Attentional and Interpretive Bias

timer Asked: Feb 1st, 2019
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Question Description

Think back to the period of time following the attacks on New York’s World Trade Center on September 11, 2001. If you happened to travel by airplane during that time, did you experience a higher degree of anxiety than you might normally have? If you did not fly during that period, imagine how flying might have felt in terms of anxiety levels.

How might anxiety affect the way that you and your fellow passengers view one another within the context of a situation involving such attacks? Do you think that you would pay more attention to other travelers? Might certain behaviors seem more suspicious? These are examples of attentional and interpretive bias. These examples demonstrate how mood can affect memory and learning.

For this Discussion, consider additional examples of effects of mood on memory and learning. Consider how anxiety or depression can influence attentional and interpretive bias.

With these thoughts in mind:

Post two ways mood might affect memory and learning and explain how. Explain one way that anxiety or depression can influence attentional and interpretive bias. Provide examples to support your response. Justify your response using the Learning Resources and current literature.

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

Neuron, Vol. 48, 175–187, October 20, 2005, Copyright ª2005 by Elsevier Inc. DOI 10.1016/j.neuron.2005.09.025 Contributions of the Amygdala to Emotion Processing: From Animal Models to Human Behavior Elizabeth A. Phelps1,* and Joseph E. LeDoux2 1 Department of Psychology New York University 4-6 Washington Place New York, New York 10003 2 Center for Neural Science New York University 4-6 Washington Place New York, New York 10003 Research on the neural systems underlying emotion in animal models over the past two decades has implicated the amygdala in fear and other emotional processes. This work stimulated interest in pursuing the brain mechanisms of emotion in humans. Here, we review research on the role of the amygdala in emotional processes in both animal models and humans. The review is not exhaustive, but it highlights five major research topics that illustrate parallel roles for the amygdala in humans and other animals, including implicit emotional learning and memory, emotional modulation of memory, emotional influences on attention and perception, emotion and social behavior, and emotion inhibition and regulation. Introduction Over the past two decades, the amygdala has gone from a being an obscure region of the brain to practically a household word (the phonetic ring of the word ‘‘amygdala’’ was the subject of a piece in the New York Times recently). Although known to be involved in emotion for some time (Weiskrantz, 1956), much of the recent scientific interest in the amygdala stems from its role in fear conditioning, a form of emotional learning in which a neutral stimulus comes to elicit defensive behavior and physiological responses after being associated with an aversive event (for review see LeDoux 1996, 2000; Maren, 2001; Davis and Whalen, 2001). This research, mostly conducted in rats, has not only identified the amygdala as a central structure in the circuitry underlying fear conditioning but has also implicated specific synaptic connections and molecular cascades in the amygdala in the acquisition and storage of memories of fear conditioning (see Rodrigues et al., 2004). Recent studies in humans have begun to search for parallels to the animal work (e.g., LaBar et al., 1995; Morris et al., 1998a, 1998b; Phelps et al., 2000; Adolphs et al., 2005). The results have complemented but also extended the basic findings from animals regarding the amygdala’s role in emotional processing. In this review, we highlight what has been learned about the amygdala’s involvement in emotion, focusing on several areas of research where the amygdala has been shown to play similar roles in humans and other animals. Implicit Emotional Learning and Memory It is now widely accepted that many brain systems are capable of learning and storing information (Squire *Correspondence: Review and Kandel, 1999; Eichenbaum, 2002; Schacter, 1996). Some of these function explicitly and give rise to our conscious memories, while others function implicitly and store memories that are accessed and used automatically, or unconsciously. Emotion systems in the brain are generally viewed as belonging to the category of systems that form implicit memories (LeDoux, 1996). This does not imply that memories for emotional situations are only formed implicitly, as other systems, such as the explicit memory system of the medial temporal lobe, can form their own memories of emotional situations. It instead implies that the memories formed and stored by emotion systems are implicitly stored and accessed. This is in fact true of most systems that store information. These systems are perhaps not best thought of as memory systems. Instead, memory and its underlying neuronal plasticity are features that allow such systems to perform their function (emotional control, sensory processing, motor regulation, etc.) more effectively (LeDoux, 2002; Eichenbaum, 2002). Much of the renewed enthusiasm for studies of emotion in neuroscience has come from studies of emotional learning and memory, especially studies of conditioned fear in rats and other mammals (LeDoux, 2000; Walker and Davis, 2002; Davis and Whalen, 2001; Fanselow and LeDoux, 1999; Kapp et al., 1992; Maren, 2001). In this procedure, the subject is exposed to an emotionally neutral conditioned stimulus (CS), such as a tone, that is paired with an aversive unconditioned stimulus (US), such as an electric shock. An association is formed between the CS and US, and later presentation of the CS alone elicits behavioral defense responses and associated autonomic and endocrine adjustments. The subject also typically forms an association between the US and the environmental context, thus often necessitating the testing of conditioning to the CS in a novel context. Research on the neural system underlying fear responses conditioned by tone-shock pairings has implicated circuits into and through the amygdala as essential to the acquisition and storage of a memory of the conditioning experience and the expression of fear responses (Figure 1) (Kapp et al., 1992; Davis and Whalen, 2001; Fanselow and LeDoux, 1999; LeDoux, 2000; Maren, 2001; Medina et al., 2002; for an alternative view, see Cahill et al., 1999). The lateral nucleus (LA) is typically viewed as the sensory interface of the amygdala and as a key site of plasticity, while the central nucleus (CE) is viewed as the output region (but see Pare et al., 2004). LA receives inputs from both thalamic and cortical stations in the auditory system, and both are involved in CS transmission. LA projects to CE both directly and indirectly (Pitkänen et al., 1997). It is still unclear whether the direct connection from LA to CE is sufficient or whether a link through the basal nuclei and/or the intercalated cell masses might be involved, or even