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Several classic studies have concluded that the accuracy of identifying uncontrollable situations depends heavily on depressive mood. Nondepressed participants tend to exhibit an optimistic illusion of control, whereas depressed participants tend to better detect a lack of control. Recently, we suggested that the different activity levels (measured as the probability of responding during a contingency learning task) exhibited by depressed and nondepressed individuals is partly responsible for this effect. The two studies presented in this paper provide further support for this mediational hypothesis, in which mood is the distal cause of the illusion of control operating through activity level, the proximal cause. In Study 1, the probability of responding, P(R), was found to be a mediator variable between the depressive symptoms and the judgments of control. In Study 2, we intervened directly on the mediator variable: The P(R) for both depressed and nondepressed participants was manipulated through instructions. Our results confirm that P(R) manipulation produced differences in the participants' perceptions of uncontrollability. Importantly, the intervention on the mediator variable cancelled the effect of the distal cause; the participants' judgments of control were no longer mood dependent when the P(R) was manipulated. This result supports the hypothesis that the so-called depressive realism effect is actually mediated by the probability of responding.
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Mediating Role of Activity Level in the Depressive
Realism Effect
Fernando Blanco*, Helena Matute, Miguel A. Vadillo
Departamento de Fundamentos y Me
´todos de la Psicologı
´a, University of Deusto, Bilbao, Spain
Abstract
Several classic studies have concluded that the accuracy of identifying uncontrollable situations depends heavily on
depressive mood. Nondepressed participants tend to exhibit an optimistic illusion of control, whereas depressed
participants tend to better detect a lack of control. Recently, we suggested that the different activity levels (measured as the
probability of responding during a contingency learning task) exhibited by depressed and nondepressed individuals is
partly responsible for this effect. The two studies presented in this paper provide further support for this mediational
hypothesis, in which mood is the distal cause of the illusion of control operating through activity level, the proximal cause.
In Study 1, the probability of responding, P(R), was found to be a mediator variable between the depressive symptoms and
the judgments of control. In Study 2, we intervened directly on the mediator variable: The P(R) for both depressed and
nondepressed participants was manipulated through instructions. Our results confirm that P(R) manipulation produced
differences in the participants’ perceptions of uncontrollability. Importantly, the intervention on the mediator variable
cancelled the effect of the distal cause; the participants’ judgments of control were no longer mood dependent when the
P(R) was manipulated. This result supports the hypothesis that the so-called depressive realism effect is actually mediated
by the probability of responding.
Citation: Blanco F, Matute H, Vadillo MA (2012) Mediating Role of Activity Level in the Depressive Realism Effect. PLoS ONE 7(9): e46203. doi:10.1371/
journal.pone.0046203
Editor: Lin Lu, Peking University, China
Received May 28, 2012; Accepted August 28, 2012; Published September 27, 2012
Copyright: ß2012 Blanco et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Support for this research was provided by the Direccio
´n General de Investigacio
´n of the Spanish Government (Grant PSI2011-26965) and the
Departamento de Educacio
´n, Universidades e Investigacio
´n of the Basque Government (Grant PI2012-56). The funders had no role in study design, data collection
and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: fblanco81@gmail.com
Introduction
Far from being optimal reasoners, people are known to exhibit
certain cognitive biases or systematic errors when performing
certain tasks. One of the most extensively studied cognitive biases
is the illusion of control, which consists of a belief that one is able
to exert control over outcomes that are actually uncontrollable.
This effect occurs mainly when the outcomes are of appetitive
valence and occur frequently [1–3].
A very influential paper by Alloy and Abramson [1] showed that
the illusion of control was stronger in nondepressed participants.
In that study, both nondepressed and mildly depressed students
were allowed to decide during each trial whether to press a button
to turn on a light bulb. The light bulb’s onset was actually
independent of the pressing of the button (i.e., it was uncontrol-
lable). During a training phase, the participants were able to press
the button as often as they wanted, and the light came on during
some of the trials but not others. Afterward, the participants were
asked about the degree of control they had exerted over the light
onset; that is, they were asked about the perceived contingency
between their responses and the outcomes. Interestingly, Alloy and
Abramson [1] found that mildly depressed participants were better
at detecting the absence of control over the light than
nondepressed participants were; nondepressed participants sys-
tematically overestimated the contingency between their actions
and the uncontrollable outcome, thereby exhibiting an illusion of
control [4–6]. Alloy and Abramson’s finding soon became known
as ‘‘depressive realism’’ [7] and has been replicated several times
using different tasks and procedures [8–12].
A variety of hypotheses have been proposed to account for this
puzzling finding (see a review by Ackerman & DeRubeis [13], and
recent proposals by different authors [9–11,14]). An examination
of the published literature leads to the impression that the
presumed realism of depressed people may be a complex,
multicausal phenomenon that involves many variables and
demands further research.
One variable that has been shown to be a source of bias in
contingency judgments is the activity level of the participants, as
determined by the response frequency [6,15,16]. Participants who
take an active approach to this type of experiment become
exposed to frequent, yet accidental, pairings between their
responses and the uncontrollable outcome. These co-occurrences
lead them to believe that they are indeed causing the outcome; as a
result, they develop an illusion of control.
