Attentional and Interpretive Bias

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Explain 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.

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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: becker54@msu.edu 1248 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. MOOD AND INATTENTIONAL BLINDNESS Leyman, & De Lissnyder, 2010; MacLeod, Mathews, & Tata, 1986). By contrast, most experiments that have investigated attentional biases for emotional stimuli in nonselect populations or populations that are or selected to be low in anxiety or depression, have either failed to find an emotional bias (Bar-Haim et al., 2007) or have found a bias away from negative stimuli (Frewen, Dozois, Joanisse, & Neufeld, 2008). Given that most people report having more positive than negative affect (Watson, Clark, & Tellegen, 1988), the finding of a bias away from negative and toward positive in these experiments is consistent with a mood congruent bias. Although this line of reasoning led us to predict a detection benefit when the unexpected object was congruent with a person’s mood, we were not at all certain of this outcome. Our uncertainty was driven by two factors. First, there are substantial and substantive differences between the tasks previously used to support a mood congruent attentional bias and the inattentional blindness task we used. Most previous research (see Yiend, 2010, for review) used either modified cuing paradigms (see Frewen et al., 2008 for review; MacLeod et al., 1986), or visual search (Matsumoto, 2010; Rinck, Becker, Kellermann, & Roth, 2003; Rinck, Reinecke, Ellwart, Heuer, & Becker, 2005) paradigms to demonstrate that attention is biased toward an emotionally charged stimulus. In both of these methods the location and/or identity of the critical target object is unknown and the participant must find the target among multiple possible objects or locations. As such, one could describe these tasks as divided attention tasks (see Pashler, 1998 Chapter 3, for a review) that evaluate the relative weighting of an emotional object in the competition for attention when the attentional system is attempting to shift attention to a new object. By contrast, in the inattentional blindness procedure one is not attempting to find a new object to which to attend, but instead already has a prespecified attentional task and is attempting to maintain attentional focus on that task. As such, this task is represents a selective attention task (see Pashler, 1998 Chapter 2, for a review) in which the detection of the unexpected object represents the interruption of an ongoing attentional task, and thus indicates how well a stimulus captures attention away from an ongoing task (Most et al., 2005) or causes an interrupt signal that interrupts ongoing attentional control (Corbetta & Shulman, 2002). In addition to the above theoretical concerns, there are also a number of published reports that suggest that we might not find a mood congruent advantage. For instance, there are claims that people should exhibit a mood-incongruent attentional bias to maintain homeostasis (Derryberry & Tucker, 1994; Gawronski, Deutsch, & Strack, 2005; Rothermund, Wentura, & Bak, 2001). Others have suggested that positive mood results in a wider attentional focus (Rowe, Hirsh, Anderson, & Smith, 2007), which would predict that people placed in a positive mood should be more likely to detect an unexpected stimulus regardless of its valence. Finally, there are suggestions that a negative mood leads to more drifts in attention (Smallwood, Fitzgerald, Miles, & Phillips, 2009), which would predict that people placed in a negative mood should be more likely to detect an unexpected stimulus regardless of its valence. In short, while there is consensus for mood-congruent attentional biases among people who are depressed or anxious, there is less of a consensus that the same bias exists in nonselect populations. In addition, none of the previous research has used an inattentional blindness task to evaluate whether a mood congruent stimulus can capture attention away from on an ongoing focused attentional task. 1249 Method Participants Two hundred thirty-eight university undergraduates with normal or corrected to normal vision participated for course credit. Procedure All participants were run individually in dimmed, sound attenuated booths that had a PC and 19 in. CRT running at 100 Hz. All surveys, the mood manipulation and experimental displays were programmed in Macromedia Director and the data was automatically saved into texts files for off-line analysis. Participants completed the State Trait Anxiety Inventory questionnaire before participating in the experiment. During the first phase of the experiment, participants were randomly assigned to receive a positive, negative, or neutral mood induction procedure. In the neutral condition, participants wrote about the route that they took to arrive at the lab. In the positive and negative conditions (see Appendix) participants were asked to write descriptive words about an emotional life event (Richter & Gendolla, 2009; Westermann, Spies, Stahl, & Hesse, 1996). Participants could journal for up to 4 min, or could self-terminate the induction task at any point after the first minute. On average participants wrote for over 2.5 min (M ⫽ 152 s, SE ⫽ 4.08 s) and the amount of time journaling did not vary by mood induction condition [F(2, 233) ⬍ 1]. Immediately after the mood induction task, participants performed a variation of Most et al.’s (Most et al., 2001) IB paradigm. Participants were shown six stationary white disks that appeared on a black display window surrounded by a gray boarder. Each ball had a diameter of 2.4 degrees of visual angle and the display window was a 15 ⫻ 11° rectangle in the center of the computer display. Three balls were empty while the other three contained the scrambled features of a schematic face (see Figure 1). After 2 s, each disk began to move in an independent, pseudorandom path. Periodically, the disks occluded one another, changed directions, and changed speeds (between two speeds: ⬃2 and ⬃3 deg/s). When a disk reached the edge of the black display window, it bounced off the edge. Participants were asked to track the three empty balls and count how many times they bounced off the side of the display. The entire tracking phase lasted for 16 s, after which subjects reported the number of bounces that the empty disk made. Consistent with previous experiments (Mack & Rock, 1998; Most et al., 2001), trials one and two had no unexpected event. In trials three through five, an unanticipated seventh ball appeared 7 s into the tracking task. This disk contained the same features as the distractor disks, but the features were unscrambled so that the disk appeared to contain a schematic, emotional face. For a given participant, the face was either smiling or frowning for all three trials in which it appeared. The unexpected disk drifted into the display window from the upper right corner and drifted across the screen for 6 s, exiting on the lower left. If the unexpected disk crossed paths with any other disk, it occluded the other disk. Trial three, the first trial in which the face appeared, was the critical IB trial. After participants reported the number of bounces they detected in trial three, they completed a brief mood manipulation check that consisted of four questions that they rated on a 7-point Likert-type scale (see Appendix). Two of these questions BECKER AND LEINENGER 1250 4.03, p ⬍ .001, than the sad group (M ⫽ 4.92, SE ⫽ .16). The neutral group (M ⫽ 5.36, SE ⫽ .15) rated their moods marginally lower than the happy group, t(150) ⫽ 1.95, p ⫽ .053, and significantly higher than the sad group, t(158) ⫽ 2.02, p ⫽ .045. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. STAI Survey To verify that our assignment to conditions did not produce groups that differed in anxiety we ran two 3 ⫻ 2 ANOVAs, one with state and one with trait anxiety as the dependent variable. Each ANOVA had three levels of mood induction and two levels of stimulus valence. Both main effects and the interaction were nonsignificant for both measure of anxiety (all F ⬍ 1). Distractors Unexpected Target Frowning Stimuli Smiling Stimuli Figure 1. IB method. The top panel is a schematic of the stimuli used in the IB task. Participants tracked the three empty disks for 16 s as they moved in a pseudorandom fashion occasionally bouncing off the side of the display window. Participants’ task was to count how many times the empty disks bounced off the sides. In Trials 3–5, an unanticipated seventh ball appeared 7 s into the tracking task. It could display a happy or sad face and drifted into the screen on the upper right and traversed across the screen, exiting on the lower left. It was visible for a total of 6 s. After Trials 3–5, participants were asked whether they noticed anything odd during the tracking task. The bottom panel shows the odd item and the to-be-ignored disks for each facial emotion condition. assessed the participants’ mood and two assessed arousal level. One of the two questions assessing a given variable was reverse coded. Following the manipulation check, participants were asked if they had seen anything odd during the tracking task. If they selected “no,” they advanced to trial four. If they selected “yes,” a text box appeared and they typed a description of the odd event before proceeding to Trial 4. These descriptions were used to verify that participants had detected the unexpected ball. Because participants were asked about the presence of an odd item in trial three, the fourth trial was considered a divided attention task in which participants might be monitoring the display for odd items (Most et al., 2001). In the fifth trial, participants were instructed to no longer count the ball bounces and simply watch the display. This trial was used to verify that the face stimuli were highly visible if attention was not otherwise engaged. Only two participants failed to detect the face in this trial and their data were eliminated from the analyses. The IB Trial In Trial 3, we found fairly high rates of IB; across all conditions the unanticipated disk was detected by only 23.3% of the participants (see Table 1). These high IB rates are surprising given prior reports that face stimuli are relatively immune from IB (Devue, Laloyaux, Feyers, Theeuwes, & Brédart, 2009; Mack & Rock, 1998). More importantly, the data suggest that people were more likely to notice a face that was congruent with their mood than one that was incongruent (see Figure 2). To evaluate this congruency effect, we used binary logistical regression to model the detection responses. A model with only the mood induction term and the facial emotion term was no better than the base model without them, ␹2(1) ⫽ .89, p ⬎ .3. However, including an interaction term in the regression model that coded whether the faces and mood induction were incongruent (sad/smiling and happy/frowning), were neutral (neutral mood/smiling and neutral mood/frowning), or were congruent (sad/frowning and happy/smiling) significantly improved the model fit, ␹2(1) ⫽ 5.314, p ⫽ .021, and the interaction term was significant, p ⫽ .024. Subsequent chi-square