The functional design of depression's influence on attention: A preliminary test of alternative control-process mechanisms

Evolutionary Psychology
www.epjournal.net – 2007. 5(3): 584-604
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Original Article
The functional design of depression’s influence on attention: A preliminary
test of alternative control-process mechanisms
Paul W. Andrews, Virginia Institute for Psychiatric and Behavioral Genetics (VIPBG), Virginia
Commonwealth University, Richmond, VA, USA. Email: pandrews@vcu.edu (Corresponding author)
Steven H. Aggen, VIPBG, Virginia Commonwealth University, Richmond, VA, USA

Geoffrey F. Miller, Psychology Department, University of New Mexico, Albuquerque, NM, USA

Christopher Radi, Psychology Department, University of New Mexico, Albuquerque, NM, USA

John E. Dencoff, Psychology Department, University of New Mexico, Albuquerque, NM, USA

Michael C. Neale, VIPBG, Virginia Commonwealth University, Richmond, VA, USA
Abstract: Substantial evidence indicates that depression focuses attention on the problems
that caused the episode, so much that it interferes with the ability to focus on other things.
We hypothesized that depression evolved as a response to important, complex problems
that could only be solved, if they could be solved at all, with an attentional state that was
highly focused for sustained periods. Under this hypothesis, depression promotes analysis
and problem-solving by focusing attention on the problem and reducing distractibility. This
predicts that attentionally demanding problems will elicit depressed affect in subjects. We
also propose two control-process mechanisms by which depression could focus attention
and reduce distractibility. Under these mechanisms, depression exerts a force on attention
like that of a spring when it is pulled or like a magnet on a steel ball. These mechanisms
make different predictions about how depressed people respond emotionally to a task that
pulls attention away from their problems. We tested these predictions in a sample of 115
undergraduate students. Consistent with our main prediction, initially non-depressed
subjects experienced an increase in their depressed affect when exposed to an attentionally
demanding task. Moreover, the overall pattern of results supported the magnet metaphor.
Keywords: analytical reasoning, attention, control-process mechanisms, depression,
emotion, evolution, functional design.
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The functional design of depression’s influence on attention
Introduction

Organisms face multiple adaptive challenges, many of them simultaneously, and
they must have adaptations that allocate attention and cognitive resources to them.
Negative emotions are thought to have evolved, at least in part, for this purpose
(Alexander, 1986; Barlow, 2002; Buss, 2000; Ohman, Flykt, and Esteves, 2001; Thornhill
and Thornhill, 1989). Specifically, the effect of negative emotions on attention is thought to
be analogous to the influence of physical pain on attention. Physical pain draws attention to
problems that are causing, or threatening to cause, physical damage to the body, such as the
pain that one feels when one inadvertently puts one’s hand on a hot surface (Eccleston and
Crombez, 1999; Wall, 2000). Similarly, negative emotions are thought to have evolved to
draw attention to important problems in the environment (often of a social nature) that had
an important impact on fitness and could be fixed or ameliorated with attention (Alexander,
1986; Thornhill and Thornhill, 1989).

Control-process views of emotion suggest that they are related to progress or
frustration in finding solutions to problems or meeting goals (Carver, Lawrence, and
Scheier, 1996; Carver and Scheier, 1990). Negative emotions are elicited when one has not
found a solution to a problem or one is not making satisfactory progress towards a goal,
and the emotion draws attention to the task of finding a solution. Conversely, positive
emotions are elicited when one has found a solution or is making satisfactory progress
towards a goal, and the emotion keeps attention focused on the adaptive course. For
instance, courtship is emotionally painful when unrequited, and attention is directed to
solving the problem of successfully wooing the desired partner. However, positive emotion
is elicited when the partner responds positively to the courtship, and attention and behavior
stays focused on the same course, at least until progress towards the mating goal becomes
unsatisfactory. Thus, the valence of emotion reflects whether or not progress towards a goal
or a solution is being frustrated (Carver et al., 1996; Carver and Scheier, 1990).

There are many different negative emotions—e.g., anger, anxiety, disgust, fear,
jealousy—and they presumably evolved to influence attention in different ways. In this
paper, we focus on the attentional function of depression or depressed affect, which is an
emotion characterized by negative affect and low arousal.