Inspired by this rationale, a recent study by Blanco et al. [9]
suggested that the passivity associated with depressive symptoms
does, to some extent, contribute to realism in detecting the
uncontrollability in the standard contingency-learning tasks
normally used in studies of depressive realism [1]. According to
Blanco et al. [9], nondepressed participants tend to spontaneously
respond more frequently during the training phase. This means
that in a typical contingency learning task, nondepressed
participants decide to press the button on more trials than do
depressed participants, who very often take a more passive
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approach to the task [17]. This passive strategy leads to few
accidental co-occurrences between the response and the outcome
and thus might prevent the development of an illusion of control.
In line with these predictions, Blanco et al. showed that mildly
depressed participants were both more passive and more realistic
than nondepressed participants in an experimental task involving
uncontrollable outcomes, similar to that of Alloy and Abramson
[1]. In addition, the authors also reported that passivity was
correlated with accuracy in detecting the absence of control. The
latter result converges with Matute’s [6] findings that the more
active participants develop stronger illusions of control, although
Matute did not include a measure of depressive symptoms in her
study. As a conclusion, Blanco et al. [9] suggested that the
depressive realism effect could partially be due to the passivity that
depressive participants exhibit in a variety of experimental settings.
Thus, depressive symptoms would be the distal cause of the
realistic detection of uncontrollability via a more proximal cause,
depressive passivity, defined as a lower probability of responding
during a contingency learning task.
Nonetheless, the claim that passivity associated with depressive
symptoms plays a relevant causal role in the depressive realism
effect needs to be supported by careful data analysis and, more
importantly, by evidence from experimental designs in which the
appropriate manipulations are carried out. Thus, our current aim
is to complement the work started by Blanco et al. [9] by using two
strategies, statistical and experimental. First, we conducted an
exact replication of Blanco et al.’s study [9], but we included a
proper mediational analysis. This statistical technique is intended
to test the hypothesis by isolating the direct effect of depressive
symptoms on control judgments while partialling out the effect of
the probability of responding, P(R). In the second study, we
conducted an experimental manipulation to further test the
mediational hypothesis. Specifically, we intervened directly on
the mediator variable, P(R). Because the hypothesized proximal
cause of the illusion, P(R), was manipulated externally, if the
mediational hypothesis were true, we would observe that the
hypothesized distal cause (i.e., depressive symptoms) and the
outcome variable (i.e., control judgments) become mutually
independent. In other words, we expect that the depressive
realism effect should disappear in our experimental study when we
directly intervene on the mediator between depressive symptoms
and control judgments. The use of this intervention strategy to
reveal causal links is described in the causal reasoning literature
[18,19] as based on the Markov condition for Bayesian networks,
which states that a variable is probabilistically independent of any
other variable (except for its descendants) conditional on its
parents. In our case, if the mediational model is true, intervening
on the parent of the outcome variable (i.e., its proximal cause) will
render the outcome variable and the distal cause independent
from each other.
Study 1
Study 1 was an exact replication of Blanco et al. [9]. Therefore,
we expect that participants who are more depressed would show
more passive behaviors (i.e., fewer responses) during the training
phase and would exhibit more accurate and realistic control
judgments. This prediction stems from previous findings and from
our interpretation of the depressive realism effect as a consequence
of the lower probability of responding. Importantly, Study 1
features a more appropriate statistical procedure than the one that
Blanco et al. [9] used. Specifically, in the current study we make
use of a mediation analysis aimed at testing whether the
probability of responding is a significant mediator between
depressive symptoms and control judgments.
Methods
Participants and apparatus. Fifty Psychology students at
the University of Deusto agreed to participate in the study as an
optional and voluntary activity in their course. The students were
not asked to give their name or any other personal information,
and were given neither course credit nor monetary compensation
for this activity. Data from one participant were excluded because
he/she did not respond on any trial, thus making it impossible to
judge whether he/she was paying attention. The study took place
during a collective session in a large computer room, and was
carried out by a teaching assistant different from the course
provider.
The study used the Flashes task, programmed in JavaScript.
Previous studies have demonstrated that this task is particularly
suitable for assessing the illusion of control [20] and the depressive
realism effect [9] both on the Internet and in the laboratory.
Ethics statement. In Study 1 and Study 2, the behavioral
and verbal data generated by the participants were sent
anonymously to the experimenters’ e-mail address through the
Internet browser. In agreement with the ethical standards for
human research on the Internet [21,22], the participants were
informed before the session that in no case they would be asked to
provide any personal information (e.g., name, phone, address, e-
mail), that their data would not be identifiable, and that they
would be allowed to terminate the study by closing the Internet
browser window at any moment without penalty, if they wished so.
In addition, right after the study finished, a screen requested the
participants to give permission to use the data they had just
generated. The participants agreed to grant this permission by
clicking a button labeled ‘‘Send data’’, which immediately
submitted the data anonymously to the experimenters’ database.