tests indicate the interaction resulted because participants were significantly more likely to detect a frowning face when they received the sad mood induction than the neutral, ␹2(1) ⫽ 4.02, p ⫽ .045, or happy, ␹2(1) ⫽ 4.77, p ⫽ .029, mood induction. While there was a trend for people to detect more smiling faces in the happy mood condition than the neutral or sad mood condition, the effect did not approach significance, both p ⬎ .4. Table 1 Number of Participants Who Detected and Missed the Unexpected Event Results Manipulation Check The manipulation check confirmed that the mood induction procedure was effective; it influenced mood, F(2, 233) ⫽ 8.066, p ⬍ .001, but not arousal, F(2, 233) ⫽ 1.28, p ⬎ .25. Planned t tests (two-tailed) found that the happy mood induction group (M ⫽ 5.74, SE ⫽ .12) rated their mood significantly higher, t(158) ⫽ Happy Face Sad Face Trial 3 Trial 4 Mood induction Mood induction Happy Neutral Sad Happy Neutral Sad 12 (27) 5 (32) 9 (28) 6 (33) 10 (34) 14 (26) 35 (4) 26 (11) 28 (9) 32 (7) 30 (14) 33 (7) Note. Numbers of detected faces are presented, with the number of missed faces in parentheses. Each row corresponds to the type of unexpected face (happy or sad). Columns are grouped by mood induction condition, with Trial 3 on the left and Trial 4 on the right. MOOD AND INATTENTIONAL BLINDNESS Smile Frown Percentage of Parcipants who Detected the Face on Trial 3 40% 35% 30% 25% 20% 15% 10% 5% This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 0% Happy Neutral Sad Mood Manipulaon Figure 2. Detection of the unexpected stimulus. The percentage of participants who detected the unexpected object (ordinate) is presented as a function of the mood manipulation (abscissa) and stimulus valence (separate bars). The cross-over interaction indicates that people were far more likely to notice the unanticipated face when its valence matched their induced mood. The Divided Attention Trial Detection rates increased dramatically in the fourth, divided attention trial; across all conditions the face was detected by 78% of the participants (see Table 1). Even so, the congruency effect was still present when assessed by the regression method (interaction term, p ⫽ .012) used to analyze the data from Trial 3. Follow up chi square tests reveal that the source of this interaction was that the smiling face was detected by more people in the happy mood condition than the sad mood condition, ␹2(1) ⫽ 5.66, p ⫽ .017, while there was a nonsignificant trend for more people to detect the frowning face when they were in the sad mood condition than the happy mood condition, ␹2(1) ⫽ 1.61, p ⫽ .21. Thus, even though people may have been dividing attention to monitor the display for an unexpected event, the face was easier to detect when its emotional valence matched their induced mood. Ball Counting Errors Total counting errors were generally low (across all trials, the average counts were within 6.3% of the actual number of ball bounces) and did not vary as a function of mood induction condition or face stimuli; an ANOVA with three levels of mood induction and two levels of face stimuli found no main effects nor an interaction, all Fs ⬍ 1. We also examined whether people who detected the face in the critical IB trial had different error rates than those who experienced IB. Participants who detected the face in Trial 3, made no more errors in trials one and two, t(234) ⫽ .80, p ⫽ .43, but those who detected the face made significantly, t(234) ⫽ 2.95, p ⫽ .003, more errors (M ⫽ 9.6%, SE ⫽ 1.8%) in Trial 3 than those who experience IB (M ⫽ 5.2%, SE ⫽ .6%). This pattern of data suggests that those who noticed the face were as engaged in the primary task (Trials 1 and 2); however, when the face broke though their attentional filter it diverted attention away from the primary task leading to more ball counting errors in the IB trial. 1251 Detection Performance and Individual Differences In post hoc analyses, we investigated whether the few individuals within a mood induction condition who detected the unexpected stimulus were different in terms of their anxiety level or responsiveness to the mood induction than those individuals who experienced IB. To perform these analyses within each mood induction condition we ran separate 2 (smiling/frowning face) ⫻ 2 (detected/experienced IB) ANOVAs with state anxiety, trait anxiety, and self-reported mood as the dependent variable. These analyses allowed us to determine whether those in a mood induction condition who noticed the unexpected object differed from those in the same condition who experience IB. We found little evidence that those who detected the unexpected object differed from those that experienced IB in terms of anxiety. For state anxiety, both the ANOVA for the happy and sad mood induction conditions yielded nonsignificant main effects for stimulus valence and detection group and no valence by group interaction (all p ⬎ .15). The pattern was the same when trait anxiety was used as the dependent variable, with no comparisons approaching significance (all p ⬎ .12) except for a trend toward a valence by detection group interaction that appeared only in the sad mood manipulation condition, F(1, 73) ⫽ 3.39, p ⫽ .07. This trend resulted because people who detected the smiling face tended to have lower anxiety scores (M ⫽ 35.90, SE ⫽ 3.35) than those that missed it (M ⫽ 39.93, SE ⫽ 1.94), but those that detected the frowning face tended to have higher anxiety scores (M ⫽ 43, SE ⫽ 2.83) than those that missed it (M ⫽ 37.3, SE ⫽ 2.21). This pattern is broadly inconsistent with the suggestion that our mood-congruent bias was driven by a subset of participants with aberrant anxiety. A similar pair of ANOVAs was run using the self-reported mood ratings (given during the manipulation check) as the dependent variable. For the group that experienced the sad mood induction procedure, neither of the main effects nor the interaction approached significance, all F(1, 80) ⬍ 1. By contrast, for the happy mood induction participants there was a main effect of face valence, F(1, 72) ⫽ 8.41, p ⫽ .005, and a marginal main effect of detection group, F(1. 72) ⫽ 3.69, p ⫽ .06, both qualified by a significant interaction, F(1, 72) ⫽ 6.87, p ⫽ .01. The source of this interaction (see Figure 3), was that the mood ratings for participants that detected a sad face (M ⫽ 4.4, SE ⫽ .45) were much lower than participants that detected the happy face (M ⫽ 6.04, SE ⫽ .29), missed the sad face (M ⫽ 5.75, SE ⫽ .18), and missed the happy face (M ⫽ 5.83, SE ⫽ .19). This pattern of results is inconsistent with the possibility that our findings of a moodcongruent bias were driven by a subset of participants that were particularly influenced by the mood induction procedures. Instead, the results demonstrate that the few people who noticed the frowning face despite being in the happy mood induction condition were those who self-reported low affect, a finding that is broadly consistent with the overall mood-congruent finding. Discussion The data from the IB trials suggest that stimuli which are congruent with one’s current mood are more likely to “break through” the attentional filter during an attentionally demanding task. As such, the findings demonstrate that one’s mood influences the attentional filter, creating an “emotional set” that biases attention such that mood BECKER AND LEINENGER 1252 7 Self Reported Mood 6 5 4 3 Frown 2 Smile This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 1 Happy Mood Happy Mood Missed Detected Sad Mood Missed Sad Mood Detected Figure 3. Self-reported mood as a function of condition and detection performance. The left side of the figure plots data for the group that received the happy mood manipulation and the right plots the group that received the sad mood manipulation. Within each mood manipulation group, means are further broken down by detection performance (whether participants detected the unexpected object or experienced IB) and the valence of the unexpected stimulus. congruent stimuli are more likely to capture attention, much as an “attentional set” biases attention toward stimuli that have goalcongruent features (Folk, Remington, & Johnston, 1992; Most et al., 2005). This finding is noteworthy for a number of reasons. Our findings demonstrate a mood-congruent attentional bias in response to a temporary mood shift among a nonselect group of participants. Most previous research documenting mood-congruent attentional biases have demonstrated the effect in anxious (Bar-Haim et al., 2007) or depressed samples (Frewen et al., 2008) that have relatively long lasting negative affect. In addition, the few studies that have reported mood congruent attentional biases because of short term mood manipulations have focused on the finding that positive mood manipulations increase attention to positive material (Tamir & Robinson, 2007; Wadlinger & Isaacowitz, 2006). By contrast, our mood-congruent result from the IB trial was primarily driven by the participants in the negative mood condition; the frowning face was invisible to almost everyone except for those in the sad mood manipulation condition. It is worth noting that we found only a slight trend for a mood-congruent bias for those in the happy condition in the IB trial; however, there was a significant happy congruent bias in the divided attention trial. As a result, we think our data is broadly consistent with, or at least not inconsistent with, previous reports of mood congruent attentional biases for induced positive moods (Tamir & Robinson, 2007; Wadlinger & Isaacowitz, 2006). Thus, our finding provides additional evidence of a mood congruent attentional bias among nonselect samples, and extends this claim to people placed in a temporary negative mood. In addition, the current research is the first to use an inattentional blindness task to investigate mood-congruent biases. This task differs in important ways from the modified attentional cuing paradigms and visual search paradigms that have been used to investigate moodcongruent attentional biases (Yiend, 2010). In those paradigms, the participant is asked to select the appropriate location from among a set of alternatives. As such, the tasks intentionally engage attentional selection mechanisms and then examine how different stimuli are weighted within the competition inherent to the selection process. In short, they examine the mechanics of this selection competition in a task where one must choose a new location for attention. By contrast, in the inattentional blindness task, the participants are not asked to select new object for attention. Instead, they are asked to maintain their attention on a prespecified set of objects. In this paradigm, the detection of the unexpected object reflects an engagement of the attentional selection process despite volitional control to suppress it; it represents an interruption of this volitional control (Corbetta & Shulman, 2002). In addition, this interruption appeared to divert resources away from the primary task, thereby producing more ball counting errors in those participants who noticed the unexpected object. To our knowledge we are the first to demonstrate that the mood-congruent bias can disrupt ongoing attentional control in this way. Although we consider the use of the inattentional