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
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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.
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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%
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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
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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.
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MOOD AND INATTENTIONAL BLINDNESS
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(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
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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-
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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
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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
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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)
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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
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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.
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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.
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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.
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