Those participants not willing to send the information after the
experiment were instructed to click a button labeled ‘‘Finish’’,
which immediately deleted the data. Finally, we did not use
cookies, keyloggers or any other software to covertly obtain
information from the participants.
The funding agency for this research (i.e., Spanish Ministry of
Science and Innovation) evaluated the scientific and practical
implications of the procedure used in these studies prior to the
approval of the research grant, and did not request any extra
formal evaluation from an ethical review board (neither before the
allocation of funds nor in subsequent renewals). This was in
agreement with the code of ethics of the Spanish Psychological
Association [23] (Chapter IV, Articles 33 to 38), which does not
state the compulsory nature of formal ethical approval before
carrying out this type of innocuous psychological research. In our
procedure, the data that the participants provided were anony-
mous and unidentifiable, the stimuli and materials were harmless
and emotionally neutral, the goal of the study was transparent, and
the task involved no deception.
Procedure. The procedure was identical to the one used by
Blanco et al. [9]. A computerized version of Beck’s Depression
Inventory (BDI [24]; Spanish adaptation by Conde & Useros [25])
was administered prior to the study, in order to assess the
participants’ depressive symptoms. Rather than establishing an
arbitrary cutoff point to split the sample into mildly depressed and
nondepressed participants, as has usually been done in previous
studies [1,9–11], the direct BDI score was taken as a continuous
variable. Acknowledging the actual nature of the variable, which is
indeed continuous, permits a less biased and more powerful
approach to data analysis (for a more elaborated argument on this
Activity Level and Depressive Realism
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methodological issue, see Cohen [26]). Using BDI scores as a
continuous dimension, rather than as a categorical one, raises a
terminology-related issue that must be addressed. Given that our
sample is not clinical, the term ‘‘depressed’’ seems inadequate.
However, once we acknowledge that our measure of depressive
symptoms is continuous in nature, it seems reasonable to use the
term ‘‘depressed’’ not as a clinical label, but as an indicator of a
given participant’s or group’s higher BDI scores relative to others
in the sample. Therefore, one may say that one participant is
‘‘more depressed’’ than other, while none of them is clinically
depressed. Thus, through the manuscript, we use the terms
‘‘depressed’’ and ‘‘nondepressed’’ not as indicators of a clinical
diagnosis, but as a indicators of relative individual or group
differences in the measure of the depressive symptoms.
After recording the BDI score, the program displayed written
instructions for the task. The participants were told that a flash
would appear on the screen from time to time and last for 1
second. Then, the screen would fade to black. As the instructions
described, the participants’ goal was to make the flash appear
again whenever it stopped by pressing the space bar on the
keyboard. That is, the termination of the flash signaled the
opportunity to respond to make it appear again. After the
instructions, a series of fifty flashes with a fixed duration of 1
second was presented, interspersed with fifty black-screen inter-
vals. The lengths of these black intervals were programmed to be
either short (1 second) or long (5 seconds) according to a
prescheduled randomized sequence. This means that, on a given
trial during which the participant had responded (by pressing the
space bar) right after the previous flash stopped, the flash could
follow either immediately (i.e., with a short, 1-second interval),
thus leading to an accidental concurrence between the response
and the flash, or after a delay (i.e., a long, 5-second interval). The
instructions specified that the flashes would appear after a short
interval of 1 second whenever the participant succeeded at
producing them. They also specified that the flashes would appear
after a delay of 5 seconds whenever the participant failed. In other
words, the trials in which the flash appears after a short 1-second
interval are given reinforcing value through instructions, whereas
the trials in which the flash appears after a long delay (5 seconds)
are intended to serve as unreinforced trials.
Given that the sequence of flashes was prescheduled and
therefore response-independent, the onset of the flashes was
actually uncontrollable for the participants. Nevertheless, acciden-
tal concurrences between response and flash were expected. These
concurrences, if frequent, could lead to the impression that flashes
were under the participants’ control. The fact that 38 out of 50
trials (76%) included a short black interval and only 12 trials (24%)
included a long black interval (i.e., 76% of trials were potentially
reinforced) was aimed at favoring the illusion of control, based on
previous studies showing that a high probability of reinforcement
produces stronger illusions [1,2]. The predefined sequence of trials
was identical to that used by Blanco et al. [9] to render a fair
comparison of the two studies.
The activity level of every participant was measured by
computing the probability of responding, P(R), during the training
phase. This value results from dividing the number of trials in
which a response (a key press) was detected by the total number of
trials (50), hence the variable ranges between 0 and 1.
Finally, the participants’ perceived control over the flashes was
assessed via a control judgment after the training phase. The test
question, translated from the original in Spanish, read as follows:
‘‘To what extent do you think that the onset of the flashes
depended on what you did?’’ The answers were given by clicking
on a scale ranging from 0 (‘‘Absolutely not’’) to 100 (‘‘Absolutely’’).
Given that the flashes were presented in a preprogrammed order,
one can assume that any judgment higher than zero was biased up
to a certain extent.