Although clinical depression is often assumed to be qualitatively different than
subclinical forms, explicit tests of this assumption have found that depressed affect is better
characterized by a single dimension that varies continuously in intensity and duration
(Aggen, Neale, and Kendler, 2005; Krueger and Markon, 2006). For instance, depressive
symptoms vary continuously in epidemiological samples (Hankin, Fraley, Lahey, and
Waldman, 2005), and the degree of psychosocial impairment covaries linearly with the
number of depressive symptoms (Kessler, Zhao, Blazer, and Swartz, 1997; Sakashita,
Slade, and Andrews, 2007). We therefore use the terms depressed affect and depression to
refer to a single continuum that varies from transient sadness to chronic, severe, clinical
depression.

There is abundant evidence that depression influences attention. People with
clinical or subclinical depression tend to report persistent ruminations about important
problems in their lives (Lyubomirsky, Tucker, Caldwell, and Berg, 1999). Indeed, people
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The functional design of depression’s influence on attention
with greater levels of depression tend to ruminate more and are less easily distracted from
their ruminations (Just and Alloy, 1997; Lam, Smith, Checkley, Rijsdijk, and Sham, 2003;
Nolen-Hoeksema and Morrow, 1991; Schmaling, Dimidjian, Katon, and Sullivan, 2002).
Attention is a limited resource (Kahneman, 1973), with one implication being that as
attention becomes more focused on one problem, fewer cognitive resources are available
for other problems. Further evidence of depression’s influence on attention thus comes
from the fact that depressives’ ruminations interfere with their ability to concentrate on
other things. For instance, when people come into a psychological testing situation with
clinical or subclinical depression, their ruminations interfere with their ability to focus on
cognitive tasks and reduce their performance (Lyubomirsky, Kasri, and Zehm, 2003;
Watkins and Brown, 2002; Watkins and Teasdale, 2001; Watkins, Teasdale, and Williams,
2000). Such research suggests that depression focuses attention on the problems that caused
the episode, so much so that it interferes with people’s ability to focus on other things. Put
another way, one of depression’s effects is to focus attention and reduce distractibility.

Depressives’ focused attentional state can affect how they process information.
Research on pre-existing and experimentally induced mood indicates that depressed affect
promotes an analytical processing style (Ambady and Gray, 2002; Au, Chan, Wang, and
Vertinsky, 2003; Bless, Bohner, Schwarz, and Strack, 1990; Bless, Mackie, and Schwarz,
1992; Braverman, 2005; Edwards and Weary, 1993; Forgas, 1998; Gasper, 2004; Gasper
and Clore, 2002; Hertel, Neuhof, Theuer, and Kerr, 2000; Schwarz and Bless, 1991;
Semmler and Brewer, 2002; Sinclair, 1988; Sinclair and Mark, 1995; Storbeck and Clore,
2005; Yost and Weary, 1996). Analytical reasoning involves dividing a complex problem
into smaller, more manageable components, where each is studied in turn. To arrive at the
solution to the whole, the solution to each component must be maintained in memory while
processing on the next component takes place. Analytical reasoning therefore requires the
use of working memory, which holds information in a highly active state because it is
crucial to ongoing processing (Baddeley, 1996).

The Raven’s Advanced Progressive Matrices (RAPM) is considered one of the best
measures of nonverbal analytical reasoning ability (Carroll, 1993). Each item is a spatial
pattern completion task in which one of eight choices correctly completes a two-
dimensional visual array, and test items become progressively more difficult. The difficulty
of test items increases, in part, because the number of elements in the array increases and
the rules for how they vary across the array can be different for each element (Carpenter,
Just, and Shell, 1990). The rule for each element must be ascertained independently, so
once subjects figure out the rule for how one element varies across the array, they must
keep the solution in their working memory while they figure out the rules for the remaining
elements. The number of elements that must be analyzed and held in working memory
varies from 1 to 5, and the proportion of people getting a test item correct is negatively
related to the number of elements that must be analyzed (Carpenter et al., 1990).

Current research indicates that analytical tasks with high working memory loads,
such as the RAPM, are attentionally demanding because they leave little room for attention
to wander (Kane and Engle, 2002). For instance, performance on the RAPM is highly
correlated with the ability to resist distractions under attentionally demanding conditions,
and the relationship is mediated by differential activity in areas of the brain known to be
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The functional design of depression’s influence on attention
involved in attentional control (Gray, Chabris, and Braver, 2003).