blindness task as a strength of our design, the low detection rates raise the possibility that our results are driven by a few participants that might be somewhat aberrant. To investigate this possibility, within a given mood induction condition, we compared the anxiety ratings and self-reported mood of those who detected the face with those that experienced IB. In general, we found no evidence that our participants who experience mood-congruent biases were aberrant in either their mood or anxiety. Indeed, the only interesting effect from these analyses was that the people in the happy mood induction condition who detected the mood-incongruent frowning face, were those who self-reported low affect. This effect could be interpreted as additional evidence for a mood-congruent attentional bias; only those for whom the happy mood induction procedure failed detected the frowning face. However, one should be cautious in making this interpretation. Given our desire to ensure that the effects of our mood manipulation lasted throughout the critical IB trial, we placed the mood manipulation check after the presentation of the unexpected face. Thus we cannot rule out an alternative interpretation of this effect. It is possible that those who detected the frowning face subsequently reported their mood as lower, either because of demand characteristics or because the detection of a frowning face decreased their mood. Summary We found that one’s mood can alter attentional filtering such that mood congruent stimuli are more likely to break through an attentional filter and become part of one’s conscious experience. This finding suggests that mood can influence very early and basic cognitive processes and cause an interruption in the ongoing volitional control of attention. This early gating of information may, at least in part, contribute to mood’s ability to influence more complex cognitive processes such as judgment and decision making (Cryder, Lerner, Gross, & Dahl, 2008; Loewenstein & Lerner, 2003). In short, we have long known that whether one reports the glass as half full or half empty may depend on the person’s mood. The current research suggests that this might not simply be a response bias, but that the person’s mood may actually alter which aspects of the environment reach awareness, such that people in a positive mood selectively perceive the full part of the glass while people in a negative mood selectively perceive the empty portion. References Bar-Haim, Y., Lamy, D., Pergamin, L., Bakermans-Kranenburg, M. J., & van Ijzendoorn, M. H. (2007). Threat-related attentional bias in anxious This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. MOOD AND INATTENTIONAL BLINDNESS and nonanxious individuals: A meta-analytic study. Psychological Bulletin, 133, 1–24. Becker, M. W., & Pashler, H. (2005). Awareness of the continuously visible: Information acquisition during preview. Perception & Psychophysics, 67, 1391–1403. Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. [10.1038/nrn755]. Nature Reviews Neuroscience, 3, 201–215. Cryder, C. E., Lerner, J. S., Gross, J. J., & Dahl, R. E. (2008). Misery is not miserly: Sad and self-focused individuals spend more. Psychological Science, 19, 525–530. Derryberry, D., & Tucker, D. M. (1994). Motivating the focus of attention. In P. M. Niedenthal, & S. Kitayama (Eds.), Heart’s eye: Emotional influences in perception and attention. (pp. 167–196). San Diego, CA: Academic Press. Devue, C., Laloyaux, C., Feyers, D., Theeuwes, J., & Brédart, S. (2009). Do pictures of faces, and which ones, capture attention in the inattentional-blindness paradigm? Perception, 38, 552–568. Eizenman, M., Yu, L. H., Grupp, L., Eizenman, E., Ellenbogen, M., Gemar, M., & Levitan, R. D. (2003). A naturalistic visual scanning approach to assess selective attention in major depressive disorder. Psychiatry Research, 118, 117–128. Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception and Performance, 18, 1030–1044. Frewen, P. A., Dozois, D. J. A., Joanisse, M. F., & Neufeld, R. W. J. (2008). Selective attention to threat versus reward: Meta-analysis and neural-network modeling of the dot-probe task. Clinical Psychology Review, 28, 307–337. Gawronski, B., Deutsch, R., & Strack, F. (2005). Approach/avoidance-related motor actions and the processing of affective stimuli: Incongruency effects in automatic attention allocation. Social Cognition, 23, 182–203. Haines, R. (1991). A breakdown in simultaneous information processing. In G. Obrecht & L. W. Stark (Eds.), Presbyopia research: From molecular biology to visual adaptation (pp. 171–175). New York: Plenum Press. James, W. (1890). The principles of psychology (p. 697, Vol I). New York: Henry Holt and Co. Koster, E. H. W., De Raedt, R., Goeleven, E., Franck, E., & Crombez, G. (2005). Mood-congruent attentional bias in dysphoria: Maintained attention to and impaired disengagement from negative information. Emotion, 5, 446 – 455. Koster, E. H. W., De Raedt, R., Leyman, L., & De Lissnyder, E. (2010). Mood-congruent attention and memory bias in dysphoria: Exploring the coherence among information-processing biases. Behaviour Research and Therapy, 48, 219 –225. Loewenstein, G., & Lerner, J. S. (2003). The role of affect in decision making. In R. Davidson, H. Goldsmith, & K. Scherer (Eds.), Handbook of Affective Science (pp. 619 – 642). New York: Oxford University Press. Mack, A. (2003). Inattentional blindness: Looking without seeing. Current Directions in Psychological Science, 12, 180 –184. Mack, A., & Rock, I. (1998). Inattentional blindness. MIT Press/Bradford Books series in cognitive psychology (p. 273). Cambridge, MA: The MIT Press. MacLeod, C., Mathews, A., & Tata, P. (1986). Attentional bias in emotional disorders. Journal of Abnormal Psychology, 95, 15–20. Matsumoto, E. (2010). Bias in attending to emotional facial expressions: Anxiety and visual search efficiency. Applied Cognitive Psychology 1253 Special Issue: Current Directions at the Juncture of Clinical and Cognitive Science, 24, 414 – 424. Mogg, K., & Bradley, B. P. (2005). Attentional bias in generalized anxiety disorder versus depressive disorder. Cognitive Therapy and Research, 29, 29 – 45. Most, S. B., Scholl, B. J., Clifford, E. R., & Simons, D. J. (2005). What you see is what you set: Sustained inattentional blindness and the capture of awareness. Psychological Review, 112, 217–242. Most, S. B., Simons, D. J., Scholl, B. J., Jimenez, R., Clifford, E., & Chabris, C. F. (2001). How not to be seen: The contribution of similarity and selective ignoring to sustained inattentional blindness. Psychological Science, 12, 9 –17. O’Regan, J. K., Deubel, H., Clark, J. J., & Rensink, R. A. (2000). Picture changes during blinks: Looking without seeing and seeing without looking. Visual Cognition Special Issue: Change Blindness and Visual Memory, 7, 191–211. Pashler, H. E. (1998). The psychology of attention (p. 494). Cambridge, MA: The MIT Press. Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8, 368 –373. Richter, M., & Gendolla, G. (2009). Mood impact on cardiovascular reactivity when task difficulty is unclear. [10.1007/s11031-009 –91344]. Motivation and Emotion, 33, 239 –248. Rinck, M., Becker, E. S., Kellermann, J., & Roth, W. T. (2003). Selective attention in anxiety: Distraction and enhancement in visual search. Depression and Anxiety, 18, 18 –28. Rinck, M., Reinecke, A., Ellwart, T., Heuer, K., & Becker, E. S. (2005). Speeded detection and increased distraction in fear of spiders: Evidence from eye movements. Journal of Abnormal Psychology, 114, 235–248. Rothermund, K., Wentura, D., & Bak, P. M. (2001). Automatic attention to stimuli signalling chances and dangers: Moderating effects of positive and negative goal and action contexts. Cognition and Emotion Special Issue: Automatic Affective Processing, 15, 231–248. Rowe, G., Hirsh, J. B., Anderson, A. K., & Smith, E. E. (2007). Positive affect increases the breadth of attentional selection. PNAS Proceedings of the National Academy of Sciences, USA, 104, 383–388. Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28, 1059 –1074. Simons, D. J., & Rensink, R. A. (2005). Change blindness: Past, present, and future. Trends in Cognitive Sciences, 9, 16 –20. Smallwood, J., Fitzgerald, A., Miles, L. K., & Phillips, L. H. (2009). Shifting moods, wandering minds: Negative moods lead the mind to wander. Emotion, 9, 271–276. Tamir, M., & Robinson, M. D. (2007). The happy spotlight: Positive mood and selective attention to rewarding information. Personality and Social Psychology Bulletin, 33, 1124 –1136. Wadlinger, H., & Isaacowitz, D. (2006). Positive mood broadens visual attention to positive stimuli. Motivation and Emotion, 30, 87–99. Watson, D., Clark, L. A., & Carey, G. (1988). Positive and negative affectivity and their relation to anxiety and depressive disorders. Journal of Abnormal Psychology, 97, 346 –353. Westermann, R., Spies, K., Stahl, G., & Hesse, F. W. (1996). Relative effectiveness and validity of mood induction procedures: A metaanalysis. European Journal of Social Psychology, 26, 557–580. Yiend, J. (2010). The effects of emotion on attention: A review of attentional processing of emotional information. Cognition and Emotion, 24, 3– 47. (Appendix follows) BECKER AND LEINENGER 1254 Appendix Mood Manipulation and Manipulation Check Materials A. The mood induction task for the positive mood condition is below. In the sad mood condition the word “happy” was replaced by “sad.” mood. This index could range from 1 to 7, with a higher score representing more positive mood. The arousal measure was obtained in a similar way, with question 4 being reverse coded and summed with question 2, so higher scores indicate greater arousal. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Instructions Instructions Recall an episode in your life that made you feel very HAPPY and continues to make you happy whenever you think about it, even today. Please imagine this episode as vividly as you can. Recall the events happening to you. Recall your surroundings as clearly as possible. Picture the people or objects involved. Try to think the same thoughts. Try to feel the same feelings. Use the spaces below to list any descriptive words that come to mind as you recall this event.” Please read the descriptors at the ends of each scale and then check the box along the scale that best describes how you feel RIGHT NOW. B. The mood manipulation check consisted of the following questions. The response to question 1 was reverse coded and then averaged with the response from question 3 to obtain an index of Received March 22, 2010 Revision received January 18, 2011 Accepted January 28, 2011 䡲 Unpleasant Tired Happy Tense OOOOOOO OOOOOOO OOOOOOO OOOOOOO Pleasant Alert Sad Relaxed Journal of Abnormal Psychology 2008, Vol. 117, No. 1, 182–192 Copyright 2008 by the American Psychological Association 0021-843X/08/$12.00 DOI: 10.1037/0021-843X.117.1.182 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. Updating the Contents of Working Memory in Depression: Interference From Irrelevant Negative Material Jutta Joormann Ian H. Gotlib University of Miami Stanford University This study was designed to assess the effects of irrelevant emotional material on the ability to update the contents of working memory in depression. For each trial, participants were required to memorize 2 lists of emotional words and subsequently to ignore 1 of the lists. The impact of irrelevant emotional material on the ability to update the contents of working memory was indexed by response latencies on a recognition task in which the participants decided whether or not a probe was a member of the relevant list. The authors compared response latencies to probes from the irrelevant list to response latencies to novel probes of the same valence (intrusion effect). The results indicate that, compared to control participants in both neutral and sad mood states, depressed participants showed greater intrusion effects when presented with negative words. In an important finding, intrusion effects for negative words were correlated with self-reported rumination. These findings indicate that depression is associated with difficulties removing irrelevant negative material from working memory. Results also indicate that the increased interference from irrelevant negative material is associated with rumination. Keywords: depression, working memory, cognition, emotion, attention thoughts and memories that serve to regulate and repair the mood state (Erber & Erber, 1994; Parrott & Sabini, 1990; Rusting & DeHart, 2000). The critical question, therefore, is why, in response to negative mood, some people fail to regulate their mood and instead initiate a self-defeating cycle of increasingly negative ruminative thinking and intensifying negative affect. If changes in mood are, in fact, associated with activations of mood-congruent material in working memory, the ability to control the contents of working memory might play an important role in the development of rumination and, therefore, in recovery from negative mood. Working memory is a limited-capacity system that provides temporary access to a select set of representations in the service of current cognitive processes (Cowan, 1999; Miyake & Shah, 1999). Thus, working memory reflects the focus of attention, holding those representations that a person is aware of at any given moment. Given the capacity limitation of this system, it is important that the contents of working memory be updated efficiently. It has been proposed that this task is controlled by executive processes and, more specifically, by inhibition (e.g., Friedman & Miyake, 2004; Hasher, Zacks, & May, 1999). Indeed, Hasher and Zacks (1988) posited that the efficient functioning of working memory depends on inhibitory processes that limit the access of information and update working memory by removing information that is no longer relevant. It is noteworthy, therefore, that several researchers have suggested that rumination and depression are associated with deficits in executive function, particularly in inhibition (Hertel, 1997; Joormann, 2005; Linville, 1996). Dysfunctions in updating the contents of working memory—more specifically an inability to appropriately expel negative cognitions and memories that were activated by a negative mood state from working memory as they become irrelevant—would lead to difficulties attending to and processing new information, result in rumination, and thereby make a depressive episode more likely. Recurrent and often unintentional and uncontrollable thoughts that involve negative, self-deprecating statements and pessimistic ideas about the self, the world, and the future are a hallmark of depressive episodes. Not only are these ruminative thoughts a debilitating symptom of depression but they have also been associated with vulnerability to the onset of depression, the recurrence of depressive episodes, and the maintenance of negative affect (Nolen-Hoeksema, 2000; Nolen-Hoeksema & Larson, 1999; Roberts, Gilboa, & Gotlib, 1998). It is critical, therefore, that we gain a better understanding of the underlying processes that increase the occurrence of ruminative thinking and, consequently, of the nature of the association between rumination and depression. Investigators examining the interaction of cognition and emotion have proposed that the experience of negative mood is generally associated with, or consists in part of, the activation of mood-congruent representations in working memory (Isen, 1984; Siemer, 2005). Thus, negative mood has been found to be related to more frequent negative thoughts, to selective attention to negative stimuli, and to greater accessibility of negative memories (Blaney, 1986; Mathews & MacLeod, 2005; Rusting, 1998). This research has also demonstrated, however, that negative mood alone does not necessarily lead to prolonged rumination. Indeed, changes in cognition due to negative mood are usually transient, and mood-congruent cognitions are often quickly replaced by Jutta Joormann, Department of Psychology, University of Miami; Ian H. Gotlib, Department of Psychology, Stanford University. This research was supported by German Research Foundation Grants DFG JO/399-1 and JO/399-2 awarded to Jutta Joormann, and by National Institute of Mental Health Grant MH59259 awarded to Ian H. Gotlib. Correspondence concerning this article should be addressed to Jutta Joormann, Department of Psychology, University of Miami, Coral Gables, FL 33124. E-mail: jjoormann@psy.miami.edu 182 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. WORKING MEMORY IN DEPRESSION A small number of investigators have examined associations between interference from emotional material, depression, and rumination by using a modified negative priming task (Goeleven, De Raedt, Baert, & Koster, 2006; Joormann, 2004). In this task, participants are instructed to respond to a target stimulus while ignoring a simultaneously presented emotional stimulus that is clearly marked as to-be-ignored and irrelevant to the task; on the subsequent trial, the to-be-ignored emotional stimulus may become the target. Negative priming is operationalized as the differential delay between responding to a previously ignored stimulus and responding to a novel stimulus (Hasher et al., 1999; Tipper, 2001; Wentura, 1999). Joormann (2006) found that participants who scored high on a self-report measure of rumination exhibited reduced negative priming in response to emotional distractors, a finding that remained significant even after partialing out the level of depressive symptoms. Joormann (2004) demonstrated that dysphoric participants and participants with a history of depressive episodes also exhibited reduced negative priming in response to negative material that they were instructed to ignore. Finally, Goeleven et al. (2006) recently replicated these findings using a negative priming task with emotional faces. These investigators demonstrated that, compared to nondepressed controls, depressed participants showed reduced negative priming of sad facial expressions but intact negative priming of happy expressions. It is important to note, however, that negative priming tasks assess only one aspect of interference: the ability to control the access of relevant and irrelevant material to working memory. While these studies suggest that depression, and probably also rumination, involve difficulty in keeping irrelevant emotional information from entering working memory, no studies have examined whether depression and rumination are also associated with difficulty in removing previously relevant negative material from working memory. Difficulties inhibiting the processing of negative material that is no longer relevant might explain why people respond to negative mood states and negative life events with recurring, uncontrollable, and unintentional negative thoughts. The present study was designed to test the formulation that depression and rumination are associated with a specific deficit in updating the contents of working memory that results in increased interference from irrelevant negative material. We posit that depression involves an inability to fully inhibit representations of previously relevant negative material. This inhibitory deficit leads to the prolonged activation of negative material in working memory, resulting in sustained negative affect and recurring negative thoughts. Thus, we propose that the inability to remove irrelevant negative information from working memory is related to the tendency to respond to negative mood and events with rumination. To test this hypothesis, we adapted a modified Sternberg task developed by Oberauer (2001, 2005a, 2005b) that combines a shortterm recognition task with instructions to ignore a previously memorized list of words to assess interference from irrelevant positive and negative stimuli. In this task, two word lists are presented simultaneously. After the lists are memorized, a cue indicates which of the two lists is relevant for the recognition task on the next display, in which participants indicate whether the probe that is presented came from the relevant list; probes from the no-longer-relevant list must be rejected, as must new probes. Oberauer (2001) and investigators who have used similar designs have found that participants take longer to reject probes from 183 the no-longer-relevant list than they do new probes (Monsell, 1978; Neuman & DeSchepper, 1992). These studies also demonstrate that participants have an automatic tendency to endorse items from the irrelevant list, which must be overridden. Thus, Oberauer (2001, 2005a, 2005b) has suggested that the difference between reaction times to an intrusion probe (i.e., a probe from the irrelevant list) and reaction times to a new probe (i.e., a completely new word) reflects the strength of the residual activation of the contents of working memory that were declared to be no longer relevant and, therefore, assesses a person’s ability to update the contents of working memory. Given the focus in the present study on depression and rumination, we varied the valence of stimuli in the relevant and the irrelevant lists. This design allows us to compare negative and positive intrusion probes to new words of the same valence in order to assess participants’ ability to remove both negative and positive material from working memory. In addition, to test the proposition that difficulties in updating the contents of working memory are not due solely to a negative mood state, we examined interference from irrelevant material both in currently depressed participants and in control participants who were induced to feel sad. We predicted that, compared to their nondepressed counterparts (both exposed and not exposed to a sad mood induction), depressed participants would exhibit increased interference from irrelevant negative material, as reflected by increased decision latencies to negative words from lists that are no longer relevant (i.e., a greater intrusion effect). We also predicted that the ability to update the contents of working memory would be related to the tendency to ruminate. Method Overview In this experiment we used a modified Sternberg task modeled after a task used by Oberauer (2001, 2005a, 2005b). Each trial in the experiment consisted of three separate displays: a learning display, a cue display, and a probe display. In the learning display, two lists of three words each were presented simultaneously. The words in one of the lists were presented in blue, and the words in the other list were presented in red. Words also differed in valence: Some of the words were positive, and others were negative. After the offset of the word lists, a cue was presented that informed participants which of the two word lists would be relevant for the recognition task that followed. The cue was either a red frame, which signaled that the red list would be relevant, or a blue frame, which signaled that the blue list would be relevant. Finally, in the probe display, a single black word appeared inside the red or blue frame, and participants were asked to indicate whether this word was from the relevant list. Participants were asked to respond as quickly and as accurately as possible by pressing the 1 key on the computer keyboard for “Yes” if the word came from the relevant list, or the 2 key for “No” if the word did not come from the relevant list. Participants’ responses and the latency of their key presses were recorded. Participants Participants were recruited from two outpatient psychiatry clinics in a university teaching hospital, as well as through advertise- 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. 