Results and Discussion
Table 1 contains the descriptive statistics (mean, standard error
of the mean, range and median) for the three variables assessed:
direct BDI score, P(R), and judgment of control. Instead of
dichotomizing the continuous variables, as is usually done in this
literature, we tested a potential mediational structure by means of
the method described by Baron and Kenny [27]. In the proposed
mediational structure, the BDI scores would affect the control
judgments (i.e., produce the depressive realism effect) via the
intermediate variable, as shown in Figure 1B. Following Baron
and Kenny’s procedure, we first assessed the direct effect of the
model, proving that BDI scores marginally predicted the
judgments of control, b=2.27, t(48) = 1.95, p= .057 (Path c in
Figure 1A). The negative coefficient implies that the higher the
participant’s BDI score, the lower (and hence, more realistic) his or
her control judgment was (see Figure 2). Therefore, we replicated
the depressive realism effect described by Alloy & Abramson [1].
Next, we found that BDI scores also predicted the P(R), b=2.52,
t(48) = 4.26, p,.001 (Path a in Figure 1B). The participants who
were more depressed also responded less frequently during the
task, in line with the findings of previous studies [9]. In the final
step, we conducted a multiple regression analysis with BDI scores
and P(R) as predictors and judgments as the predicted variable.
This revealed that P(R) was a reliable predictor of the judgments
even when controlling for the BDI scores, b= .40, t(47) = 2.57,
p,.05 (Path b in Figure 1B), whereas the BDI scores failed to
predict these judgments, b=2.06, t(47) = 0.40, p= .68 (the direct
effect depicted as Path c’ in Figure 1B). The Sobel test [28]
confirmed that the variance in the judgments that the BDI scores
were able to predict (i.e., the total effect) was in fact accounted for
by the indirect path via a mediator variable, P(R), z= 2.22, p,.05.
Once the effect that the proximal cause, P(R), had on judgments
was partialed out, there was little variance left in the judgments
that could be predicted directly by the distal cause (namely, the
BDI scores). This finding is consistent with our initial mediational
hypothesis.
Table 1. Descriptive statistics.
BDI scores Judgments P(R)
Study 1 (N= 49) Mean 6.92 37.45 .75
SEM 0.88 3.82 .03
Range 0–25 0–85 .12–1
Median 6 45 .88
Study 2: Analytic
instructions (N = 95)
Mean 9.33 26.05 .48
SEM 0.82 3.24 .03
Range 0–36 0–100 .02–1
Median 7 15 .48
Study 2: Naturalistic
instructions (N = 103)
Mean 9.60 30.53 .65
SEM 0.84 2.91 .02
Range 0–40 0–100 .04–1
Median 7 20 .70
doi:10.1371/journal.pone.0046203.t001
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Study 2
Blanco et al. [9] and (more clearly) its exact replication in Study
1 of the current report both suggested that a causal chain model
links depressive symptoms (as assessed by the BDI) to judgments of
control via a mediator variable (the activity level), as shown in
Figure 1B. To further investigate the mediation of the P(R)
between the BDI scores and the judgments, we conducted Study 2.
Here, we intervened by manipulating the P(R) via instructions,
independent of depressive symptoms (as shown in Figure 3). A
direct manipulation of the P(R) breaks the causal chain because it
prevents mood from affecting P(R). Consequently, in Study 2, we
should find that the P(R), and not the distal cause (BDI scores), is
the variable that best predicts judgments of control.
This strategy aims to reveal causal links by means of
interventions, and is grounded in the causal reasoning literature
[18,19], which typically embeds the variables into causal networks
similar to those depicted in Figures 1 and 3. Under the
mediational hypothesis, when an intervention is performed on a
mediator variable the effect of the distal cause (called the ‘‘parent
variable’’ in the causal networks literature) on subsequent elements
(the ‘‘descendant variables’’) of the chain is cancelled due to the
Markov condition. Therefore, this manipulation serves as a test of
the mediational hypothesis of depressive realism.
In addition, the choice of P(R) as the independent variable in
Study 2 allows for disambiguation between our mediational
hypothesis and an alternative causal model that may also be at the
basis of the depressive realism effect. According to a common-
cause model, both the P(R) and the control judgments would be
directly affected by depressive symptoms. Crucially, the manipu-
lation of the P(R) under such an alternative hypothesis would not
eliminate the depressive realism effect because the intervention
would be performed on a terminal node (i.e., a variable without
descendants), and the link between BDI scores and judgments
Figure 1. Mediational model tested in Study 1. The letters a, b, c,
and c’ depict the paths between the three variables, which are
weighted by the standardized regression coefficients. One or two
asterisks indicate a significant coefficient (p,.05 or p,.01, respectively),
while n.s. means that the coefficient failed to reach the significance
level (p..05). Panel (A): Path c corresponds to the predictive link
between BDI scores and the judgments (i.e., the total effect in the
model). The negative coefficient indicates that we replicated the
depressive realism effect. Panel (B): The total effect (Path c above) can
be decomposed into two components, the direct effect and the indirect
effect. Paths a and b represent the indirect effect that operates via the
mediator, P(R). Path c’ represents the direct effect of BDI scores on the
judgments, that is, the amount of predictive power left after the
mediational effect of P(R) has been partialed out. Overall, the
mediational analyses support a causal chain model in which BDI scores
produce depressive realism indirectly via the mediator variable, P(R).