whether direct sensory connections to CE might play a role (see Pare et al., 2004). Other amygdala areas, though, do not seem to play an essential role in this simple form of conditioning (Amorapanth et al., 2000; Neuron 176 Figure 1. Neural Pathways underlying Fear Conditioning Fear conditioning is a procedure in which an emotionally neutral conditioned stimulus (CS) is presented in association with an aversive unconditioned stimulus (US). In studies of rats, the CS has typically been an auditory tone and the US an electric footshock. The pathways mediating auditory fear conditioning in rats involve the convergence of the CS and US pathways onto single cells in the lateral nucleus of the amygdala (LA) from thalamic and cortical processing regions in the sensory systems that process the CS (auditory system) and US (somatosensory system). The LA then connects with the CE both directly and by way of other amygdala regions (not shown). Outputs of the CE then control the expression of fear responses, including freezing behavior and related autonomic nervous system (e.g., blood pressure and heart rate) and endocrine (pituitary-adrenal hormones) responses. Lesion and imaging studies, described in the text, have confirmed that the human amygdala is also involved in fear conditioning, but the involvement of subregions of the amygdala is still poorly understood in humans. CG, central gray; LH, lateral hypothalamus; PVN, paraventricular hypothalamus. From Medina et al. (2002). Nader et al., 2001). For context conditioning, the inputs appear to enter the amygdala from the hippocampus (Maren, 2001; LeDoux, 2000). Connections likely to mediate this processing including the projections from the ventral CA1 and subiculum to the basal amygdala. As with tone conditioning, the CE is involved in controlling responses, but the input region involves synapses in the basal nucleus. The bed nucleus of the stria terminalis also seems to be involved in context conditioning (Sullivan et al., 2004; Walker et al., 2003b). While most fear conditioning studies to date have involved rodents, recent work in primates has confirmed the role of the central amygdala (Kalin et al., 2004). Recent electrophysiological studies have identified two groups of cells in LA, one involved in initial learning and the other in memory storage (Repa et al., 2001; Medina et al., 2002; Radwanska et al., 2002). These cell types are in separate regions of the dorsal part of the LA, with each region containing about 12,000 neurons (unpublished data). This precise localization provides new clues about where to search for additional cellular and molecular events that occur during learning and memory. Progress has also been made in elucidating the cellular and molecular mechanisms involved in fear condi- tioning (see Maren, 2001; Schafe et al., 2001; Blair et al., 2001; Rosenkranz and Grace, 2002; Sah et al., 2003; Rodrigues et al., 2004; Rosen, 2004; Fanselow and Poulos, 2005). While extensive discussion of these mechanisms is beyond the scope of this review, a brief summary is in order. This work has implicated NMDA receptors and voltage-gated calcium channels as sources of calcium entry during learning. The rise in calcium then triggers intracellular cascades that store the synaptic changes. Some of the intracellular processes involved include protein kinases (CamKII, PKA, PKC, MAPK), gene transcription factors (especially CREB), and RNA and protein synthesis. Protein synthesis has also been implicated in the maintenance of memory following retrieval, a process referred to as reconsolidation (see Nader et al., 2000). Animal studies have shown that the amygdala receives sensory information via two routes: a rapid but crude input from the sensory thalamus and a slower but more veridical representation from the sensory cortex (LeDoux et al., 1984; LeDoux, 1994, 2002). Either the thalamic or cortical pathway can be used for simple sensory stimuli such as those typically used in animalconditioning studies, but presumably more-complex stimuli would require cortical processing. However, even for complex stimuli it is possible that crude features of the stimulus might have emotional potency due to innate wiring. For example, through the thalamic pathway, the amygdala might be activated by features or fragments of stimuli. This could lead to inappropriate activation—for example, when walking through the woods, the amygdala might be activated by the curvature of a slender stick on the ground, as if it were a snake. Alternatively, through past learning certain stimulus features might acquire the ability to activate the amygdala unwittingly. Current techniques for examining human brain function do not allow the exploration of neural systems with the same level of specificity as animal models. Nevertheless, studies exploring the role of the human amygdala in fear learning are consistent with these models. Fear conditioning in humans results in an increased blood-oxygen-level-dependent (BOLD) signal in the amygdala as assessed with functional magnetic resonance imaging (fMRI; Buchel et al., 1998; LaBar et al., 1998). The magnitude of this BOLD response is predictive of the strength of the conditioned response (LaBar et al., 1998, Phelps et al., 2004). In addition, a subliminally presented CS—one presented so quickly that subjects are unaware of its presentation—leads to coactivation between the amygdala and both the superior colliculus and pulvinar, which was not apparent for a CS presented supraliminally (Morris et al., 1998a). These structures are potential components of a subcortical pathway for emotional detection, supporting the animal results suggesting two pathways for conveying information to the amygdala (also see discussion below). In addition, studies in patients with brain lesions are consistent with the animal models. Although the interpretation of lesion studies in humans can be problematic because these lesions almost always include damage to additional structures and often occur years before experimental testing, allowing the possibility for the development of compensatory mechanisms, these Review 177 studies can provide a hint into the critical function of structures in human brain. As expected from previous studies with other techniques, patients whose damage includes the amygdala fail to show physiological indications of conditioned fear. However, if the hippocampus in these patients is relatively intact, they are able to explicitly recollect and report the events of fearconditioning procedures, such as the relation between the CS and US (Bechara et al., 1995; LaBar et al., 1995). In contrast, damage that includes the hippocampus bilaterally but spares the amygdala impairs the ability to consciously report the events of fear conditioning, although there is normal expression of conditioned fear as assessed through physiological measures (Bechara et al., 1995). This dissociation following amygdala or hippocampal damage between indirect physiological assessments of the conditioned fear response (amygdala dependent) and awareness of the aversive properties of the CS (hippocampal dependent) supports the conclusion that there