184 JOORMANN AND GOTLIB ments posted in numerous locations within the community (e.g., Internet bulletin boards, university kiosks, supermarkets). Participants’ responses to a telephone interview provided initial selection information. This phone screen established that participants were fluent in English and were between 18 and 60 years of age. We excluded participants if they reported severe head trauma or learning disabilities, current or lifetime anxiety disorder, psychotic symptoms, bipolar disorder, or alcohol or substance abuse within the past 6 months. Eligible individuals were invited to come to the laboratory for a more extensive interview. Trained interviewers administered the Structured Clinical Interview for the DSM–IV (SCID; First, Spitzer, Gibbon, & Williams, 1996) to eligible participants during their first session in the study. The SCID has demonstrated good reliability for the majority of the disorders covered in the interview (Skre, Onstad, Torgeresn, & Kringlen, 1991; Williams et al., 1992). All interviewers had extensive training in the use of the SCID, as well as previous experience in administering structured clinical interviews with psychiatric patients. In previous studies, our team of interviewers achieved excellent interrater reliability. The ␬ coefficients were .93 for the diagnosis of major depressive disorder (MDD) and .92 for the “nonpsychiatric control” diagnosis (i.e., the absence of current or lifetime psychiatric diagnoses). For the current study, two independent raters rated a randomly selected sample of 25% of the SCID tapes and achieved perfect agreement with the original interviewers. Although this represents excellent reliability, we should note that the interviewers used the “skip out” strategy of the SCID, which may have reduced the opportunities for the independent raters to disagree with the diagnoses (Gotlib et al., 2004). Participants were included in the depressed group if they met the MDD criteria of the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM–IV; American Psychiatric Association, 1994). The never-disordered control group consisted of individuals with no current diagnosis and no history of any Axis I disorder. Participants were scheduled for a second session of “computer tasks,” usually within 2 weeks after the interview. Sixty-three individuals (23 diagnosed with MDD and 40 never-disordered controls) participated in this study. The control participants were randomly assigned either to receive (CTL-SAD; N ⫽ 19) or not to receive (CTL; n ⫽ 21) a sad mood induction. Questionnaires Participants completed the Beck Depression Inventory—II (BDI; Beck, Steer, & Brown, 1996), a 21-item, self-report measure of the severity of depressive symptoms. The acceptable reliability and validity of the BDI has been well documented (Beck, Steer, & Garbin, 1988). We also administered the 22-item Ruminative Response Scale (RRS) of the Response Style Questionnaire (Nolen-Hoeksema & Morrow, 1991) to examine how participants tend to respond to sad feelings and symptoms of dysphoria. The RRS assesses responses to dysphoric mood that are focused on the self (think about all your shortcomings, failings, faults, mistakes), on symptoms (think about how hard it is to concentrate), or on possible consequences and causes of moods (analyze recent events to try to understand why you are depressed) using a 4-point scale (almost never to almost always). In addition, the RRS assesses behavioral responses to sad moods (go someplace alone to think about your feelings). Previous studies using this measure have shown good test–retest reliability and acceptable convergent and predictive validity (Nolen-Hoeksema & Morrow, 1991; NolenHoeksema, Parker, & Larsen, 1994; Treynor, Gonzales, & NolenHoeksema, 2003). Treynor et al. (2003) recently suggested that the RRS is composed of two subscales that reflect adaptive and maladaptive components of rumination. Treynor et al. have interpreted the five-item Reflective Pondering subscales as assessing “a purposeful turning inward to engage in cognitive problem solving to alleviate one’s depressive symptoms,” and the five-item Brooding subscale as assessing “a passive comparison of one’s current situation with some unachieved standard” (p. 256). Given that Treynor et al. have reported that these subscales differentially predict concurrent and future depression, we included the Reflective Pondering and Brooding subscales in this study. Both subscales have been found to have acceptable internal consistencies and retest reliabilities (Treynor et al., 2003). Mood Induction Before participating in the task, half of the control participants were instructed to listen to sad music and try to imagine unpleasant times in their life that made them unhappy. The participants were asked to experience as intensely as possible the feelings of the music and their memories, and to listen to the tape for 3 min. The effectiveness of the mood manipulation was assessed with a visual analogue scale administered before and after the mood induction. Participants rated their mood state on a 10-point bipolar scale anchored with ⫺5 (very sad) and ⫹5 (very happy). Stimuli Words from the Affective Norms of English Words (Bradley & Lang, 1999), which lists valence and arousal ratings for over 1,000 English adjectives, verbs, and nouns on 9-point scales, were used as stimuli. Nouns with a rating of 4 or less were examined for possible inclusion in the negative valence condition, and nouns with a rating of 6 or more were examined for inclusion in the positive valence condition. We selected words from these lists, taking care to ensure that the positive and negative words did not differ in arousal ratings or word length. The final set of 208 positive nouns had an average valence rating of M ⫽ 7.28 (SD ⫽ 0.64) and an arousal rating of M ⫽ 5.49 (SD ⫽ 0.95), while the final set of 208 negative nouns had an average valence rating of M ⫽ 2.83 (SD ⫽ 0.72) and an average arousal rating of M ⫽ 5.42 (SD ⫽ 0.85). Positive and negative words in the two conditions did not differ on the arousal dimension or in average word length, both ts(414) ⬍ 1, ns. Design and Procedure We compared three different groups of participants (MDD, CTL, CTL-SAD groups). Our task consisted of eight different conditions (see Table 1): we varied the valence of the words in the relevant list (positive or negative) and the probe type (relevant probes [i.e., words from the relevant list]; intrusion probes [i.e., words from the irrelevant list]; new positive probes; and new negative probes). Each condition was presented four times in each block, and each run was composed of three blocks. In the critical trials, the red and the blue list included either only positive words WORKING MEMORY IN DEPRESSION 185 Table 1 Experimental Conditions, Response Latencies, and Percentage of Correct Responses 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. MDD CTL CTL-SAD Cond Relevant Irrelevant Probe M SD % M SD % 1 2 3 4 5 6 7 8 Positive Positive Positive Positive Negative Negative Negative Negative Negative Negative Negative Negative Positive Positive Positive Positive Relevant Intrusion New positive New negative Relevant Intrusion New positive New negative 1,135 1,493 1,062 1,040 1,181 1,389 1,060 1,098 194 186 225 207 187 185 264 221 93 86 97 99 89 86 99 98 1,106 1,308 1,056 1,027 1,124 1,387 1,016 1,020 236 318 251 275 194 336 223 201 95 94 99 100 96 94 100 99 M 1,097 1,155 1,012 986 1,113 1,283 988 1,001 SD % 372 344 321 308 340 311 331 307 96 86 98 99 95 86 98 97 Note. Cond ⫽ condition; MDD ⫽ participants diagnosed with major depressive disorder; CTL ⫽ control group; CTL-SAD ⫽ control group with sad mood induction. or only negative words, and the two lists always differed in valence. In addition to these critical trials, we included eight trials in each block in which positive and negative words were mixed within the red or blue lists to discourage participants from using the valence of the lists as a cue when responding to the probes and to be able to assess the use of this strategy.1 Thus, we presented 120 trials, which were preceded by five practice trials. For each participant, a random sample of words was selected from the word lists without replacement. Thus, words were never repeated within a block but could be presented up to three times within the experiment. All possible combinations of color assignment to the positive or negative list and the presentation of the blue and red lists in the upper or lower part of the screen were presented equally often within each block. The sequence of trials within blocks and the order of the blocks were randomized. Each of the trials began with the presentation of a fixation cross for 500 ms, followed by the simultaneous presentation of six words arranged in two rows of three words each (learning display). The words in one row were presented in blue, and the words in the other row were presented in red. The participants were instructed to read the words and to memorize them. The presentation time for this display was 7.8 s (1.3 s ⫻ number of words in the display). Next, a blank screen was presented for 800 ms, followed by the frame for 1 s (cue display). The frame was either blue or red to indicate which of the two lists just presented would be relevant for making the upcoming probe decision. Finally, the probe was presented in black in the frame in the center of the screen and remained on the screen until the participants made their response ( probe display). Participants were tested individually within 2 weeks after their initial diagnostic interview. Half of the control participants (selected randomly) were administered a mood assessment, followed by a sad mood induction, followed by another mood assessment. Participants were told that the experiment was designed to assess memory and learning. After responding to practice trials to familiarize themselves with the procedure and the stimuli, participants were presented with the 120 trials in three blocks with short breaks between the blocks. The entire task took about 30 min. Finally, participants completed the questionnaires described above. Results Participant Characteristics Demographic and clinical characteristics