In summary, depressed affect focuses attention on problems, and it promotes an
analytical processing style. Because analytical reasoning requires focused attention, it
seems reasonable to hypothesize that depressed affect may promote an analytical
processing style by its attention-focusing effects.

We suggest that depressed affect evolved as a response to important, analytically
challenging problems that could only be solved, if they could be solved at all, with an
attentional state that was highly focused for a sustained period of time (Watson and
Andrews, 2002). Under this hypothesis, depressed affect promotes analysis and problem-
solving by focusing attention on the problem and reducing distractibility.

If depressed affect is a response to analytically challenging problems, then a task
such as the RAPM should be able to induce depressed affect in people with low levels of
depression. Established methods for inducing depressed mood involve having subjects
listen to sad music or watch sad movies, giving them negative feedback about their
performance on tasks, having them apply self-referent statements to themselves (e.g., “I
feel a little down today”, “I wish I could be myself, but nobody likes me when I am”)
(Seibert and Ellis, 1991), and so on (Westermann, Spies, Stahl, and Hesse, 1996). There is
also substantial evidence that stressful life events can induce depression (Kendler,
Karkowski, and Prescott, 1999). While cognitively effortful tasks are often used in methods
that rely on negative feedback, the feedback is almost always fixed (i.e., even people who
perform well on the task are given negative feedback) (Westermann et al., 1996).
Moreover, it is failure itself, and not the nature of the task, that is assumed to elicit
depressed affect. Our prediction that an analytically and attentionally challenging task can
induce depressed affect, and not failure per se, is, to our knowledge, novel and untested.

There are two potential control-process mechanisms by which depressed affect
could focus attention and reduce distractibility. First, depressed affect may keep attention
focused on a problem in a way that is similar to the force exerted by a spring. In this
analogy, the problem could be thought of as being attached to one end of the spring and
attention to the other end. When the spring is compressed and relaxed, attention is focused
on the problem, and the force exerted by the spring is minimized. When the spring is
pulled, attention is pulled away from the problem, and the spring’s force increases. If
depression’s mode of action is like the force exerted by a spring, then depressed affect
should increase as attention is pulled from a focal problem, which would tend to draw
attention back to the problem. Validation of the spring metaphor would suggest that, at the
time of measurement, depressed affect is a marker of the degree to which attention is
diverted from the problem that elicited the episode.
Alternatively, depression’s influence on attention could be more like the attractive
force on a steel ball produced by a magnet. In this analogy, the “magnet” is a difficult
problem (e.g., marital troubles). The attractive force that the problem generates is depressed
affect, and it draws attention to the problem just as the magnetic force draws a steel ball to
the magnet. Since the magnetic force is greatest when the steel ball is closest to the magnet,
depressed affect should be greatest when attention is fully focused on the problem, where it
tends to keep attention focused. When attention is diverted to some other problem,
depressed affect will decrease, just as the attractive force on the steel ball lessens as it is
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The functional design of depression’s influence on attention
pulled from the magnet. Validation of the magnet metaphor would therefore suggest that, at
the time of measurement, depressed affect is a marker of the degree to which attention is
focused and distractibility is reduced.
We stress that the terms magnet and spring are merely metaphors to describe the
possible ways depressed affect could exert force on attention. However, we use them
because they help describe the different mechanisms of action that we are hypothesizing.
According to the hypothesis, depressed affect is a response to analytically and
attentionally demanding problems that may take a long time to solve. Consequently, the
organism might occasionally need to interrupt processing to deal with pressing issues that
require immediate attention (e.g., predators, important social problems). After the issue has
been dealt with, attention must return to the core problem that caused the depressive
episode. Since attention must be pulled from the core problem to be focused on the pressing
issue, processing the pressing problem would be very difficult with a spring-like
mechanism because a great deal of force must be expended to keep attention focused there.
However, under a magnet-like mechanism, once attention was pulled away from the core
problem and focused on the pressing problem, less force would be needed to keep it there.
Thus, a magnet-like mechanism would be better from an engineering perspective.