doi:10.1371/journal.pone.0046203.g001
Figure 2. Scatterplot depicting the judgments as a function of BDI scores in Study 1. The scatterplot shows the judgments of control
(vertical axis) as a function of the direct BDI scores (horizontal axis). The line fitting the data points displays a negative slope, indicating that the
participants with higher BDI scores were more accurate in their judgments, thus replicating the depressive realism effect.
doi:10.1371/journal.pone.0046203.g002
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would remain statistically intact. That is, concerning the manip-
ulation of P(R), the predictions of the two alternative models differ
substantially.
Method
Participants and apparatus. A sample of 209 anonymous
Internet users volunteered to participate in the study through our
laboratory’s webpage (www.labpsico.deusto.es). The experimental
task was identical to that used in Study 1 and in previous
publications [9,20]. Previous studies conducted in our own
laboratory and elsewhere showed that samples collected on the
Internet are as reliable as those collected in the laboratory,
specifically college students [20]. A frequently raised concern
about Internet-based studies is the possibility of multiple
submission of the same participant. We acknowledge that it is
indeed impossible to assure that no participant in our sample took
part twice in Study 2. However, according to most studies on this
issue [29,30], multiple submission is a rather uncommon event (of
virtually negligible impact on the sample of 209 Internet users in
Study 2) [29,30]. Birnbaum [29] concluded that multiple
submission ‘‘is not a real problem’’ in Internet-based research.
Note also that, as mentioned above, we tested the reliability of this
particular procedure (i.e., the flashes task) in previous studies in
which we compared a sample of Internet users (collected in similar
conditions to Study 2) with another sample comprising college
students, and found the same results in both of them [20].
Moreover, convergent results concerning the depressive realism
effect were also found in Blanco et al. [9], with a sample of
Internet users, and in Study 1, which was conducted with college
students, despite the fact that multiple submission was only
possible through the Internet.
The participants were randomly assigned to one of two groups,
which differed only in the instructions they received before the
training phase. Eleven participants were excluded from the
analyses because they did not respond in even a single trial during
the experiment, which made the data they provided unreliable
(there is no way to determine whether they were paying attention
to the experiment). Therefore, the final sample was reduced to 198
volunteers, 103 in the Naturalistic Instructions group and 95 in the
Analytic Instructions group.
Procedure. The procedure was identical to that used in
Study 1 except for the instructions given before the training. The
participants were presented with either of two instructional sets,
the Naturalistic instructions or the Analytic instructions. The
participants in the Naturalistic instructions group were encouraged
to press the space bar frequently to make the flashes of light appear
on the screen, whereas the participants in the Analytic instructions
group were told to press the space bar in only 50% of the trials to
determine whether they could control the flashes with their
responses. The two instructional sets were based on the
instructions from Matute [6], in which the same manipulation of
the P(R) was conducted successfully.
Results and Discussion
The descriptive statistics for BDI, P(R), and judgments are
summarized in Table 1. To begin, we ensured that there were no
significant differences in the BDI scores between the two groups (F
,1), thus avoiding a potential source of sample bias. As planned,
the instructional sets produced higher P(R) levels in the
Naturalistic group than in the Analytic group, F(1, 196) = 21.15,
p,.001. This manipulation was conducted independent of the
participants’ depressive symptoms. As expected, the BDI scores
were unable to affect the P(R) in Study 2, b=2.03, t(196) = 0.48,
p= .63. The manipulation of the activity level successfully
prevented the depressive passivity that we found in Study 1. In
Figure 3, this is displayed as a crossed-out path between the BDI
scores and the P(R). As we predicted from the mediational
hypothesis, two linear regression analyses showed that BDI scores
failed to predict the judgments, both when the two groups were
collapsed, b= .01, t(196) = 0.19, p= .85, and when the instructions
were controlled as a factor in the model, b= .01, t(195) = 0.17,
p= .86 (observe the flat slopes in the scatterplot of Figure 4). Thus,
intervening on the mediator variable P(R) resulted in the
abolishment of the depressive realism effect.
In contrast, the P(R) was a good predictor of the judgments,
b= .33, t(196) = 4.86, p,.001, even when we controlled for the
instructions by entering them in the model, b= .34, t(195) = 4.75,
p,.001. Moreover, once the P(R) effect was partialed out, the
direct effect of the instructions on judgments was not significant,
b=2.03, t(195) = 0.46, p= .64. This latter result indicates that the
variance in the judgments was due to the P(R) rather than any
potential direct influence of the instructions (e.g., different
expectations directly induced by the different instruction versions).
To sum up, Study 29s results converge with the conclusions from
Study 1 by highlighting that P(R) predicts the variance in the
judgments to a greater extent than the BDI scores do. This is
consistent with the idea that P(R) is the proximal factor that
mediates between depressed symptoms and judgments of control.