of the three groups of participants are presented in Table 2. As is evident from the table, the three groups did not differ significantly in age, F(2, 60) ⬍ 1, or education, ␹2(2) ⬍ 1, ns. As expected, the groups did differ in their BDI scores, F(2, 60) ⫽ 63.63, p ⬍ .01; the MDD group had significantly higher BDI scores than did the CTL and the CTLSAD participants, both ps ⬍ .05. In addition, MDD participants reported a greater tendency to respond to negative life events and negative mood states with rumination, as indicated by their elevated RRS scores, F(2, 60) ⫽ 21.12, p ⬍ .01, compared to the CTL and the CTL-SAD participants. Two participants in the MDD group reported comorbid disorders: 1 participant was diagnosed with comorbid dysthymia and 1 with comorbid dysthymia and binge eating disorder. Finally, the analysis of the pre–post induction sad mood ratings indicated that the mood induction was successful in the CTL-SAD group, t(18) ⫽ 9.60, p ⬍ .01. Correct Responses Oberauer (2001, 2005a, 2005b) found low overall error rates using a similar modified Sternberg task; consequently, we did not expect to find valence or group differences in error rates in the present study. The mean percentages of correct responses in the different conditions are presented in Table 1. As expected, overall error rates were low (MDD, 6.6%; CTL, 2.8%; CTL-SAD, 5%). We conducted mixed effects analyses of variance (ANOVAs) to examine differences in the number of correct responses as a function of group and experimental condition. We conducted a two-way ANOVA (Group [MDD, CTL, CTL-SAD] ⫻ Probe Valence [positive, negative]) to examine group differences in 1 We included control trials in which we presented lists that included both positive and negative words (mixed lists) to evaluate the use of valence as a strategy to make decisions about the probes. If participants remember the valence of the lists instead of the words, their performance should decrease in the mixed lists trials. Overall, however, response accuracy for the mixed list trials was well above 90% and did not differ among groups, F(2, 60) ⬍ 2, ns. JOORMANN AND GOTLIB 186 Table 2 Characteristics of Participants 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. Group Variables MDD CTL CTL-SAD N (N female) Age College education (%) Participants with comorbid diagnosis Beck Depression Inventory Rumination Scale Reflective pondering Brooding 23 (16) 35.45a (10.83) 69 2 27.48a (11.48) 51.63a (13.23) 11.01a (3.17) 12.12a (4.47) 21 (14) 35.52a (12.49) 66 0 1.19b (1.99) 31.54b (6.76) 9.25a (3.69) 6.86b (1.77) 19 (12) 33.42a (8.90) 78 0 1.25b (2.05) 30.26b (6.95) 8.18a (2.07) 7.18b (2.01) Note. In last seven rows, standard deviations are shown in parentheses. Means with the same subscript are not significantly different at p ⬍ .05. MDD ⫽ patients diagnosed with major depressive disorder; CTL ⫽ control group; CTL-SAD ⫽ control group with sad mood induction. correctly identifying relevant words. This ANOVA, which compared the correct responses of participants in three groups to probes from the relevant list (Conditions 1 and 5; see Table 1), yielded only a significant main effect for group, F(2, 60) ⫽ 3.56, p ⬍ .05; neither the main effect for probe valence nor the interaction of group and probe valence was significant, both Fs ⬍ 1. Follow-up tests indicated that the MDD participants had significantly fewer correct responses to relevant probes than did either the CTL participants, t(42) ⫽ 2.20, p ⬍ .05, or the CTL-SAD participants, t(40) ⫽ 2.12, p ⬍ .05, who did not differ significantly from each other, t(38) ⬍ 1, ns. We also conducted a three-way ANOVA (Group [MDD, CTL, CTL-SAD] ⫻ Probe Valence [positive, negative] ⫻ Condition [irrelevant, control]) comparing correct responses to intrusion probes (i.e., probes from the irrelevant list; Conditions 2 and 6 in Table 1) to responses to new probes of the same valence (Conditions 4 and 7 in Table 1). This analysis yielded only a significant main effect for condition, F(1, 60) ⫽ 51.14, p ⬍ .01. Overall, participants made fewer errors when evaluating a new probe than an intrusion probe. No other significant main effects or interactions with valence or group were obtained. In sum, therefore, while depressed participants made more errors in the relevant trials, there was no difference between depressed and control participants in the intrusion trials. Decision Latencies to Relevant Probes For all conditions (relevant and irrelevant probes), we restricted our analyses of decision latencies to trials on which participants made correct responses. To eliminate outliers, we treated decision latencies that exceeded 3 s as missing values (fewer than 5% of all reaction times). No group differences in the number of outlying latencies were obtained, F(2, 60) ⬍ 1. Mean decision latencies for participants in the three groups in the different experimental conditions are presented in Table 1. We had no specific predictions for group or valence differences in response to the relevant probe words and, in fact, a two-way ANOVA (Group [MDD, CTL, CTL-SAD] ⫻ Probe Valence [positive, negative]) conducted on the decision latencies in response to probes from the relevant lists (Conditions 1 and 5 in Table 1) yielded no significant main effects or interactions, all Fs ⬍ 1.2 Decision Latencies to Intrusion Probes (Intrusion Effects) Our main hypotheses involve decision latencies to the intrusion probes.3 First, we predicted a significant three-way interaction of group, valence, and condition. We expected that depressed participants would show increased interference from irrelevant negative words and, therefore, that MDD participants would be significantly slower than controls to decide whether negative intrusion probes came from the relevant list; no group differences were expected for decisions about new probes of the same valence. We tested this prediction by analyzing the decision latencies in the irrelevant condition with a three-way mixed-effects ANOVA (Group [MDD, CTL, CTL-SAD] ⫻ Probe Valence [positive, negative] ⫻ Condition [irrelevant, new]) (Conditions 2, 6 vs. 4, 6 in Table 1). This analysis yielded a significant main effect for condition, F(1, 60) ⫽ 124.99, p ⬍ .01, which was qualified by the predicted significant three-way interaction of group, valence, and condition, F(2, 60) ⫽ 5.03, p ⬍ .01. Because our hypothesis posits an interaction of group and condition only for negative intrusion probes, we conducted follow-up tests separately for positive and negative probes. For positive probes we obtained a significant main effect for condition, F(1, 60) ⫽ 92.80, p ⬍ .01; no other main effects or interactions were significant, all Fs ⬍ 2, ns. For the negative probes, however, we obtained significant main effects for group, F(2, 60) ⫽ 3.38, p ⬍ .05, and for condition, F(1, 60) ⫽ 86.65, p ⬍ .01, which were qualified by the predicted significant interaction of group and condition, F(2, 60) ⫽ 6.61, p ⬍ .01. Mean decision latencies for the interaction are presented in Figure 1. While no group differences were obtained for decision latencies to the new negative probes, all ts ⬍ 1, ns, MDD participants took significantly longer to decide whether a negative intrusion probe 2 We did not compare decision latencies to new probes in the relevant condition to relevant probes. Whereas the former require a “no” response, the relevant probes require a “yes” response; these conditions, therefore, cannot meaningfully be compared. 3 We calculated internal consistency scores for the intrusion effects to investigate the reliability of our reaction-time data. Cronbach’s alpha for the intrusion score for negative words was .91. WORKING MEMORY IN DEPRESSION 187 1600 1500 RT (in ms) 1400 1300 MDD CTL CTL-SAD 1200 1000 900 Neg-Intr Pos-Intr Neg-New Pos-New Condition Figure 1. Mean decision latencies for negative intrusion probes (Neg-Intr), positive intrusion probes (Pos-Intr), new negative probes (Neg-New), and new positive probes (Pos-New) in participants with major depressive disorder (MDD), control participants (CTL), and control participants with a sad mood induction (CTL-SAD). Error bars represent one standard error. RT ⫽ response time. was relevant than did the CTL participants in a neutral mood state, t(42) ⫽ 2.37, p ⬍ .05, and the CTL-SAD participants, t(40) ⫽ 4.06, p ⬍ .01, who did not differ significantly from each other, t(38) ⫽ 1.47, ns. Following Oberauer (2001), to examine this finding further, we calculated intrusion effects (decision latencies to intrusion probes minus decision latencies to new probes of the same valence), which are presented in Figure 2. No group differences were found in intrusion effects when comparing responses to positive material, all ts ⬍ 1, ns. As predicted, however, MDD participants had significantly higher intrusion effects when responding to negative material than did both the CTL participants, t(42) ⫽ 2.60, p ⬍ .01, d ⫽ 0.78, and the CTL-SAD participants, t(40) ⫽ 3.38, p ⬍ .01, d ⫽ 1.03, who did not differ from each other, t(38) ⬍ 2, ns. 600 500 Intrusion Effect (in ms) 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. 1100 400 MDD CTL 300 CTL-SAD 200 100 0 Negative Pos it ive Valence Figure 2. Mean intrusion effects for negative and positive material (response time to intrusion probes minus response time to new probes) in participants diagnosed with major depressive disorder (MDD), control participants (CTL), and participants with a sad mood induction (CTL-SAD) as a function of valence of facial expression. Error bars represent one standard error. JOORMANN AND GOTLIB 188 Table 3 Correlations and Regression Analysis of Intrusion Effects, BDI scores, and Rumination Intrusion 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. Measures Group: MDD (N ⫽ 23) Intrusion Positive BDI RRS Reflection Brooding Group: CTL (N ⫽ 40) Intrusion Positive BDI RRS Reflection Brooding Regression analysis Negative Positive .46* .25 .49* .50* .48* .03 .16 .16 .34 .46* .17 .23 .21 .26 .23 .19 .29 .03 DV:RRS Step 1: Step 2: ␤ ⌬R2 BDI: .71* BDI: .62* Intrusion negative: .34* .50* .11* Note. DV ⫽ dependent variable; RRS ⫽ Rumination Scale; MDD ⫽ major depressive disorder; CTL ⫽ control; Intrusion ⫽ intrusion effect in the Modified Sternberg Task; BDI ⫽ Beck Depression Inventory. * p ⬍ .05. Intrusion Effects and Rumination Our second main hypothesis was that the interference from irrelevant negative material would be related to individual differences in rumination. We expected, therefore, that the intrusion effect for negative material would be significantly correlated with individual differences in rumination. We computed correlations between intrusion effects for positive and negative material in the modified Sternberg task and self-reported depressive symptomatology and rumination within the groups of MDD and CTL participants.4 Table 3 presents the results of this analysis. Significant correlations between intrusion effects and rumination were found only in the MDD group, not in the CTL group. Given the high correlation of BDI and rumination scores within the MDD group (r ⫽ .71), we conducted a hierarchical linear regression analysis in which we predicted individual differences in rumination by entering BDI scores in Step 1 and intrusion effects for negative material in Step 2. In this regression model, both BDI