are multiple systems for the encoding and expression of emotional learning. We have emphasized the contribution of the amygdala in aversive emotional experiences because the neural basis of these has been elucidated most thoroughly in animal studies. However, some studies have explored positive emotions in animals (e.g., Holland and Gallagher, 1999; Rolls, 1999; Ono and Nishijo, 1992; Everitt et al., 1999; Baxter and Murray, 2002, Gallagher and Chiba, 1996) and humans (Anderson et al., 2003; Canli et al., 2002; Hamann et al., 1999; Johnsrude et al., 2000), suggesting that the amygdala’s role in implicit learning and emotion processing is not limited to fear. Emotional Modulation of Memory In addition to undergoing plastic changes that constitute implicit memories, the amygdala contributes to the memory storage functions of other systems, including systems that function both implicitly and explicitly (for review, see McGaugh, 2000; Packard and Cahill, 2001). Studies of rats and other laboratory animals, for example, have shown that damage to the hippocampus prevents the formation of certain kinds of spatial memories (a rodent analog of explicit memory) while damage to the striatum prevents the formation of habit memories (an example of implicit memory) (see Martin and Morris, 2002; Eichenbaum, 2002; Packard and Cahill, 2001). These two kinds of memories can be enhanced by systemic treatment with drugs that mimic the effects of adrenal hormones, including both adrenergic hormones (epinephrine and norepinephrine) and steroid hormones (cortisol/corticosterone) (McGaugh, 2000, 2002, 2004; Packard and Cahill, 2001; Roozendaal, 2002). Infusion of these drugs into the lateral and basal regions of the amygdala has a similar effect. Moreover, blockade of b-adrenergic receptors in the amygdala interferes with the modulatory effects of systemically administered adrenergic drugs and steroid hormones, and modulatory effects are also induced by direct stimulation of b-adrenergic or glucocorticoid receptors in the amygdala (McGaugh, 2000, 2002, 2004; Roozendaal, 2002; Packard and Cahill, 2001). The amygdala’s modulation of hippocampal- or striatal-dependent memories comes about primarily by enhancing the consolidation of memory rather than initial encoding (McGaugh, 2000, 2004; Packard and Cahill, 2001), although the amygdala may also influence processing during memory encoding (see ‘‘Emotional Influences on Attention and Perception’’ below). This is indicated by the fact that posttraining manipulations of the amygdala, a time when encoding is presumably complete, alter later memory performance (Packard and Teather, 1998). The hormonal changes that influence consolidation are concomitants of emotional arousal and as noted are triggered, at least in situations of danger, by amygdala processing of the fear-arousing event (LeDoux, 1996). These neurohormonal changes persist after termination of the threat and continue to modulate memory storage during this time, helping to insure that stimuli and events that lead to an emotional reaction, and that are likely more important to survival, are not forgotten (McGaugh, 2000). In spite of its ability to modulate spatial memories (hippocampal dependent) and habit memories (striatal dependent), the amygdala does not appear to be required to modulate memories of fear conditioning (amygdala dependent). Thus, posttraining manipulations that interfere with memory modulation in the hippocampus or striatum have no effect on the strength of conditioned fear (Wilensky et al., 1999; Lee et al., 2001). In humans, both psychological and neuroscience research supports the conclusion that the storage of explicit memories is modulated with arousal and that this modulation depends on the amygdala. Early psychological research examining the effect of emotion on explicit memory found that arousal during encoding results in less forgetting over time (Berlyne and Carey, 1968; Kleinsmith and Kaplan, 1963), consistent with enhanced consolidation or storage. Damage to the amygdala results in similar forgetting curves for arousing and neutral stimuli (LaBar and Phelps, 1998) and impaired delayed memory for emotional stimuli (Adolphs et al., 2000; Cahill et al., 1995). More recently, a number of brain-imaging studies have reported activation of the amygdala during encoding that is predictive of later memory retention for emotional stimuli (Cahill et al., 1996; Canli et al., 2000; Hamann et al., 1999; Dolcos et al., 2004) (Figure 2). In addition, pharmacological blockade of b-adrenergic receptors impairs enhanced memory for emotional events in humans, suggesting that the amygdala’s modulation Figure 2. Amygdala Activation Predicts Memory for Emotional Items Activation of the amygdala (arrows) during encoding predicts subsequent memory for emotional pictures (emotional Dm effect) but not for neutral pictures (neutral Dm effect). Adapted from Dolcos et al. (2004). Neuron 178 of consolidation is dependent on the neurohormonal changes that occur with arousal (Cahill et al., 1994). These findings in humans indicate a role for arousal and the amygdala in the modulation of explicit or episodic memories, but they do not clearly demonstrate that this occurs through enhancing the consolidation stage of memory formation. Two recent studies induced an arousal response immediately after a stimulus was encountered using a pharmacological (Cahill and Alkire, 2003) or pain (Cahill et al., 2003) manipulation. These studies found that poststimulus arousal enhanced later memory. Interestingly, this effect only emerged for stimuli rated as emotional prior to the study (compared to similarly rated stimuli that were not followed by an arousal manipulation). There was no effect of poststimulus arousal on stimuli previously rated as neutral. These results suggest that the amygdala’s modulation of memory consolidation in humans may favor stimuli that are predisposed to lead to an emotional reaction. There is not yet direct evidence in humans demonstrating that the amygdala modulates striataldependent skill or habit learning. However, given the evidence that hippocampal-dependent memories are modulated by amygdala-dependent processes in humans, together with the fact that hippocampal-dependent and striatal-dependent learning is modulated by amygdala-dependent functions in rats, it seems likely that striatal-dependent memory in humans might also depend on amygdala modulation in emotional situations, but this remains to be determined. Emotional Influences on Attention and Perception In addition to modulating memory systems, the amygdala also alters processing in cortical systems involved in attention and perception and thereby potentially influences a range of downstream cognitive functions. Two lines of research support this view. The Amygdala Influences Cortical Sensory Plasticity In a pioneering series of studies beginning in the 1970s, Weinberger and colleagues demonstrated that fear conditioning, which depends on amygdala plasticity, alters the neural representation of an auditory CS in the auditory system (Weinberger, 1995; Edeline, 1999). Specifically, these researchers mapped the