ .001, but not arousal, F(2, 233) ⫽ 1.28, p ⬎ .25. Planned t tests (two-tailed) found that the happy mood induction group (M ⫽ 5.74, SE ⫽ .12) rated their mood significantly higher, t(158) ⫽ Happy Face Sad Face Trial 3 Trial 4 Mood induction Mood induction Happy Neutral Sad Happy Neutral Sad 12 (27) 5 (32) 9 (28) 6 (33) 10 (34) 14 (26) 35 (4) 26 (11) 28 (9) 32 (7) 30 (14) 33 (7) Note. Numbers of detected faces are presented, with the number of missed faces in parentheses. Each row corresponds to the type of unexpected face (happy or sad). Columns are grouped by mood induction condition, with Trial 3 on the left and Trial 4 on the right. MOOD AND INATTENTIONAL BLINDNESS Smile Frown Percentage of Parcipants who Detected the Face on Trial 3 40% 35% 30% 25% 20% 15% 10% 5% This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 0% Happy Neutral Sad Mood Manipulaon Figure 2. Detection of the unexpected stimulus. The percentage of participants who detected the unexpected object (ordinate) is presented as a function of the mood manipulation (abscissa) and stimulus valence (separate bars). The cross-over interaction indicates that people were far more likely to notice the unanticipated face when its valence matched their induced mood. The Divided Attention Trial Detection rates increased dramatically in the fourth, divided attention trial; across all conditions the face was detected by 78% of the participants (see Table 1). Even so, the congruency effect was still present when assessed by the regression method (interaction term, p ⫽ .012) used to analyze the data from Trial 3. Follow up chi square tests reveal that the source of this interaction was that the smiling face was detected by more people in the happy mood condition than the sad mood condition, ␹2(1) ⫽ 5.66, p ⫽ .017, while there was a nonsignificant trend for more people to detect the frowning face when they were in the sad mood condition than the happy mood condition, ␹2(1) ⫽ 1.61, p ⫽ .21. Thus, even though people may have been dividing attention to monitor the display for an unexpected event, the face was easier to detect when its emotional valence matched their induced mood. Ball Counting Errors Total counting errors were generally low (across all trials, the average counts were within 6.3% of the actual number of ball bounces) and did not vary as a function of mood induction condition or face stimuli; an ANOVA with three levels of mood induction and two levels of face stimuli found no main effects nor an interaction, all Fs ⬍ 1. We also examined whether people who detected the face in the critical IB trial had different error rates than those who experienced IB. Participants who detected the face in Trial 3, made no more errors in trials one and two, t(234) ⫽ .80, p ⫽ .43, but those who detected the face made significantly, t(234) ⫽ 2.95, p ⫽ .003, more errors (M ⫽ 9.6%, SE ⫽ 1.8%) in Trial 3 than those who experience IB (M ⫽ 5.2%, SE ⫽ .6%). This pattern of data suggests that those who noticed the face were as engaged in the primary task (Trials 1 and 2); however, when the face broke though their attentional filter it diverted attention away from the primary task leading to more ball counting errors in the IB trial. 1251 Detection Performance and Individual Differences In post hoc analyses, we investigated whether the few individuals within a mood induction condition who detected the unexpected stimulus were different in terms of their anxiety level or responsiveness to the mood induction than those individuals who experienced IB. To perform these analyses within each mood induction condition we ran separate 2 (smiling/frowning face) ⫻ 2 (detected/experienced IB) ANOVAs with state anxiety, trait anxiety, and self-reported mood as the dependent variable. These analyses allowed us to determine whether those in a mood induction condition who noticed the unexpected object differed from those in the same condition who experience IB. We found little evidence that those who detected the unexpected object differed from those that experienced IB in terms of anxiety. For state anxiety, both the ANOVA for the happy and sad mood induction conditions yielded nonsignificant main effects for stimulus valence and detection group and no valence by group interaction (all p ⬎ .15). The pattern was the same when trait anxiety was used as the dependent variable, with no comparisons approaching significance (all p ⬎ .12) except for a trend toward a valence by detection group interaction that appeared only in the sad mood manipulation