scores and intrusion effects for negative material were significant predictors of individual differences in rumination in the MDD group and together explained 61% of the variance in rumination scores. Discussion Depression is associated with a tendency to respond to negative mood states and negative life events with ruminative thinking (Nolen-Hoeksema, 2000; Nolen-Hoeksema & Morrow, 1991). Moreover, numerous studies have demonstrated that rumination is linked to a heightened vulnerability for the onset and maintenance of depressive episodes (Lyubomirsky & Nolen-Hoeksema, 1993, 1995; Nolen-Hoeksema, 1991; Nolen-Hoeksema, Morrow, & Fredrickson, 1993). Despite this growing body of research, however, it is still unclear why some people are especially prone to ruminate while others find it relatively easy to reorient and recover from sad mood states. Previous research has reported that rumination is related to memory deficits and memory biases (Hertel, 1998; Lyubomirsky, Caldwell, & Nolen-Hoeksema, 1998). In the present study, we used a modified Sternberg task to test the hypotheses that depressed individuals experience difficulty updating the contents of working memory (more specifically, that they experience interference from irrelevant negative material), and that this difficulty is associated with rumination and might, therefore, be an important mechanism by which depression and rumination are related. As predicted, the results of this study indicate that participants diagnosed with major depression exhibit increased interference from irrelevant negative material when updating the contents of working memory. Specifically, compared to never-depressed controls, depressed individuals demonstrated greater decision latencies to an intrusion probe (i.e., a probe from the irrelevant list) than to a new probe (i.e., a completely new word), reflecting the strength of the residual activation of the contents of working memory that were declared to be no longer relevant (see Oberauer, 2001, 2005a, 2005b). An important finding is that this pattern was not found for positive material. To examine whether these difficulties were due simply to elevated levels of sad mood, we compared the performance of depressed participants to that of never-depressed participants who completed the task after receiving a sad mood induction. We find it important that the depressed participants exhibited greater interference from irrelevant negative material than did the control participants who were in a sad mood, indicating that a negative mood state alone is not sufficient to explain this effect. We also found that interference from negative irrelevant words was correlated with self-reported rumination. This relation with rumination was limited to the MDD group and remained significant even after partialing out the level of depressive symptomatology: the higher the participants’ scores on a self-report measure of rumination, the more difficulty they exhib4 The correlations within the control group without the mood induction did not differ from the correlations within the control group with the negative mood induction. Therefore, we collapsed across the control groups. 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. WORKING MEMORY IN DEPRESSION ited in removing task-irrelevant negative material from working memory. In sum, therefore, the present findings suggest that depression and rumination are associated with impairments in updating the contents of working memory—specifically, with difficulties in removing irrelevant negative material from working memory. This study adds to a small but growing literature linking depression and rumination with difficulties in inhibiting negative material. As we noted earlier, investigators have recently used a negative affective priming task to examine inhibition of emotional stimuli in depression. Although the results of these studies indicate that depression and a lifetime diagnosis of depressive episodes are related to increased interference in the processing of negative material (Goeleven et al., 2006; Joormann, 2004), it is important to recognize that the negative priming task and the modified Sternberg task differ in the degree to which stimuli are processed. In the negative priming task, participants are instructed to completely ignore the stimuli that are irrelevant to their response, to not process them at all. In contrast, in the Sternberg task, participants are instructed first to memorize all of the words in two lists, and then to ignore or forget one of the lists. Thus, while the negative priming design assesses individual differences in controlling the access of irrelevant material to working memory, the modified Sternberg task assesses individual differences in removing irrelevant material from working memory. Certainly, these two mechanisms are not mutually exclusive; indeed, they may contribute additively to rumination and depression. If a negative mood state is associated with the activation of mood-congruent material in working memory, the ability to restrict access to working memory could be closely related to the initial response to the moodinducing situation, whereas the ability to update the contents of working memory may be associated more strongly with recovery from the mood state. These findings are also consistent with a small number of studies that have used a “directed forgetting” task to examine individual differences in instructed forgetting (e.g., Korfine & Hooley, 2000; Tolin, Hamlin, & Foa, 2002). Of these, only one has examined intentional forgetting by participants diagnosed with MDD. Power, Dalgleish, Claudio, Tata, and Kentish (2000) used a directed forgetting paradigm in which, halfway through the learning phase, depressed and nondepressed participants were instructed to forget the words they had learned so far. When participants were tested on their final recall for words from both halves of the list, only the depressed participants showed better memory for the to-be-forgotten negative than for the to-be-forgotten positive words in the first half of the list. We find it interesting, however, that Power et al. found this effect only in one of their three studies, in which participants were asked to make selfreference judgments about the words. One significant difference between our study and Power et al.’s investigation is that we did not have participants encode the stimuli self-referentially; future research would do well to assess the effects of this procedure more explicitly. Although investigators have suggested that a deficit in executive functioning and, in particular, in inhibition plays an important role in rumination (Hertel, 1997; Linville, 1996), the current study is among the first to demonstrate such an association empirically. Because previous studies assessed executive functions while participants were processing neutral stimuli, they do not address the 189 important question of why rumination typically involves negatively valenced material. For example, Davis and NolenHoeksema (2000) used the Wisconsin Card Sorting Task and found that, compared to nonruminators, ruminators made more perseverative errors, regardless of their level of depressive symptomatology. Watkins and Brown (2002) induced rumination in depressed participants and demonstrated that, compared both to depressed participants in a distraction group and to nondepressed participants in a rumination group, these participants showed stereotyped counting responses in a random number generating task, reflecting their difficulty inhibiting prepotent responses. In contrast to these results, Goeleven et al. (2006) found that selfreported level of rumination was not related to differences in negative priming in response to sad faces. Goeleven et al. suggested that this finding might be due to the use of facial expressions, underscoring the potentially important association between rumination and semantic material, such as that found in the present study. In addition, however, it is possible that rumination is related more closely to difficulties expelling negative material from working memory than to difficulties controlling access of negative material to working memory, a formulation that should be examined more explicitly in future research. This study provides a critical first step in investigating the relations among working memory, rumination, and depression. Given the cross-sectional design of this study, formulations concerning underlying mechanisms and consequences are necessarily speculative. Although investigators have demonstrated that rumination is not simply a symptom of depression but also predicts the onset of depressive episodes (Just & Alloy, 1997; NolenHoeksema, 2000), the specific role that individual differences in the ability to update the contents of working memory may play in this association is unclear. Longitudinal studies are needed to assess this ability prior to the onset of rumination and/or depression in order to provide clear evidence that these impairments underlie rumination and thereby increase the risk for the onset and maintenance of depressive episodes. Preliminary evidence for this proposition comes from studies that have shown that inhibitory dysfunctions are not only correlates of depression, but can also be found in individuals who have recovered from a depressive episode (Goeleven et al., 2006; Joormann, 2004). In this context, it is also important to note that we found no evidence in the current study of increased interference from negative material in control participants in a negative mood state. We also found no significant correlation between interference and rumination in the control group, which is likely due to the restricted range in both the interference and the rumination measure in this group. Future studies should include additional rumination measures and, ideally, should compare self-reported rumination and experimental rumination manipulations. We also found that interference from negative material was associated with individual differences in both the brooding and the reflection components of rumination. This is surprising given that other studies have reported that cognitive biases in depression are related only to the maladaptive brooding component (Joormann, Dkane, & Gotlib, 2006). Although future studies are needed to clarify this finding, it is possible that deficits in updating working memory are related to a higher frequency of intrusive thoughts in general rather than being confined to maladaptive rumination. 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. 