Consistent with this mediational hypothesis, when we intervened
directly on the P(R), the mood measure was unable to account for
any variance in the judgments. Furthermore, the latter finding is
not predicted by the alternative hypothesis in which both P(R) and
judgments are directly dependent on the depressive symptoms.
General Discussion
The intriguing finding by Alloy and Abramson [1] opened a
fertile research field that soon spread from basic learning literature
to social and applied psychology (see reviews [13,14,31,32] and a
Figure 3. Diagram of the intervention in Study 2. The paths
represent the links between the variables of interest, weighted by the
provided standardized regression coefficients. One or two asterisks
indicate a significant coefficient (p,.05 or p,.01, respectively), while
n.s. means that the coefficient failed to reach the significance level
(p..05). In addition, crossed-out paths indicate that the two connected
variables are not correlated to each other. First, the diagram shows that
the direct intervention on the P(R) cancels out the otherwise natural
effect of mood on activity level (i.e., the depressive passivity reported in
Study 1 and in Blanco et al., 2009), as expected. Consequently, and
given the mediational structure that we described in Study 1 (see
Figure 1B), judgments are no longer predicted by BDI scores (i.e., the
intervention renders the BDI scores and the judgments mutually
independent, conditional on the mediator). This supports the media-
tional hypothesis in which the effect of mood on judgments is mainly
due to the mediation of P(R).
doi:10.1371/journal.pone.0046203.g003
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recent meta-analysis [33]). According to the numerous works
published on the topic, many different variables may modulate the
depressive realism effect, which explains why cannot always be
replicated. Some of the previously studied variables include the
valence of the outcomes [1,34], the presence of an observer [35],
the outcome frequency [1], and the length of the intertrial interval
[10,11]. Recently, Blanco et al. [9] suggested that yet another
factor contributes to the so-called depressive realism effect. As they
pointed out, depressed participants’ lack of responding during the
training phase prevents the illusion of control by reducing the
frequency of accidental concurrences between the participant’s
responses and the uncontrollable outcome. In contrast, the
nondepressed participants (who are usually more active and
respond frequently during the training phase) are prone to develop
an illusion of control via the formation of a spurious association
between the two events (see Blanco et al. [15] and Matute [6] for
demonstrations of the effect of the response frequency on the
illusions of control). Not only is the P(R) effect a robust empirical
finding, it is also consistent with the predictions of leading
associative learning theories, e.g., the Rescorla-Wagner model
[36]; see Matute, Vadillo, Blanco, and Musca [37] for computer
simulations illustrating this point).
Blanco et al. [9] showed that, in an uncontrollable task,
depressed participants were both more passive and more accurate
than nondepressed participants, while the response frequency
correlated positively with the illusion of control. In Study 1, we
conducted an exact replication of Blanco et al. [9] featuring a
mediational analysis aimed at revealing the causal structure
connecting the three variables of interest. This analysis confirmed
that higher BDI scores correlated with more accurate judgments of
control (i.e., depressive realism) and that probability of responding,
P(R), mediated the effect between depressive symptoms and
judgments. The resulting causal model can be depicted as a chain,
with BDI scores as the distal cause and the P(R) as a more
proximal cause of the variance in the judgments of control
(Figure 1B). Crucially, depressive symptoms affected the judg-
ments only via the mediator variable, P(R). In addition, while the
study conducted by Blanco et al. [9] used a sample of anonymous
Internet users, Study 1 in the current report was conducted with
college students, and both yielded similar results (the former
population could be assumed to be relatively heterogeneous in age,
sex, etc.). In summary, the results from Study 1 suggest that the
findings of Blanco et al. [9] are robust and partially generalisable
across populations, while complementing them with additional
statistical evidence of the mediator role of P(R) in the depressive
realism effect.
Study 2 provides converging evidence for the latter finding. The
same causal structure was tested, but through a different strategy
(experimental intervention). Study 2 used exactly the same tasks
and parameters that were used in Blanco et al.’s work [9] and
Study 1, except for the instructions that the participants received
before the training phase. These instructions were intended to
manipulate the probability of responding, in a manner similar to
that employed in previous studies [6]. As expected, the group that
received the Analytic instructions (which included the requirement
to respond on 50% of the trials) responded less frequently than the
group that was presented with the Naturalistic instructions, which
included encouragement to respond frequently.