condition, F(1, 73) ⫽ 3.39, p ⫽ .07. This trend resulted because people who detected the smiling face tended to have lower anxiety scores (M ⫽ 35.90, SE ⫽ 3.35) than those that missed it (M ⫽ 39.93, SE ⫽ 1.94), but those that detected the frowning face tended to have higher anxiety scores (M ⫽ 43, SE ⫽ 2.83) than those that missed it (M ⫽ 37.3, SE ⫽ 2.21). This pattern is broadly inconsistent with the suggestion that our mood-congruent bias was driven by a subset of participants with aberrant anxiety. A similar pair of ANOVAs was run using the self-reported mood ratings (given during the manipulation check) as the dependent variable. For the group that experienced the sad mood induction procedure, neither of the main effects nor the interaction approached significance, all F(1, 80) ⬍ 1. By contrast, for the happy mood induction participants there was a main effect of face valence, F(1, 72) ⫽ 8.41, p ⫽ .005, and a marginal main effect of detection group, F(1. 72) ⫽ 3.69, p ⫽ .06, both qualified by a significant interaction, F(1, 72) ⫽ 6.87, p ⫽ .01. The source of this interaction (see Figure 3), was that the mood ratings for participants that detected a sad face (M ⫽ 4.4, SE ⫽ .45) were much lower than participants that detected the happy face (M ⫽ 6.04, SE ⫽ .29), missed the sad face (M ⫽ 5.75, SE ⫽ .18), and missed the happy face (M ⫽ 5.83, SE ⫽ .19). This pattern of results is inconsistent with the possibility that our findings of a moodcongruent bias were driven by a subset of participants that were particularly influenced by the mood induction procedures. Instead, the results demonstrate that the few people who noticed the frowning face despite being in the happy mood induction condition were those who self-reported low affect, a finding that is broadly consistent with the overall mood-congruent finding. Discussion The data from the IB trials suggest that stimuli which are congruent with one’s current mood are more likely to “break through” the attentional filter during an attentionally demanding task. As such, the findings demonstrate that one’s mood influences the attentional filter, creating an “emotional set” that biases attention such that mood BECKER AND LEINENGER 1252 7 Self Reported Mood 6 5 4 3 Frown 2 Smile This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1 Happy Mood Happy Mood Missed Detected Sad Mood Missed Sad Mood Detected Figure 3. Self-reported mood as a function of condition and detection performance. The left side of the figure plots data for the group that received the happy mood manipulation and the right plots the group that received the sad mood manipulation. Within each mood manipulation group, means are further broken down by detection performance (whether participants detected the unexpected object or experienced IB) and the valence of the unexpected stimulus. congruent stimuli are more likely to capture attention, much as an “attentional set” biases attention toward stimuli that have goalcongruent features (Folk, Remington, & Johnston, 1992; Most et al., 2005). This finding is noteworthy for a number of reasons. Our findings demonstrate a mood-congruent attentional bias in response to a temporary mood shift among a nonselect group of participants. Most previous research documenting mood-congruent attentional biases have demonstrated the effect in anxious (Bar-Haim et al., 2007) or depressed samples (Frewen et al., 2008) that have relatively long lasting negative affect. In addition, the few studies that have reported mood congruent attentional biases because of short term mood manipulations have focused on the finding that positive mood manipulations increase attention to positive material (Tamir & Robinson, 2007; Wadlinger & Isaacowitz, 2006). By contrast, our mood-congruent result from the IB trial was primarily driven by the participants in the negative mood condition; the frowning face was invisible to almost everyone except for those in the sad mood manipulation condition. It is worth noting that we found only a slight trend for a mood-congruent bias for those in the happy condition in the IB trial; however, there was a significant happy congruent bias in the divided attention trial. As a result, we think our data is broadly consistent with, or at least not inconsistent with, previous reports of mood congruent attentional biases for induced positive moods (Tamir & Robinson, 2007; Wadlinger & Isaacowitz, 2006). Thus, our finding provides additional evidence of a mood congruent attentional bias among nonselect samples, and extends