auditoryfrequency-receptive fields of single cells in the auditory thalamus or auditory cortex. They then picked a frequency that was not the best frequency of the cell (the frequency that the cell responded strongest to) and used that as a CS. After several pairings with the US, they showed that the cell’s frequency response had shifted such that the response to the CS frequency was selectively enhanced at the expense of other frequencies. Further, these changes persisted for weeks. Importantly, the changes were relatively small and only occurred if the CS frequency was within an octave of the cell’s best frequency. In other words, emotional arousal does not completely rewire the auditory system but instead produces subtle shifts or biases that allow the system to become more attuned to important events and to then attend to these more strongly in the future. Plasticity is presumably restricted to the sensory system that is processing the CS. In contrast, plasticity in the amygdala occurs regardless of the CS modality. Modality-independent neural plasticity in the amygdala thus interacts with sensory-specific plasticity during fear conditioning. That the amygdala actually influences plasticity in specific sensory-processing systems is suggested by three lines of evidence. First, during auditory fear conditioning, plasticity in the lateral amygdala appears to develop sooner (in fewer trials) than plasticity in the auditory cortex (Quirk et al., 1997). Second, damage to or inactivation of the amygdala prevents plasticity in the auditory system (Poremba and Gabriel, 2001; Maren et al., 2001). Third, the central nucleus of the amygdala, via connections to the basal forebrain cholinergic system, appears to be necessary for auditory cortex receptive field plasticity (Weinberger, 1995). Due to limitations in techniques for examining the human brain, it is difficult to find conclusive evidence for lasting changes in the sensory representation of stimuli that have acquired an emotional significance through learning, as predicted by the findings from Weinberger and colleagues (Weinberger, 1995; Edeline, 1999). However, brain-imaging studies have demonstrated enhanced cortical responses to learned emotional stimuli. For instance, Dolan and colleagues (Morris et al., 2001a) conditioned subjects to fear a tone and found greater auditory cortex responses to this tone CS compared to a neutral tone. In addition, Anderson (2004) showed enhanced activation to arousing words in the lingual gyrus, a region thought to be important for the cortical representation of lexical items (Booth et al., 2002). In both of these studies, the amygdala also responded to these stimuli, consistent with the notion that the amygdala may support changes in the cortical representation of stimuli linked with emotion, though it is not possible to determine in such studies whether amygdala activity played a causal role in cortical activity. The Amygdala Facilitates Attention to Salient Stimuli It has long been known that when salient stimuli appear they are more likely to enter into awareness (Cherry, 1953). An important goal of cognitive neuroscience is to understand how the brain allows unattended salient stimuli priority in awareness. One hypothesis is that after a salient stimulus is detected by the amygdala, projections from the amygdala to the cortex (Amaral et al., 2003) are able to facilitate attention and perception (Armony et al., 1997; Armony and LeDoux, 1999; Whalen et al., 1998). Kapp and colleagues (B.S. Kapp et al., 1996, 1997, Soc. Neurosci., abstract) have shown that cells in CE respond to a CS and that fluctuations in the cortical EEG are correlated with changes in the spontaneous activity of CE cells. Both direct and indirect pathways are proposed for the amygdala’s transitory modulation of cortical regions. First, there are reciprocal connections between amygdala nuclei and sensory cortex (Amaral et al., 2003), indicating a means by which the amygdala could influence sensory processes through direct projections. Second, the CE projects to the nucleus basalis of Meynert (NBM), which projects to widespread cortical areas, many of which are sensory-processing regions. Acetycholine, which is released in these cortical areas via the NBM, has been shown to facilitate neuronal responsivity (Chiba et al., 1995; Everitt and Robbins, Review 179 Figure 3. Activation of Visual Cortex to Fear Faces Is Diminished following Amygdala Damage Statistical parametric maps of emotion 3 group interaction (A–C) across the whole brain, showing the main effect for fearful versus neutral faces between patient groups in (A) the left striate cortex, (B) left and right inferior temporal lobe, (C) and right inferior temporal lobe. Parameter estimates for the relative size of effect in this ANOVA (arbitrary units, mean centered) for peaks in (D) left striate cortex, (E) left inferior temporal lobe, and (F) right inferior temporal lobe, showing increased activation to fearful faces in both normal controls (N) and patients with damaged confined to the hippocampus (H), but not patients with both hippocampal and amygdala damage (H+A). Adapted from Vuilleumier et al. (2004). 1997; Weinberger et al., 1990; Edeline, 1999). The amygdala’s transitory modulation of cortical regions might result in increased cortical attention and vigilance in situations of danger (Armony et al. 1997; Armony and LeDoux, 1999; Whalen, 1998; Davis and Whalen, 2001). Evidence from a range of techniques in humans is consistent with a transitory-feedback model in which emotional stimuli, via the amygdala’s influence on cortical sensory processing, can influence attention and perception. The first line of evidence in support of this model is brain-imaging studies demonstrating that amygdala activation to fear (versus neutral) faces does not depend on subjects’ awareness of the presentation of the faces (Whalen et al., 1998), or whether or not the faces are the focus of attention (Vuilleumier et al., 2001, Anderson et al., 2003, Williams et al., 2004). These studies indicate that the amygdala responds to a fear stimulus automatically and prior to awareness. It is proposed that this quick amygdala response, early in stimulus processing, enables modulation of subsequent attention and perception (Whalen, 1998). More direct evidence that the amygdala plays a critical role in the facilitation of attention and perception for emotional stimuli comes from studies examining patients with damage to the amygdala. These patients fail to show the normal facilitation of attention for emotional stimuli (Anderson and Phelps, 2001). Further support that this facilitation of attention occurs through feedback from the amygdala to sensory cortical regions is derived from fMRI studies reporting enhanced activation in the visual cortex for fear (versus neutral) faces that is correlated with the magnitude of amygdala activation (Morris et al., 1998a, 1998b). This enhanced activation in visual regions to fear faces is absent if the amygdala is damaged (see Figure 3; Vuilleumier et al., 2004). Although the visual-processing regions identified in these studies, such as extrastriate cortex (see