Aligning with Blanco et al. [9], the only predictor of judgments
was the probability of responding. Thus, when we prevented
depressive symptoms from influencing the level of responding
(because participants were explicitly instructed to respond with
Figure 4. Scatterplot depicting judgments as a function of BDI scores in Study 2. Three regression lines are plotted: The shaded line shows
the overall relation between BDI scores and judgments; the full black line and the dashed black line refer to the Analytic and Naturalistic groups,
respectively. Note the flatness of the three slopes (in fact, no coefficient was significantly different from zero; see main text).
doi:10.1371/journal.pone.0046203.g004
Activity Level and Depressive Realism
PLOS ONE | www.plosone.org 6 September 2012 | Volume 7 | Issue 9 | e46203
either high or low frequency), it was the response frequency and
not the depressive symptoms that predicted the judgments: A
higher probability of responding led to a stronger illusion of
control, whereas a lower probability of responding led to better
accuracy at detecting the uncontrollability of the outcome. We
highlight that, apart from the instructional manipulation, the task
was identical to the one in which the depressive realism effect was
shown in Blanco et al. [9] and in Study 1. In addition, the sample
size of Study 2 (N= 198) was much larger than that of Study 1
(N= 49) and Blanco et al. (N= 66), hence minimizing the
possibility that the absence of an effect of depressive symptoms
was due to a lack of statistical power. Then, we conclude that
depressive realism vanished when depressive symptoms were not
allowed to influence the response frequency because the latter was
directly manipulated.
It is important to note that our account that the depressive
realism effect is mediated by the probability of responding is not
incompatible with other proposed mechanisms based on cognitive
or behavioral factors. Thus, it is not intended to rule out the
complex and multicausal view on the phenomenon that some
experts support [13]. The clear and distinctive prediction that can
be derived from our proposal is that any manipulation aimed at
reducing the frequency of responses should also lead to the more
accurate detection of uncontrollability, be it depressive symptoms
(as in Study 1 and Blanco et al. [9]), instructions (as in Study 2),
fatigue, motivational factors, an analytical rather than naturalistic
approach to the problem, or other means.
Moreover, our mediational hypothesis allows a theoretical
account of depressive realism in terms of contingency learning. It is
important to mention that depressive realism has been recently
studied in this domain [10,11,14]. Most theoretical models of
learning, such as the Rescorla-Wagner model [36], do not include
any explicit statement about the effects of motivational or mood-
related factors on the learning of associations between stimuli. If
these factors are reconceptualized, however, as distal causes
mediated by the P(R), as we argue here, then some contingency
learning models may indeed account for depressive realism and
related phenomena because the effect of the mediator variable,
P(R), can be easily addressed by these theories. For instance, some
contingency learning models would claim that the illusion of
control displayed by nondepressed/active participants in our
studies could be explained as the result of granting uneven
importance to different types of trials during the training phase
(i.e., cell weighting). That is, when evaluating contingency, the
participants give different weights to each type of events they are
exposed to, and usually consider concurrences of the desired
outcomes with their responses to be the most important event [38–
40]. In more general domains (e.g., social psychology), people are
also known to base their beliefs and decisions on confirmatory
evidence, neglecting disconfirmatory information [41,42] in a way
similar to the one we are describing here. This idea is also implicit
in the Rescorla-Wagner model [36], given the widely accepted
assumption that the learning rate parameter possesses a greater
value in response-present trials than in response-absent trials. As a
result of this asymmetry, the associative strength of the response
increases more rapidly because of the accidental concurrences
than it decreases because of the response-no outcome trials.
Furthermore, note that for those participants responding with high
probability, the accidental response-outcome concurrences are
actually the most frequent type of trial [37].
To sum up, the predictions of these two variants of the
contingency learning theory (cell weighting-based and associative)
converge when they are applied to our two current studies: They
both predict that increasing the response frequency will strengthen
the illusory perception of a response-outcome correlation (assum-
ing that the probability of the desired outcome is high in all cases).
Thus, a mediational hypothesis that transfers motivational or
depression-related differences to a more proximal cause, P(R),
which is amenable to modeling in contingency-learning terms
(unlike the former variables), becomes useful for making specific or
quantitative predictions and modulating the depressive realism
effect.
Finally, we would like to discuss the implications of our current
findings for the clinical field. It has been argued that an accurate
and realistic perception of reality is not always a healthy feature
[8,29]. In fact, the illusion of control protects against stressors,
both natural and artificially induced in the laboratory [8].
Therefore, some theories claim that certain optimistic biases and
illusions are attributes of healthy, well-adapted behavior
[29,43,44]. According to this view, the illusion of control may
enhance self-esteem, leading the individual to optimistically believe
that he or she is able to control certain aspects of his or her life
instead of attributing them to chance or to uncontrollable factors.
It has been suggested that depressed people are not as motivated to
protect their self-esteem as healthy people are, leading to the
realistic but uncomfortable perception of uncontrollability [43,44].
Based on the mediational hypothesis presented here, we can
suggest that clinically imposed contingencies aimed at increasing
the depressed patient’s activity and probability of responding (like
those commonly used in cognitive-behavioral therapy) may help
develop these healthy, optimistic illusions. Therapists in the
behavioral tradition are already promoting an increase in patient
activity as a clinical strategy against depression [17,45]. These
therapies are mostly based on theories that conceptualize
depression as a result of a previous history of nonreinforcement,
and being active is normally the best possible way to obtain
reinforcers. We acknowledge the obvious limitations inherent in
working with nonclinical samples, such as college students or
Internet users; still, we note that our results tend to coincide with
the previously mentioned approach to depression treatment, and
they add value to it: Increasing the probability of responding is
good not only because it allows people to obtain controllable
reinforcers but also because it makes their behavior coincide even
with those desired outcomes that occur by mere chance and are
beyond the individual’s control, thus leading to the development of
healthy optimistic illusions.