this claim to people placed in a temporary negative mood. In addition, the current research is the first to use an inattentional blindness task to investigate mood-congruent biases. This task differs in important ways from the modified attentional cuing paradigms and visual search paradigms that have been used to investigate moodcongruent attentional biases (Yiend, 2010). In those paradigms, the participant is asked to select the appropriate location from among a set of alternatives. As such, the tasks intentionally engage attentional selection mechanisms and then examine how different stimuli are weighted within the competition inherent to the selection process. In short, they examine the mechanics of this selection competition in a task where one must choose a new location for attention. By contrast, in the inattentional blindness task, the participants are not asked to select new object for attention. Instead, they are asked to maintain their attention on a prespecified set of objects. In this paradigm, the detection of the unexpected object reflects an engagement of the attentional selection process despite volitional control to suppress it; it represents an interruption of this volitional control (Corbetta & Shulman, 2002). In addition, this interruption appeared to divert resources away from the primary task, thereby producing more ball counting errors in those participants who noticed the unexpected object. To our knowledge we are the first to demonstrate that the mood-congruent bias can disrupt ongoing attentional control in this way. Although we consider the use of the inattentional blindness task as a strength of our design, the low detection rates raise the possibility that our results are driven by a few participants that might be somewhat aberrant. To investigate this possibility, within a given mood induction condition, we compared the anxiety ratings and self-reported mood of those who detected the face with those that experienced IB. In general, we found no evidence that our participants who experience mood-congruent biases were aberrant in either their mood or anxiety. Indeed, the only interesting effect from these analyses was that the people in the happy mood induction condition who detected the mood-incongruent frowning face, were those who self-reported low affect. This effect could be interpreted as additional evidence for a mood-congruent attentional bias; only those for whom the happy mood induction procedure failed detected the frowning face. However, one should be cautious in making this interpretation. Given our desire to ensure that the effects of our mood manipulation lasted throughout the critical IB trial, we placed the mood manipulation check after the presentation of the unexpected face. Thus we cannot rule out an alternative interpretation of this effect. It is possible that those who detected the frowning face subsequently reported their mood as lower, either because of demand characteristics or because the detection of a frowning face decreased their mood. Summary We found that one’s mood can alter attentional filtering such that mood congruent stimuli are more likely to break through an attentional filter and become part of one’s conscious experience. This finding suggests that mood can influence very early and basic cognitive processes and cause an interruption in the ongoing volitional control of attention. This early gating of information may, at least in part, contribute to mood’s ability to influence more complex cognitive processes such as judgment and decision making (Cryder, Lerner, Gross, & Dahl, 2008; Loewenstein & Lerner, 2003). In short, we have long known that whether one reports the glass as half full or half empty may depend on the person’s mood. The current research suggests that this might not simply be a response bias, but that the person’s mood may actually alter which aspects of the environment reach awareness, such that people in a positive mood selectively perceive the full part of the glass while people in a negative mood selectively perceive the empty portion. References Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2007). Threat-related attentional bias in anxious This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. MOOD AND INATTENTIONAL BLINDNESS and nonanxious individuals: A meta-analytic study. Psychological Bulletin, 133, 1–24. Becker, M. W., & Pashler, H. (2005). Awareness of the continuously visible: Information acquisition during preview. Perception & Psychophysics, 67, 1391–1403. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. [10.1038/nrn755]. Nature Reviews Neuroscience, 3, 201–215. Cryder, C. E., Lerner, J. S., Gross, J. J., & Dahl, R. E. (2008). Misery is not miserly: Sad and self-focused individuals spend more. Psychological Science, 19, 525–530. Derryberry, D., & Tucker, D. M. (1994). Motivating the focus of attention. In P. M. Niedenthal, & S. Kitayama (Eds.), Heart’s eye: Emotional influences in perception and attention. (pp. 167–196). San Diego, CA: Academic Press. Devue, C., Laloyaux, C., Feyers, D., Theeuwes, J., & Brédart, S. (2009). Do pictures of faces, and which ones, capture attention in the inattentional-blindness paradigm? Perception, 38, 552–568. Eizenman, M., Yu, L. H., Grupp, L., Eizenman, E., Ellenbogen, M., Gemar, M., & Levitan, R. D. (2003). A naturalistic visual scanning approach to assess selective attention in major depressive disorder. Psychiatry Research, 118, 117–128. Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044. Frewen, P. A., Dozois, D. J. A., Joanisse, M. F., & Neufeld, R. W. J. (2008). Selective attention to threat versus reward: Meta-analysis and neural-network modeling of the dot-probe task. Clinical Psychology Review, 28, 307–337. Gawronski, B., Deutsch, R., & Strack, F. (2005). Approach/avoidance-related motor actions and the processing of affective stimuli: Incongruency effects in automatic attention allocation. Social Cognition, 23, 182–203. Haines, R. (1991). A breakdown in simultaneous information processing. In G. Obrecht & L. W. Stark (Eds.), Presbyopia research: From molecular biology to visual adaptation (pp. 171–175). New York: Plenum Press. James, W. (1890). The principles of psychology (p. 697, Vol I). New York: Henry Holt and Co. Koster, E. H. W., De Raedt, R., Goeleven, E., Franck, E., & Crombez, G. (2005). Mood-congruent attentional bias in dysphoria: Maintained attention to and impaired disengagement from negative information. Emotion, 5, 446 – 455. Koster, E. H. W., De Raedt, R., Leyman, L., & De Lissnyder, E. (2010). Mood-congruent attention and memory bias in dysphoria: Exploring the coherence among information-processing biases. Behaviour Research and Therapy, 48, 219 –225. Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson, H. Goldsmith, & K. Scherer (Eds.), Handbook of Affective Science (pp. 619 – 642). New York: Oxford University Press. Mack, A. (2003). Inattentional blindness: Looking without seeing. Current Directions in Psychological Science, 12, 180 –184. Mack, A., & Rock, I. (1998). Inattentional blindness. MIT Press/Bradford Books series in cognitive psychology (p. 273). Cambridge, MA: The MIT Press. MacLeod, C., Mathews, A., & Tata, P. (1986). Attentional bias in emotional disorders. Journal of Abnormal Psychology, 95, 15–20. Matsumoto, E. (2010). Bias in attending to emotional facial expressions: Anxiety and visual search efficiency. Applied Cognitive Psychology 1253 Special Issue: Current Directions at the Juncture of Clinical and Cognitive Science, 24, 414 – 424. Mogg, K., & Bradley, B. P. (2005). Attentional bias in generalized anxiety disorder versus depressive disorder. Cognitive Therapy and Research, 29, 29 – 45. Most, S. B., Scholl, B. J., Clifford, E. R., & Simons, D. J. (2005). What you see is what you set: Sustained inattentional blindness and the capture of awareness. Psychological Review, 112, 217–242. Most, S. B., Simons, D. J., Scholl, B. J., Jimenez, R., Clifford, E., & Chabris, C. F. (2001). How not to be seen: The contribution of similarity and selective ignoring to sustained inattentional blindness. Psychological Science, 12, 9 –17. O’Regan, J. K., Deubel, H., Clark, J. J., & Rensink, R. A. (2000). Picture changes during blinks: Looking without seeing and seeing without looking. Visual Cognition Special Issue: Change Blindness and Visual Memory, 7, 191–211. Pashler, H. E. (1998). The psychology of attention (p. 494). Cambridge, MA: The MIT Press. Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8, 368 –373. Richter, M., & Gendolla, G. (2009). Mood impact on cardiovascular reactivity when task difficulty is unclear. [10.1007/s11031-009 –91344]. Motivation and Emotion, 33, 239 –248. Rinck, M., Becker, E. S., Kellermann, J., & Roth, W. T. (2003). Selective attention in anxiety: Distraction and enhancement in visual search. Depression and Anxiety, 18, 18 –28. Rinck, M., Reinecke, A., Ellwart, T., Heuer, K., & Becker, E. S. (2005). Speeded detection and increased distraction in fear of spiders: Evidence from eye movements. Journal of Abnormal Psychology, 114, 235–248. Rothermund, K., Wentura, D., & Bak, P. M. (2001). Automatic attention to stimuli signalling chances and dangers: Moderating effects of positive and negative goal and action contexts. Cognition and Emotion Special Issue: Automatic Affective Processing, 15, 231–248. Rowe, G., Hirsh, J. B., Anderson, A. K., & Smith, E. E. (2007). Positive affect increases the breadth of attentional selection. PNAS Proceedings of the National Academy of Sciences, USA, 104, 383–388. Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28, 1059 –1074. Simons, D. J., & Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in Cognitive Sciences, 9, 16 –20. Smallwood, J., Fitzgerald, A., Miles, L. K., & Phillips, L. H. (2009). Shifting moods, wandering minds: Negative moods lead the mind to wander. Emotion, 9, 271–276. Tamir, M., & Robinson, M. D. (2007). The happy spotlight: Positive mood and selective attention to rewarding information. Personality and Social Psychology Bulletin, 33, 1124 –1136. Wadlinger, H., & Isaacowitz, D. (2006). Positive mood broadens visual attention to positive stimuli. Motivation and Emotion, 30, 87–99. Watson, D., Clark, L. A., & Carey, G. (1988). Positive and negative affectivity and their relation to anxiety and depressive disorders. Journal of Abnormal Psychology, 97, 346 –353. Westermann, R., Spies, K., Stahl, G., & Hesse, F. W. (1996). Relative effectiveness and validity of mood induction procedures: A metaanalysis. European Journal of Social Psychology, 26, 557–580. Yiend, J. (2010). The effects of emotion on attention: A review of attentional processing of emotional information. Cognition and Emotion, 24, 3– 47. (Appendix follows) BECKER AND LEINENGER 1254 Appendix Mood Manipulation and Manipulation Check Materials A. The mood induction task for the positive mood condition is below. In the sad mood condition the word “happy” was replaced by “sad.” mood. This index could range from 1 to 7, with a higher score representing more positive mood. The arousal measure was obtained in a similar way, with question 4 being reverse coded and summed with question 2, so higher scores indicate greater arousal. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Instructions Instructions Recall an episode in your life that made you feel very HAPPY and continues to make you happy whenever you think about it, even today. Please imagine this episode as vividly as you can. Recall the events happening to you. Recall your surroundings as clearly as possible. Picture the people or objects involved. Try to think the same thoughts. Try to feel the same feelings. Use the spaces below to list any descriptive words that come to mind as you recall this event.” Please read the descriptors at the ends of each scale and then check the box along the scale that best describes how you feel RIGHT NOW. B. The mood manipulation check consisted of the following questions. The response to question 1 was reverse coded and then averaged with the response from question 3 to obtain an index of Received March 22, 2010 Revision received January 18, 2011 Accepted January 28, 2011 䡲 Unpleasant Tired Happy Tense OOOOOOO OOOOOOO OOOOOOO OOOOOOO Pleasant Alert Sad Relaxed

Tutor Answer

School: UC Berkeley

Hello, I have completed the assignment. see the attached documents. its nice working with you😇



Stress Management
Institutional Affiliation


Effects of stress on cognitive functions

Stress is an aversive emotional, sensitive and motivational state happening in threatening
circumstances. Stress can also be stated as a situation where a person is not able to instigate a
clear pattern of behavior to remove or alter a situation that is threatening the achievement of a
certain goal. Stress affects cognitive functions in different ways. Stress brings about threatrelated distractors. This is...

flag Report DMCA

Good stuff. Would use again.

Similar Questions
Hot Questions
Related Tags
Study Guides

Brown University

1271 Tutors

California Institute of Technology

2131 Tutors

Carnegie Mellon University

982 Tutors

Columbia University

1256 Tutors

Dartmouth University

2113 Tutors

Emory University

2279 Tutors

Harvard University

599 Tutors

Massachusetts Institute of Technology

2319 Tutors

New York University

1645 Tutors

Notre Dam University

1911 Tutors

Oklahoma University

2122 Tutors

Pennsylvania State University

932 Tutors

Princeton University

1211 Tutors

Stanford University

983 Tutors

University of California

1282 Tutors

Oxford University

123 Tutors

Yale University

2325 Tutors