Figure 3), are thought to support perceptual processes, not necessarily the allocation of attention (Corbetta and Shulman, 2002), there is increasing evidence that many standard attention effects can be explained by changes in perceptual abilities that occur with attention (Carrasco, 2004; Polonsky et al., 2000). A recent study found that even early perceptual functions (i.e., contrast sensitivity) are enhanced with fear face cues (Phelps et al., 2005), consistent with the amygdala’s modulation of these primary visual regions (Vuilleumier et al., 2004). These lesion and imaging studies in humans provide strong support for the idea that the amygdala modulates attention and perception via rapid feedback to sensory-processing regions. However, there is considerable debate concerning some of the neural pathways underlying this modulation. The amygdala’s response to fear faces irrespective of awareness and attentional focus has led some to propose that the amygdala is detecting threat stimuli via a subcortical pathway that bypasses visual cortex (see the section on ‘‘Implicit Emotional Learning and Memory’’ for a description). In support of this argument, fMRI studies have reported a lack of activation in visual cortex when fear faces are processed without awareness (Pasley et al., 2004; Williams et al., 2004). In addition, Vuilleumier et al. (2003) took advantage of the fact that a visual subcortical pathway would be more sensitive to low-spatialfrequency information, whereas ventral visual cortex should respond preferentially to high-spatial-frequency information. They found that the amygdala, pulvinar, and superior colliculus (components of a proposed subcortical pathway) respond preferentially to low-spatialfrequency fear versus neutral faces, whereas the fusiform cortex responds preferentially to high-spatialfrequency fear versus neutral faces. Finally, two studies have reported amygdala activation to fear versus neutral faces in patients suffering from blindsight, whose visual cortices are damaged, resulting in an inability to identify stimuli (Morris et al., 2001b; Pegna et al., 2005). The amygdala’s enhanced BOLD response to fear faces in the absence of awareness, high-spatialfrequency information, or an intact visual cortex is argued to support the existence of a subcortical pathway for the detection of threat stimuli by the amygdala. However, a recent study by Pessoa et al. (2002) questions this conclusion. Using a demanding-attention task, they failed to observe amygdala activation in the Neuron 180 absence of attention. In this situation, the amygdala’s response to fear faces is not automatic. Pessoa et al. (2002) argue that the presence of a subcortical pathway for the detection of threat by the amygdala should result in an obligatory response to a fear face, regardless of how demanding the attentional task. In other words, the amygdala’s response should never be dependent on attention. In addition, there is not yet any anatomical evidence in primates verifying the existence of a subcortical pathway for the detection of visual information by the amygdala (Pessoa and Ungerleider, 2004). The research to date has been limited to rats using both auditory and visual stimuli (LeDoux et al., 1984, 1989; Romanski and LeDoux, 1992). The finding that attention can modulate the amygdala’s response to a fear face stimulus clearly demonstrates that activation of the amygdala can be dependent on attention in some circumstances. However, a limitation in interpreting the BOLD signal is that it is not an absolute response of neural function, but rather a relative response indicating the degree of difference between conditions (e.g., fear versus neutral faces). In such studies, it is only possible to demonstrate the abscence of a significant difference in BOLD signal rather than the abscence of an amygdala response. Of course, fMRI results used to argue in favor of a subcortical pathway by pointing to the lack of activation in visual regions in normal subjects suffer from the same limitation (Pasley et al., 2004; Williams et al., 2004). It is unclear whether fMRI, when used without other techniques, can provide sufficient evidence that a subcortical pathway for the detection of visual threat stimuli by the human amygdala does or does not exist. Irrespective of whether the amygdala receives input about the emotional significance of a stimulus via a cortical or subcortical pathway, amygdala activation to fear faces meets most of the principles of automaticity—that is it is independent of attention and awareness, with certain limitations for highly demanding attention tasks. A combination of lesion and imaging studies has provided strong evidence that transitory feedback from the human amygdala to sensory cortical regions can facilitate attention and perception. The amygdala’s influence on cortical sensory plasticity may also result in enhanced perception for stimuli that have acquired emotional properties through learning. By influencing attention and perception, the amygdala is altering the gateway of information processing. The amygdala enables preferential processing of stimuli that are emotional and potentially threatening, thus assuring that information of importance to the organism is more likely to influence behavior. Emotion and Social Behavior It has long been known that damage to the temporal lobes in monkeys results in a dramatic set of symptoms, including a reduction in the fear-arousing potency of predators (snakes and humans), changes in dietary habits (attempts to eat inedible objects), and unusual sexual behavior (engaging in homosexual behavior or attempting to copulate with members of other species) (Kluver and Bucy, 1937). In the 1950s, Weiskrantz (1956) proposed that many of the components of the so-called Kluver-Bucy syndrome were due to a dissociation of the sensory and affective properties of visual stimuli resulting from damage to amygdala. This was the origin of the idea that the amygdala plays a key role in emotional behavior. Much subsequent research in rats has elaborated on the importance of the amygdala in emotion (for review, see LeDoux, 2002; Davis and Whalen, 2001). In addition, a considerable body of research in monkeys has also further implicated the amygdala in emotion, including emotional responses to social stimuli (for review, see Ono and Nishijo, 1992; Aggleton and Mishkin, 1986; Rolls, 1999; Zola-Morgan et al., 1991; Meunier et al., 1999). Research evolving from the Kluver-Bucy syndrome first emphasized the importance of the amygdala in social behavior (Rosvold et al., 1954; Kling and Brothers, 1992; Meunier et al., 1999; Meunier and Bachevalier, 2002). Amaral and colleagues (Amaral, 2003) have recently revisited this issue in both adult and infant monkeys and concluded that damage to the amygdala in adult animals, while reducing fear of toy snakes, fails to produce significant adverse alterations in social and affiliative behavior. In contrast, damage in infants alters later adult behavior in such a way that fear of a toy snake is intact, but fear in social situations is altered. This specific effect on social fear is consistent with studies that