190 JOORMANN AND GOTLIB It is unclear whether an induced negative mood state can be compared to the negative mood that is associated with chronic depression. Consequently, it is possible that the obtained differences between the MDD participants and CTL participants who experienced a negative mood induction are due to differences in the intensity of their negative mood. Because the CTL participants in a negative mood state did not differ significantly from the CTL participants without a mood induction (and actually showed a trend toward weaker intrusion effects for negative material), while the MDD participants showed significantly stronger intrusion effects than did both groups of control participants, this is unlikely to be a viable explanation for the obtained results. It is possible, however, that the relation between mood and interference from negative material in working memory is nonlinear (e.g., interference effects are found only at very high levels of negative affect). Although a comprehensive discussion of the following issue is beyond the scope of this article, we should point out that the concept of inhibition has been criticized in research on attention and memory (e.g., Friedman & Miyake, 2004; MacLeod, Dodd, Sheard, Wilson, & Bibi, 2003). Thus, the construct validity of several measures that have been proposed to assess inhibition has been questioned (Friedman & Miyake, 2004). Specifically, research on the negative priming task has led investigators to propose a number of alternative mechanisms that could underlie the observed effects. Indeed, MacLeod et al. (2003) argued that many results that are interpreted in terms of inhibitory processes can be explained without reference to this concept. One possible alternative explanation for the present results is that there is differential initial activation of positive and negative material in the depressed group in the absence of group differences in the strength of inhibition. While we cannot rule out these alternative explanations, we should note that we are able to compare responses to relevant and irrelevant probes within the same task. If negative material were differentially activated in the MDD and CTL groups, we would expect the depressed group to be faster to respond to negative material in the relevant trials, that is, when judging that a presented negative probe indeed came from the relevant list. We did not find any evidence for such an activation effect in our data. While there might be other explanations for the lack of group differences in the relevant trials, this pattern of findings suggests that the concept of differential activation is not a complete explanation for the present findings. Still, we cannot rule out differences in other mechanisms that might underlie our findings, such as source monitoring (e.g., Johnson & Raye, 1981; Mandler, 1980) or other memory processes. Clearly, future studies are needed to investigate whether inhibition, or any of these alternative mechanisms, provides the best explanation of the observed effects. We believe, however, that our finding of increased response latencies to negative intrusion probes in the MDD group, which are correlated with self-reported rumination, represents an important finding even if the precise underlying mechanisms remain open to debate. These findings could provide insights into cognitive deficits in depression such as concentration difficulties and memory impairment. Indeed, several investigators have suggested that the source of general cognitive deficits in depression is a competition between attempts to direct attention to the task at hand and away from distractive and intrusive effects of negative thoughts and memories (Christopher & MacDonald, 2005; Hertel, 1998). In addition, the observed relation between the intrusion effect and self-reported rumination suggests that these problems in updating the contents of working memory might underlie the sustained processing of negative material that is seen in ruminative responses and has been found to predict both the onset and the maintenance of depression (e.g., Nolen-Hoeksema, 2000). Other potential alternative explanations of the obtained results involve the concept of generalized deficits in depression (Chapman & Chapman, 1973, 1978). That is, it is possible that MDD participants are characterized by general memory impairments, a general slowing in response times, or reduced confidence in their judgments. In this context, it is important to note that in the condition in which the MDD participants exhibited the slowest response times (negative intrusion probes), the CTL participants were faster to respond than they were to positive intrusion trials, indicating that this was not the most difficult condition overall. In sum, while additional studies are needed to investigate the role of inhibition in depression and its associated mechanisms, the present study is important in beginning to elucidate the nature of the relationship between rumination, removal of irrelevant negative material from working memory, and depression. Because the experience of negative mood states and negative life events is associated with the activation of mood-congruent cognitions in working memory, the ability to control the contents of working memory could be crucial in understanding and differentiating people who recover easily from negative affect from those who initiate a vicious cycle of increasingly negative ruminative thinking and deepening sad mood. Investigating individual differences in executive functions and, specifically, in the control of the contents of working memory, has the potential to provide important insights into the maintenance of negative affect and vulnerability to experience depressive episodes. References American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author. Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Manual for the Beck Depression Inventory—II. San Antonio, TX: Psychological Corporation. Beck, A. T., Steer, R. A., & Garbin, M. G. (1988). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8, 77–100. Blaney, P. H. (1986). Affect and memory: A review. Psychological Bulletin, 99, 229 –246. Bradley, M. M., & Lang, P. J. (1999). Affective Norms for English Words (ANEW): Technical manual and affective ratings. Gainesville, FL: The Center for Research in Psychophysiology. Chapman, L. J., & Chapman, J. P. (1973). Problems in the measurement of cognitive deficits. Psychological Bulletin, 79, 380 –385. Chapman, L. J., & Chapman, J. P. (1978). The measurement of differential deficit. Journal of Psychiatric Research, 14, 303–311. Christopher, G., & MacDonald, J. (2005). The impact of clinical depression on working memory. Cognitive Neuropsychiatry, 10, 379 –399. Cowan, N. (1999). An embedded-processes model of working memory. In A. Miyake & P. Shah (Eds.), Models of working memory: Mechanisms of active maintenance and executive control (pp. 62–101). New York: Cambridge University Press. Davis, R. N., & Nolen-Hoeksema, S. (2000). Cognitive inflexibility among ruminators and nonruminators. Cognitive Therapy and Research, 24, 699 –711. Erber, R., & Erber, M. W. (1994). Beyond mood and social judgment: Mood incongruent recall and mood regulation [Special issue]. European Journal of Social Psychology, 24, 79 – 88. 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. WORKING MEMORY IN DEPRESSION First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1996). Structured Clinical Interview for DSM–IV Axis I Disorders—Clinician Version (SCID-CV). Washington, DC: American Psychiatric Press. Friedman, N. P., & Miyake, A. (2004). The relations among inhibition and interference control functions: A latent–variable analysis. Journal of Experimental Psychology: General, 133, 101–135. Goeleven, E., De Raedt, R., Baert, S., & Koster, E. H. W. (2006). Deficient inhibition of emotional information in depression. Journal of Affective Disorders, 93, 149 –157. Gotlib, I. H., Kasch, K. L., Traill, S. K., Joormann, J., Arnow, B. A., & Johnson, S. L. (2004). Coherence and specificity of informationprocessing biases in depression and social phobia. Journal of Abnormal Psychology, 113, 386 –398. Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. In G. H. Bower (Ed.), The psychology of learning and motivation: Vol. 22 (pp. 193–225). San Diego, CA: Academic Press. Hasher, L., Zacks, R. T., & May, C. P. (1999). Inhibitory control, circadian arousal, and age. In D. Gopher & A. Koriat (Eds.), Attention and performance (pp. 653– 675). Cambridge, MA: MIT Press. Hertel, P. T. (1997). On the contribution of deficient cognitive control to memory impairments in depression. Cognition and Emotion, 11, 569 – 584. Hertel, P. T. (1998). The relationship between rumination and impaired memory in dysphoric moods. Journal of Abnormal Psychology, 107, 166 –172. Isen, A. M. (1984). Toward understanding the role of affect in cognition. In R. S. Wyer & T. S. Srull (Eds.), Handbook of social cognition (pp. 179 –236). Hillsdale, NJ: Erlbaum. Johnson, M. K., & Raye, C. L. (1981). Reality monitoring. Psychological Review, 88, 67– 85. Joormann, J. (2004). Attentional bias in dysphoria: The role of inhibitory processes. Cognition and Emotion, 18, 125–147. Joormann, J. (2005). Inhibition, rumination, and mood regulation in depression. In R. W. Engle, G. Sedek, U. von Hecker, & D. N. McIntosh (Eds.), Cognitive limitations in aging and psychopathology: Attention, working memory, and executive functions (pp. 275–312). New York: Cambridge University Press. Joormann, J. (2006). Differential effects of rumination and dysphoria on the inhibition of irrelevant emotional material: Evidence from a negative priming task. Cognitive Therapy and Research, 30, 149 –160. Joormann, J., Dkane, M., & Gotlib, I. H. (2006). Adaptive and maladaptive components of rumination? Diagnostic specificity and relation to depressive biases. Behavior Therapy, 37, 269 –280. Just, N., & Alloy, L. B. (1997). The response styles theory of depression: Tests and an extension of the theory. Journal of Abnormal Psychology, 106, 221–229. Korfine, L., & Hooley, J. M. (2000). Directed forgetting of emotional stimuli in borderline personality disorder. Journal of Abnormal Psychology, 109, 214 –221. Linville, P. (1996). Attention inhibition: Does it underlie ruminative thought? In R. S. Wyer, Jr. (Ed.), Ruminative thoughts: Vol. 9. Advances in social cognition (pp. 121–133). Mahwah, NJ: Erlbaum. Lyubomirsky, S., Caldwell, N. D., & Nolen-Hoeksema, S. (1998). Effects of ruminative and distracting responses to depressed mood on retrieval of autobiographical memories. Journal of Personality & Social Psychology, 75, 166 –177. Lyubomirsky, S., & Nolen-Hoeksema, S. (1993). Self-perpetuating properties of dysphoric rumination. Journal of Personality and Social Psychology, 65, 339 –349. Lyubomirsky, S., & Nolen-Hoeksema, S. (1995). Effects of self-focused rumination on negative thinking and interpersonal problem solving. Journal of Personality and Social Psychology, 69, 176 –190. MacLeod, C. M., Dodd, M. D., Sheard, E. D., Wilson, D. E., & Bibi, U. 191 (2003). In opposition to inhibition. In B. H. Ross (Ed.), The psychology of learning and motivation: Advances in research a...
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Running Head: MOOD, MEMORY AND ANXIETY

Mood, Memory & Anxiety
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MOOD, MEMORY AND ANXIETY

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Mood, Memory, and Anxiety- Attentional and Interpretive Bias
Mood refers to a person’s feeling at any particular time and is less intense, therefore, has less
influence on cognitive processes compared to emotions. There are two types of moods: positive and
negative moods. A person with a positive mood is normally cheerful, energetic, calm, ecstatic,
amused, content, blissful and dreamy. An individual with negative moods, on the other hand, can be
angry, frustrated, depressed, apathetic, envious, annoyed and even cranky.
Both a positi...


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