Acknowledgments
We would like to thank Esther Calvete and Susana Segura for their very
helpful comments on and discussion of earlier versions of this paper.
Author Contributions
Conceived and designed the experiments: FB HM MAV. Performed the
experiments: FB. Analyzed the data: FB. Wrote the paper: FB HM MAV.
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... Another approach is to assume that perceived control is data-driven. Thus, it has been argued that instead of, or perhaps in addition to, pre-existing biases being the main influencer of perceived control ratings, it is the processing of and learning about specific aspects of contingency experience (Blanco, Matute, & Vadillo, 2011;Blanco, Matute, & Vadillo, 2012;Blanco et al., 2009;Byrom, Msetfi, & Murphy, 2015;Msetfi, Wade, & Murphy, 2013) or the current motivational state of the participant (e.g., Baker, Msetfi, Hanley, & Murphy, 2011). For example, Matute (1996) has argued that rates of behaviour, affect the degree of control perceived. ...
... Although participants might be instructed to sample actions and non-actions, there are variations in behaviour. Matute argued that non-depressed people are more likely to do this, and indeed there is evidence consistent with this position (Blanco et al., 2009(Blanco et al., , 2012. ...
... This type of difference may make people showing signs of depression to be more sensitive to non-controllable adverse events (Johnson & Sarason, 1978). However, our evidence while correlational, suggests that the High BDI participants: a pre-existing context attentional bias produced more responses ; or a general increased response rate enhanced overall control judgments, (Blanco et al., 2012), particularly in High BDI participants where they over-integrate information from the context . ...
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Depression has been linked to weakened perceptions of control. The experimental evidence derives from tasks with exposure to stable action-outcome contingencies. One assumption has been that performance represents a general cognitive bias that might manifest itself by a global performance difference. Another view is that people have specific situational perceptions of control reflecting their recent actions and the contingencies to which they are currently experiencing. In an experiment with N = 179, participants acquired one of four action-outcome sequences (Constant or Variable). We measured how learning was reflected in ratings of control and probability of responding in relation to mood. In three experimental treatments, the overall contingency across training involved an average moderate degree of control (ΔP = 0.25), but differed in how control varied (Constant or one of two Variable treatments). A fourth, control treatment involved a Constant zero degree of control (ΔP = 0.00). Participants rated their control before, during and after each sequence, providing measures of pre-existing bias, ratings of control in specific situations and generalised control perceptions. Specific control ratings were only influenced by the contingency experience and not pre-existing bias. Higher scores on the Beck’s depression inventory were associated with weakened association between action and context ratings. Overall, these data suggest that human agency is related to rates of responding and that mood is related to a difference in sensitivity to the ratings of and responding to the context.
... Исследование Alloy и Abramson (1979) показало, что люди, страдающие депрессией, более точны, в обнаружении их отсутствия контроля в условных нулевой сопряженности причины и результата, чем люди, не страдающие депрессией. Согласно Blanco F. et al. (2009Blanco F. et al. ( , 2012, одним из аспектов депрессии является большая пассивность, то есть снижение способности инициировать добровольные ответы. Предполагается что испытуемые без депрессии действуют с большей частотой, чтобы получить результат. ...
... В рамках «эвристической модели» существуют также несколько подходов к пониманию иллюзии контроля: в социальной психологии этот феномен воспринимается как мотивационное искажение или искажение самозащиты Weiner B.,1979). В некоторых работах исследователи предполагают этот феномен результатом ассоциативного обучения (Blanco F. et al., 2012;Matute H., 1996), а в более поздних как искажение восприятия сопряженности (Blanco F., 2017;Matute H. et al., 2015 ...
... Как уже упоминалось при описании связи иллюзии контроля и настроения, исследование Alloy и Abramson (1979) (Blanco et al., 2009(Blanco et al., , 2012. Эти черты связаны с дисфорией, но не обязательно подразумевают причину и следствие. ...
Thesis
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... In the depressed view (so-called "depressive realism"), the reward/punishment ratio is evenly balanced, which substantially reduces opportunities for learning and ultimately makes any action pointless (Nettle and Bateson, 2012). This account agrees with clinical reports of greater passivity in depressed people, that is, a reduced ability to initiate voluntary actions (Blanco, Matute, & Vadillo, 2012). Acting with less frequency would make depressed individuals exposed to fewer incidental associations between actions and action-contingent events (reduced "action-density" bias, see Matute et al., 2015), which might in turn impede learning instrumental contingencies and aggravate depressive symptoms in the long run. ...
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... Elucidation of the illusion of control that emphasizes the role of coincidences between behavior and environmental changes is an important step toward providing a basic background for understanding behavioral and learning mechanisms that are related to the origins of false beliefs (Blanco, 2017;Blanco et al., 2009Blanco et al., , 2011Blanco et al., , 2012Blanco et al., , 2013Matute, 1996;Matute et al., 2007). The present data support this approach to better understand the general notion of the illusion of control. ...
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