have emphasized the importance of amygdala alterations in autism (Bachevalier, 1994; Baron-Cohen et al., 2000; Kemper and Bauman, 1993). Unlike the significant impairment in social responses observed in monkeys with early temporal lobe damage, especially involving the amygdala, humans with temporal lobe damage do not have social deficits that are readily apparent. The famous amnesic patient HM had his medial temporal lobe, including the amygdala, surgically removed in an effort to control epilepsy. However, even though HM had a lesion similar to the Kluver-Bucy lesion, his primary difficulty was explicit memory. His social behavior was reported as relatively normal (Milner et al., 1968). Case studies of patients with selective bilateral amygdala damage who are not amnesic also report relatively normal social behavior (Adolphs, 1999; Anderson and Phelps, 2002). These preserved social abilities may be related to intact components of the amygdala or to cognitive compensation for the loss of emotional functions. Specifically, patients with emotional impairments might use episodic or semantic memory of social information and responses, as well as habitual behavioral responses, to act normally in social and emotional situations. Indeed, such patients are aware of social norms and are able to correctly interpret appropriate social reactions from verbal descriptions (Adolphs et al., 1995). They also show normal facial expressions of emotion (Anderson and Phelps, 2000) and rate their daily emotional states as similar to control subjects (Anderson and Phelps, 2002). It may be that this intact explicit representation and understanding of social responding and the normal subjective sense of emotion is sufficient to guide social behavior in most circumstances, especially if the person went though development with an intact amygdala and only developed amygdala pathology later in life. In this regard, it is important to note that for most reported cases of patients with selective bilateral amygdala lesions, it is unclear when, during development, the damage Review 181 Figure 4. Activation of the Amygdala to Subliminal Presentation of Fearful versus Happy Eyes Presenting the whites of fear (far left) versus happy (middle) eyes for only 17 ms, which is too quick for subjects to consciously detect the stimuli (subliminal presentation), results in a differential BOLD-signal response in the amygdala (far right). Adapted from Whalen et al. (2004). occurred (Adolphs et al., 1995; Cahill et al., 1995; Phelps et al., 1998). It is possible that they learned, with their amygdala intact, how to behave in social situations. Such individuals may be especially capable of cognitively compensating for the absence of the amygdala later in life. Even though patients with amygdala damage do not have markedly impaired social behavior, they do have deficits in social responses in some circumstances. In spite of their ability to generate normal facial expressions of emotion, they do not always interpret facial expression in others correctly. This impairment is most apparent for fear expressions. Damage to the amygdala results in an impairment in interpreting the intensity of fear expressions in others (Adolphs et al., 1999). Recent studies suggest that the amygdala primarily responds to the eyes in fear faces (see Figure 4; Whalen et al., 2004), and amygdala lesions result in a deficit in appropriately focusing on the eyes when interpreting facial expression (Adolphs et al., 2005). This impairment in decoding facial expressions also leads these patients to rate some faces as more trustworthy and approachable than normal controls (Adolphs et al., 1998). The deficit in responding to facial expressions is subtle, but nonetheless reliable and potentially important. Further, it is consistent with the results of the primate mentioned studies above suggesting a role for the amygdala in social emotions (Amaral, 2003). Another deficit in social responding observed following amygdala damage is the ability to read social interpretations into ambiguous circumstances. The tendency to anthropomorphize, that is, to apply human traits to nonhuman forms, occurs naturally without effort. For example, a classic video by Heider and Simmel (Heider and Simmel, 1944) shows triangles and a circle moving around a box. Although these are simple geometric shapes, the nature of the movements result in most people describing the shapes as characters engaging in a social interaction. Patients with amygdala damage, however, fail to read any social intent and simply describe the movement of the shapes when responding to this video (Heberlein and Adolphs, 2004). It is suggested that this deficit in anthropomorphizing may be indicative of the kind of implicit social signals that depend on the amygdala for normal interpretation. The impairments in social responses following amygdala damage in humans are robust, but limited. In con- trast, brain-imaging studies in normal subjects have reported amygdala activation to a range of social stimuli, from bodily movements indicating fear (de Gelder et al., 2004), to race group information (Phelps et al., 2000), to pictures of individuals who have previously acted unstrustworthy (Singer et al., 2004). These imaging studies suggest that the amygdala responds to a wide range of social cues and indicate that the subtle deficits observed following amygdala lesions may reflect compensatory mechanisms and may not be indicative of the extent of the amygdala’s involvement in normal social behavior. Across species, there is evidence that the amygdala is an important component of the network of neural systems that produce adaptive social interactions. Inhibition and Regulation of Emotion Until recently, researchers investigating the neural systems of emotion have primarily focused on understanding how stimuli acquire an emotional significance or how an emotional response might alter perception or cognition. However, there is a growing interest in translating this research on the neural systems of emotion to the treatment of emotional disorders. Although understanding how emotional significance is learned and expressed is important in this endeavor, it is equally important to discover how learned emotional responses might be diminished or controlled. There has been recent progress uncovering the neural mechanisms underlying the alteration of emotional responses. Below we will discuss research on extinction, reconsolidation, and emotion regulation. Each of these involves the amygdala, as well as other brain regions. Extinction of Emotional Learning One technique for altering learned emotional responses, especially those established as Pavlovian associations, is experimental extinction (Myers and Davis, 2002; Morgan et al., 1993; Bouton, 2002; Sotres-Bayon et al., 2004). With this procedure, a CS previously linked to aversive US is presented alone for a number of trials until the subject learns that the CS no longer predicts the US. Although conditioned emotional responses (CRs) are diminished with extinction, the responses are inhibited rather than eliminated. Thus, after complete extinction a number of situations