To test between these two mechanisms, we measured subjects’ level of depressed
affect twice. The first assessment (T1) was a baseline measure to assess the level of
depression that they brought with them into the laboratory. Since depressed affect is
continuously distributed in populations, people come into a psychological testing situation
with varying levels of depressed affect unless pre-screening takes place. The causes of their
depressive symptoms are assumed to reflect important pre-existing life issues, and we refer
to this as their pre-existing depression.

Subjects completed the second assessment (T2) of depressed affect after they had
been given intervention—in this case, practice questions from the RAPM. The hypothesis
that depressed affect arises in response to an analytically and attentionally challenging
problem predicts that subjects with low levels of pre-existing depression should experience
an increase after exposure to the intervention. We were concerned that after the subjects
had completed the attentionally demanding intervention, their attention would immediately
relax and we would be unable to detect the emotional effect we were looking for when they
took the T2 measure. So we devised the intervention’s effect to be prolonged.

The intervention was also designed to get subjects with high levels of pre-existing
depression to pull their attention away from their pre-existing problems. The spring and
magnet mechanisms make different predictions about how they will respond emotionally to
the intervention. According to the spring metaphor, this is like pulling a spring, and
depressed affect should increase. Under the magnet metaphor, however, the intervention is
like pulling a steel ball away from a magnet. This should cause the level of depressed affect
to decrease just as the magnetic force exerted on the ball decreases.
Materials and Methods
Participants
The 115 participants were University of New Mexico students recruited from
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The functional design of depression’s influence on attention
psychology courses and participated in exchange for extra credit. The intervention group
had 65 participants (68% females, SD=.47, average age=21.9, SD=4.4), whereas the control
group had 50 participants (88% females, SD=.33, average age=25.4, SD=10.2). One person
in the control group did not provide information about their sex or age.

Instruments

Scale for assessing depressed affect. Since our two mood-state measures were to be
completed within a few minutes of each other, we were concerned that subjects might
remember their T1 answers when filling out the scales at T2. Moreover, we wanted to be
able to detect subtle changes in affect. No existing instruments were adequate for these
purposes.
To accomplish these goals, we constructed two parallel instruments (forms A and
B) from a pool of 26 adjectives designed to assess state depression. The pool was
composed of 16 negative and 10 positive affect adjectives, with each adjective having one
synonym (i.e., there were 13 sets of synonyms). From each paired synonym set, one
adjective was assigned to each scale so that there were 13 adjectives on each form (e.g.,
“sad” was on form A and “blue” was the form B synonym). Each adjective was rated on a
9-point Likert scale according to how one was feeling right then (1=extremely inaccurate
as a self-description, 9=extremely accurate as a self-description). The construction of two
different instruments that were roughly equivalent allowed us to reduce memory effects,
and the use of multiple adjectives that were rated on Likert scales (as opposed to checklists)
allowed us to detect subtle changes in affect.
To test for equivalence, we tested the factor structure of the forms on the control
group. All subjects took both forms, counterbalanced for order. We used Mx (Neale, Boker,
Xie, and Maes, 2003) to perform a series of latent variable analyses using structural
equation modeling (SEM). In SEM, variables are connected by a series of arrows that
represent the presumed direction of causation. The likelihood is the probability of obtaining
the observed data under the assumptions of the model (e.g., a multivariate normal
distribution), and it is influenced by the unknown parameters in the model (e.g., the
regression coefficients of the variables connected by arrows). Mx searches through the
parameter space for the regression coefficients that maximize the likelihood. The fit of the
model is -2 times the natural logarithm of the likelihood (-2LL). For our latent variable
models, the latent measure of state depression is assumed to influence the observed
measure for each adjective, and Mx uses the variance that the observed measures share in
common to estimate the regression coefficients to the latent factor. To our knowledge, this
is the first attempt to use maximum likelihood estimation techniques to test the equivalence
of two instruments for assessing affective states.
With maximum likelihood, significance testing is done by calculating the difference
in the fits between nested models, -2?LL, which is asymptotically distributed as chi-square.
(One model is “nested” inside another if the parameters to be estimated in the former are a
subset of the parameters of the latter.) A common significance test is to compare the fit
between a model and a submodel in which a parameter is dropped from the structural
equation path or constrained in its value. For instance, in a latent factor model in which two
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The functional design of depression’s influence on attention
parameters are constrained to have equal loadings onto the latent factor, an insignificant
increase in fit is evidence that the parameters do not have significantly different loadings.
We first tested whether the 26 items were better described by one or two latent
factors. The two-factor model fit significantly better (negative affect items loaded high on
the first factor and positive affect items loaded high on the second factor), -2?LL=374.44,
?df=27, p<.0001. We retained all the negative affect items from the first factor because
they appeared to be more closely related to depressed affect (e.g., sad, cheerless, somber,
lonely). This reduced the forms from 13 items each to eight each. Then, we conducted eight
tests (one for each synonym pair), in which we tested whether the items in the pair had
significantly different loadings on the latent factor. Based on these tests, we deleted two
more pairs. The remaining six synonym pairs passed a strict test of factorial invariance in
which each item and its synonym were simultaneously constrained to load equally onto the
latent factor, -2?LL=10.76, ?df =6, p=n.s. (see Table 1).
We also gave subjects in both groups the Beck Depression Inventory, which is a
commonly used instrument for assessing depressed affect over the past two weeks (Beck,
Ward, Mendelson, Mock, and Erbaugh, 1961). It is not state-like enough for our purposes,
and so we only used it to validate our constructed scales.