can result in the reexpression of previously extinguished CRs, such as the passage of time (spontaneous recovery), exposure to the US Neuron 182 Figure 5. Regions of Activation during Extinction of Conditioned Fear in Humans (A) Activation of the vmPFC (arrow), indicating a decrease in BOLD signal to the CS+ (relative to a CS2) during acquisition. This vmPFC BOLD response increased as extinction training progressed, and the magnitude of this increase predicted the retention of extinction learning. (B) Amygdala activation (arrow) to the CS+ during acquisition versus early extinction, indicating that extinction training results in a reduction in BOLD signal to the CS+. This change in the amygdala response during extinction training predicted early extinction success. Adapted from Phelps et al. (2004). (reinstatement), or exposure to the original learning context (renewal) (for a review, see Bouton, 2002). The reappearance of extinguished CRs demonstrates that the learned response was stored and that its expression was inhibited by extinction learning. Animal research on extinction has implicated the amygdala and medial prefrontal cortex (mPFC). We start with the mPFC because it was implicated in fear extinction first. Damage to mPFC, especially the ventral-most portion of this region (vmPFC), significantly alters the ability of rats to undergo extinction learning (Morgan et al., 1993, 2003; Morgan and LeDoux, 1995; Quirk et al., 2000; Quirk and Gehlert 2003; Garcia, 2002). Furthermore, neural activity increases in the mPFC as extinction is learned (Milad and Quirk, 2002; Rosenkranz et al., 2003), and electrical sitmulation of mPFC can facilitate extinction learning (Milad et al., 2004). It appears that vmPFC may be particularly involved in the memory or retention of extinction (Quirk and Gehlert, 2003). Because the amygdala is needed to express fear responses, most work on the role of the amygdala in extinction has used pharmacological manipulations rather than lesions. Particularly important have been studies by Davis and colleagues (Myers and Davis, 2002; Walker and Davis, 2002; Walker et al., 2002). They have shown that blockade of NMDA receptors in the amygdala disrupts extinction and that facilitation of NMDA receptor function with d-cycloserine enhances extinction (Walker and Davis, 2002; Walker et al., 2002). This work has important implications for the treatment of fear disorders (Davis and Myers, 2002). It has recently been reported that the administration of d-cycloserine to humans prior to exposure therapy for the treatment of phobia enhances the treatment response (Ressler et al., 2004). Much like research on the acquisition of fear conditioning, brain-imaging evidence in humans indicates that the neural mechanisms of fear extinction are preserved across species (LaBar et al., 1998; Knight et al., 2004; Phelps et al. 2004). A recent study (Phelps et al., 2004) found that amygdala activation was correlated with the expression of the CR during both acquisition and early extinction, suggesting that the amygdala is involved in initial extinction learning. After a day, however, activity in the subgenual anterior cingulate—a region hypothesized to be homologous to infralimibic cortex in rats and monkeys (Kim et al., 2003)—was predictive of the retention of extinction and the expression of the CR, consistent with a role for this region in the re- call of extinction learning (see Figure 5). Although it is not possible to determine a critical functional role from brain-imaging data, these results are largely consistent with research from nonhuman animals on the mechanisms of extinction learning. Reconsolidation of Emotional Learning A second mechanism by which learned emotional responses could be altered is through disrupting reconsolidition. It is well established that in order for short-term memory (STM) to persist as long-term memory (LTM), the neurons storing the memory have to synthesize new proteins (Davis and Squire, 1984; Bailey et al., 1996). Thus, STM is labile and subject to disruption until it is converted to LTM by protein synthesis and consolidated. However, there is also evidence that consolidated LTM becomes labile and subject to disruption after retrieval (Sara, 2000; Nader et al., 2000). The latter is said to show that memory is reconsolidated following retrieval (Sara, 2000; Nader et al., 2000). For example, in a fear-conditioning paradigm, disruption of protein synthesis in the lateral amygdala immediately after training (Schafe and LeDoux, 2000) or immediately after retrieval of the CS (Nader et al., 2000) has no effect on the CR for several hours but then prevents the expression of the CR the next day. The impairment observed in the later expression of the CR when protein synthesis in the amygdala is disrupted immediately after retrieval of the CS indicates that previously learned emotional responses can be disrupted after memory reactivation. Reconsolidation of memory has now been shown in a variety of species and for a variety of training conditions exploring both hippocampal- and amygdaladependent learned-fear responses (Sara, 2000; Nader et al., 2000; Debiec et al., 2002; Dudai 2002; Eisenberg et al., 2003; Milekic and Alberini, 2002). Blockade of reconsolidation has been proposed as a possible treatment for PTSD and other conditions in humans involving intrusive memories (Nader et al., 2000; Debiec and LeDoux, 2004). There is some evidence for reconsolidaiton in humans (Sara, 2000; Walker et al., 2003a), but more work on human subjects is needed. While there are many questions regarding the mechanisms underlying postretrieval memory vulnerabiltiy and whether the phenomenon involves storage deficits, retrieval deficits, or extinction (Sara, 2000; Nader et al., 2000; Debiec et al., 2002; Dudai 2002; Eisenberg et al., 2003; Milekic and Alberini, 2002; Lattal et al., 2004; Riccio et al., 2002), the fact is that manipulations of the Review 183 brain after retrieval with drugs or behaviorally with interference tasks (Anderson et al., 2004) can affect memory performance. These dramatic effects on memory need to be more thoroughly incorporated into psychological and biological models of memory in the future. Emotional Regulation and Coping In complex social and emotional environments, it is often important to be able to control our emotional reactions in order to behave in adaptive or appropriate ways. The ability to regulate and cope with emotion is a fundamental skill for normal social interaction. It is also an important component of mental health. A characteristic difficulty in many psychological disorders, such as depression or anxiety, is the maladaptive cognitive interpretation of situations or events. A component of treatment for these disorders is to teach active coping skills, consciously applied strategies that help assure adaptive interpretations or reactions to emotional stimuli. In humans, a significant portion of our emotional life is generated by our thoughts, interpretations, and imagination. The habits and skills we develop to guide these internally generated emotional events are critical. Recent research on the neural systems of