Table 1. The forms for assessing depressed affect.

Form A
Form B
Depression Items
Lonely (1)
.
Somber (2)
.
Miserable (3)*
.
Sad (4)
.
Downhearted (5) .
Cheerless (6)
.
. Alone
(1)
. Grim
(2)
. Awful
(3)*
. Blue
(4)
. Crestfallen
(5)
. Downcast
(6)

Items with the same number were synonyms that had equivalent factor loadings in the control group.
Items with an asterisk (*) were eliminated from the analyses because the intervention influenced their
loadings onto the latent measure (see text).

Raven’s Advanced Progressive Matrices. We gave subjects in the intervention group
questions from the RAPM, which was described above. The full RAPM is considered one
of the best measures of nonverbal analytical reasoning ability and fluid intelligence with an
internal consistency reliability of about 0.90 and a validity of about 0.80 in measuring
general intelligence (Carroll, 1993; Raven, Court, and Raven, 1994). The 12-item short
form correlates 0.90 with the full 36-item RAPM (Arthur and Day, 1994).
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Procedure

The protocols were completed in classroom settings. For the control protocol, each
participant first read the instructions for either form A or form B (counterbalanced for
order) for the T1 assessment of affect and then completed it. After completing the first
form, they then read the instructions for the T2 assessment of affect and then completed it.
Consequently, the time between the two measures was short. Subjects were also given the
BDI and answered a short background questionnaire. After completing the protocol, the
subjects were debriefed and thanked.
A key difference in the intervention protocol is that there was an intervention
between the T1 and T2 measures of affect (Figure 1). At T1, subjects were also given the
BDI, after which they were given the intervention. We were concerned that after the
subjects had completed the attentionally demanding intervention, their attention would
immediately relax and we would be unable to detect the emotional effect we were looking
for when they took the T2 measure. So we devised an intervention that was intended to
promote a prolonged focusing effect. Specifically, participants read that they were about to
take a test, which involved questions that got progressively more difficult. They also read
that they would first be given some practice questions to familiarize them with the rules of
the test and give them some idea of the difficulty they would encounter in the test. Subjects
were then given five practice questions taken from the remaining 24 questions from the
RAPM that were not used in the short form. One easy question was given to familiarize
participants with the rules of the test, and the other four had high working memory loads to
help them understand the difficult nature of the test they would be taking. After they had
answered each practice question, participants were given the correct answer and told to
analyze the question until they had satisfied themselves that they knew why it was the
correct answer. This feature was deemed necessary because, without knowing the correct
answer, subjects might not have understood that the questions were difficult. The use of
analytically challenging questions for the intervention should have helped subjects focus
their attention, and the fact that they were practice questions should have helped subjects
remain in the focused state so that they were psychologically and emotionally prepared for
taking the test. Thus, the intervention was designed to prolong the focusing effect so we
could measure affect after subjects had completed the intervention. After the intervention,
participants completed the T2 assessment of affect. Then, subjects completed the short-
form of the RAPM, which was administered under a 15-minute time limit. Finally, subjects
filled out a short background questionnaire, and then were debriefed and thanked.