emotion regulation explores the mechanisms underlying the ability to use cognitive and active coping strategies to alter emotional reactions. In animals, emotion regulation (coping) can be studied by examining the manner in which fear-arousing stimuli are dealt with. Although rats initially freeze to a CS associated with shock, they can, with training, learn to actively control their exposure to the CS and thus reduce its aversive consequences (Amorapanth et al., 2000). For example, rats can learn to cross to the other side of a chamber to terminate or prevent the occurrence of a fear-arousing CS. Damage to the central nucleus of the amygdala prevents freezing to the CS (a passive form of coping) but does not interfere with the ability to learn responses that terminate or prevent the CS (active coping). In contrast, damage to the basal amygdala has no effect on freezing but prevents learning of the active coping response. Damage to the lateral nucleus prevents both forms of learning. This suggests that the lateral nucleus is essential for processing the CS and that passive and active coping responses elicited by the CS involve different outputs of the lateral nucleus within the amygdala. These findings are relevant to understanding the benefits of active coping strategies in anxiety disorders (LeDoux and Gorman, 2001). For related studies involving both appetitive and aversive conditioning, see Killcross et al. (1997) and Everitt et al. (1999). In humans, emotion regulation using cognitive control has been examined in several ways. For example, reappraisal involves reinterpreting an emotional stimulus in such a way that the emotional reaction is altered. If shown a scene of women crying outside a church, subjects might interpret it as representing a funeral and the women as expressing grief. However, an attempt to reappraise this ambiguous scene might lead to another interpretation in which subjects imagine the women are crying in joy at the end of a wedding. Successful reappraisal of emotional scenes alters physiological arousal responses, as well as ratings of emotional reactions (Gross, 2002). Two recent fMRI studies found that this type of reappraisal of emotional scenes also leads to a decrease in amygdala activation (Ochsner et al., 2002; Schaefer et al., 2002). This cognitive modulation of the amygdala may be linked to regions in the prefrontal cortex (PFC) thought to be important for the online processing of information, or working memory. The study by Ochsner and colleagues (Ochsner et al., 2002) found that the activation in the left middle frontal gyrus increased during successful reappraisal and was negatively correlated with the amygdala response. Although there are not direct projections between the amygdala and this lateral PFC region, this region does project to more-directly connected medial PFC regions (Barbas, 2000), which may be part of a circuit that helps regulate the amygdala. Fears acquired through conditioning can also be diminished using emotion-regulation strategies (Delgado et al., 2004). A recent study examined the ability to reinterpret the meaning of a CS paired with a shock, by using the CS (a colored square) as a cue to imagine a soothing scene. When the CS prompted the imagination of a soothing scene, subjects showed less of an arousal response and diminished amygdala activation. Similar to the reappraisal study by Ochsner et al. (2002), a region of the left middle frontal gyrus showed increased activation during the reinterpretation of the CS. Interestingly, the vmPFC region that has previously been linked to extinction learning in humans (Phelps et al., 2004) also showed a similar pattern of response when conditioned fear was diminished with a cognitive strategy, suggesting that overlapping neural mechanisms for amygdala inhibition/regulation may support both cognitive emotion-regulation strategies and extinction learning. Cognitive interpretation of a stimulus can also enhance amygdala activation and the expression of negative affect or fear. Fears to previously neutral stimuli that have been acquired symbolically, through verbal instruction, result in physiological expression and amygdala activation that is similar to that observed in fear conditioning (Phelps, et al., 2001). Damage to the left amygdala impairs the physiological expression of these instructed, abstract fears (Funayama et al., 2001). In addition, reappraising the meaning of an ambiguous scene so that it is more fearful results in enhanced amygdala activation (Ochsner et al., 2004). In everyday human life, many of our fears are imagined and anticipated, but never actually experienced. These results suggest that the expression of fears that are represented abstractly, and based on imagination and interpretation, rely on similar neural systems as fears learned directly though fear conditioning. Understanding the mechanisms mediating the alteration of emotional responses will be critical as we attempt to apply research on the neuroscience of emotion to the treatment of emotional disorders. By inhibiting, disrupting, or regulating emotional responses we may be able to change maladaptive emotional reactions. Reconsolidation, extinction, and emotion regulation are three important tools in the translation of basic neuroscience to clinical practice. Conclusion Animal models of amygdala function have provided a foundation on which to explore the representation of emotion in the human brain. In this review, we highlight Neuron 184 the evidence for similarities in amygdala function across species, focusing on five topics of investigation. Studies on the neural systems of implicit emotional learning, emotion and memory, emotion’s influence on attention and perception, social responding, and emotion inhibition and regulation indicate an important role for the amygdala. Although studies in humans cannot explore the neural systems of behavior with the same level of specificity as research in nonhuman animals, identifying links in the neural representation of behavior across species results in a greater understanding of both the behavioral influence and neural representation of emotion in humans. Anderson, M.C., Ochsner, K.N., Kuhl, B., Cooper, J., Robertson, E., Gabrieli, S.W., Glover, G.H., and Gabrieli, J.D. (2004). 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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. 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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
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. 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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. 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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

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Attentional and Interpretive Bias
Memory and learning are influenced by emotion in various ways. Emotions have specific roles in
the stages of learning such as encoding information, consolidation of memories and recalling of
experiences. Emotion determines the level of attention being ac...

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