Figure 1. Time-line for the intervention group.
T1 (assessment of
Intervention
T2 (assessment of
RAPM short form
depressed affect)
(practice questions)
depressed affect)
Time

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Results
The latent depressed affect constructs

The intervention could have influenced the measurement properties of the state
depression constructs. We ran a series of models in which we compared the fully saturated
model to one in which a particular synonym pair was constrained to have equal loadings
across forms and times. Doing this for each of the six pairs, we found that one pair
(miserable-awful) had a significantly worse fit across the two times, -2?LL=18.973, ?df=3,
p<0.0005, so we deleted it from our constructs. The remaining five pairs passed a test of
factorial invariance in which each item and its synonym was simultaneously constrained to
load equally onto the latent factor across T1 and T2, -2?LL=13.049, ?df=15, p=n.s.
From the remaining adjectives, we used Mx to estimate factor scores for the latent
T1 and T2 measures of state depression and then imported them into SAS. Both variables
exhibited good spread, had roughly bell-shaped distributions and passed Shapiro-Wilk tests
of normality. We therefore had no evidence that our population was emotionally unusual.
To test the validity of our instruments, we explored the relationship between the T1
measures of depressed affect of both forms, which are state measures of pre-existing
depression, with the BDI, which is a more trait-like measure of pre-existing depression.
Across both the control and intervention groups, the baseline (T1) score on form A was
significantly correlated with the BDI, r(61)=0.56, p<0.001. The baseline (T1) score on
form B was also significantly correlated with the BDI, r(54)=0.73, p<0.001. Despite being
state measures of depressed affect, both forms were moderately good predictors of the BDI,
which supports their validity as measures of depressed affect.

The effects of age, sex, and order

Age was not significantly correlated with the T1 depression score, the T2
depression score, or the RAPM score in either the control group or the intervention group.
These variables also were not affected by the sex of subjects or the order in which they
took the two forms.

The baseline measure of depressed affect at T1 in the control and intervention groups

The control group’s mean level of pre-existing depression at T1 was 0.29, SD=1.03
(range=-1.77 to 3.01), whereas the mean T1 score for the intervention group was -0.04,
SD=0.86 (range=-1.97 to 1.58). The control group was marginally more depressed, t=1.90,
df=113, p=0.06. When an outlier in the control group was removed, the groups were not
significantly different from each other in their baseline level of depression, t=1.64, df=112,
p>0.10. Put simply, save for the outlier in the control group, the groups were similar in
their baseline level of depression. All subsequent results that we report include the outlier,
but they do not change substantively if the outlier is excluded.
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The change in depressed affect from T1 to T2

We predicted that the analytically challenging intervention would elicit depressed
affect in subjects with low levels of pre-existing depression. We divided the control and
intervention groups into three approximately equally sized subgroups, based on their T1
depression. Consistent with our prediction, intervention subjects in the low pre-existing
depression group tended to increase in depression at T2, mean change=+0.12, SD=0.22,
p=0.02, whereas control subjects with low pre-existing depression tended to decrease at T2,
mean change=-0.03, SD=0.03, p<0.001 (see Figure 2).

Figure 2. The white bars represent the control groups, whereas the gray bars represent the
intervention groups. The error bars represent twice the standard error of the mean.
0.3
0.2
0.1
sed Affect (T2-T1)
0.0
-0.1
-0.2
Mean Change in Depres
Low
Medium
High
Depressed Affect at T1

The spring and magnet metaphors made different predictions about how subjects
with high levels of pre-existing depression would respond emotionally to the intervention.
Consistent with the magnet metaphor, subjects with high levels of pre-existing depression
showed a significant decrease in depressed affect after exposure to the intervention, mean
change=-0.14, SD=0.14, p<0.001, whereas those in the control group showed a slight, but
significant increase, mean change=+0.06, SD=0.04, p<0.001.
We get qualitatively similar results if pre-existing depression is treated as a
continuous variable. Graphically, the depressive response (T2-T1) is positively related to
baseline depression at T1 in the control group and negatively related in the intervention
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