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The illusion of control consists of overestimating the influence that our behavior exerts over uncontrollable outcomes. Available evidence suggests that an important factor in development of this illusion is the personal involvement of participants who are trying to obtain the outcome. The dominant view assumes that this is due to social motivations and self-esteem protection. We propose that this may be due to a bias in contingency detection which occurs when the probability of the action (i.e., of the potential cause) is high. Indeed, personal involvement might have been often confounded with the probability of acting, as participants who are more involved tend to act more frequently than those for whom the outcome is irrelevant and therefore become mere observers. We tested these two variables separately. In two experiments, the outcome was always uncontrollable and we used a yoked design in which the participants of one condition were actively involved in obtaining it and the participants in the other condition observed the adventitious cause-effect pairs. The results support the latter approach: Those acting more often to obtain the outcome developed stronger illusions, and so did their yoked counterparts.
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Research Article
Illusion of Control
The Role of Personal Involvement
Ion Yarritu,
Helena Matute,
and Miguel A. Vadillo
Universidad de Deusto, Bilbao, Spain,
University College London, UK
Abstract. The illusion of control consists of overestimating the influence that our behavior exerts over uncontrollable outcomes. Available
evidence suggests that an important factor in development of this illusion is the personal involvement of participants who are trying to obtain
the outcome. The dominant view assumes that this is due to social motivations and self-esteem protection. We propose that this may be due to a
bias in contingency detection which occurs when the probability of the action (i.e., of the potential cause) is high. Indeed, personal involvement
might have been often confounded with the probability of acting, as participants who are more involved tend to act more frequently than those
for whom the outcome is irrelevant and therefore become mere observers. We tested these two variables separately. In two experiments, the
outcome was always uncontrollable and we used a yoked design in which the participants of one condition were actively involved in obtaining
it and the participants in the other condition observed the adventitious cause-effect pairs. The results support the latter approach: Those acting
more often to obtain the outcome developed stronger illusions, and so did their yoked counterparts.
Keywords: illusion of control, illusion of causality, contingency judgments, causal judgments, causal learning
In her seminal work on the illusion of control, Langer
(1975) found that people trying to obtain a desired outcome
that occurred independently of their behavior tended to
believe that they were controlling it. The experiments con-
ducted by Langer were followed by many studies with a
common feature: Even though the participants behavior
was not the actual cause of the outcomes, participants nev-
ertheless believed that they were controlling the outcomes
(e.g., Alloy & Abramson, 1979; Matute, 1995, 1996;
Ono, 1987; Rudski, Lischner, & Albert, 1999; Thompson,
1999; Vyse, 1997).
A common index to measure the contingency between
two events is the normative Dp rule (Jenkins & Ward,
1965). It is computed as the difference between the proba-
bility that an outcome occurs in the presence and in the
absence of the potential cause, p(O|C) and p(O|C), respec-
tively. If these two probabilities are equal, the contingency
between the two events is zero and there is no causal rela-
tionship between them. The illusion of control occurs in
these cases.
The traditional approach to the illusion of control has
been framed in motivational terms (e.g., Koenig, Clements,
& Alloy, 1992; Langer, 1975; Thompson, Armstrong, &
Thomas, 1998). From this perspective, peoplesjudgments
of control are influenced by subjective needs related with
the maintenance and enhancement of the self-esteem
(e.g., Heider, 1958; Kelley, 1973; Weiner, 1979). One of
those is the so-called need for control (e.g., Adler, 1930;
Kelley, 1973; White, 1959). It has been shown that the
sense of having control has benefits for well-being (e.g.,
Bandura, 1989; Lefcourt, 1973). The perception of uncon-
trollability has been related to negative consequences at
emotional, cognitive, and motivational levels (Overmier
& Seligman, 1967; Seligman & Maier, 1967), and even
to depression (Abramson, Seligman, & Teasdale, 1978).
Given the importance of actual and perceived control,
some researchers have suggested that the illusion of control
is a self-serving bias that prevents people from the negative
consequences of perceiving the uncontrollability of impor-
tant events (e.g., Alloy & Abramson, 1979; Alloy,
Abramson, & Kossman, 1985; Koenig et al., 1992). As
other self-serving biases, the illusion of control is seen as
a self-esteem enhancing mechanism that allows people to
take credit for successful actions and to deny responsibility
for failures (Bradley, 1978; Heider, 1976). In that way,
when people acting to obtain a desired outcome face a ran-
dom sequence of successes and failures, they may tend to
view themselves as responsible for successes and attribute
failures to other causes such as, for example, chance
(e.g., Langer & Roth, 1975). Moreover, some researchers
have found a positive relationship between the degree of
need for an outcome and the participants overconfidence
in their own chances to obtain it (Biner, Angle, Park,
Mellinger, & Barber, 1995).
From this perspective, overestimating the actual degree
of control over an event is only important to the extent that
controlling it might pose a challenge to self-esteem. Thus,
people do not need to overestimate their control over events
that are irrelevant for their self-esteem. The extent to which
people are involved in obtaining the outcome or the extent
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Hogrefe OpenMind License DOI: 10.1027/1618-3169/a000225
to which the outcome is important for them becomes a cru-
cial factor in this approach (see Thompson, 1999). This fac-
tor, that we will call personal involvement,dependsonthe
potential causal role of the participants actions, as opposed
to external causes (Alloy et al., 1985; Langer, 1975; Langer
& Roth, 1975). Following this reasoning, Alloy et al.
(1985) also claimed that the illusion of control should be
larger in situations in which a persons behavior is the
potential cause because these situations are relevant to
self-esteem; cases in which the persons behavior is not a
potential cause are irrelevant and should not produce an
Evidence for this view comes mainly from studies on
the depressive realism effect (Alloy & Abramson, 1979;
Alloy, Abramson, & Viscusi, 1981; Alloy et al., 1985;
Msetfi, Murphy, & Simpson, 2007; Msetfi, Murphy,
Simpson, & Kornbrot, 2005; Presson, & Benassi, 2003).
In their seminal work, Alloy and Abramson (1979) found
that depressed and nondepressed people differed in their
ability to detect the absence of control. Nondepressed par-
ticipants showed an illusion of control when they judged
the control they exerted over uncontrollable outcomes.
Depressed participants showed an accurate perception of
their absence of control. This has generally been interpreted
as a lack of motivation of d epressive participants to make
use of the self-service mechanism that leads to the illusion
of control (or vice versa, a weaker susceptibility to the illu-
sion of control being part of the causal chain leading to
depression, see Alloy & Abramson, 1979; Alloy et al.,
A very different approach has emphasized the cognitive
aspects of the illusion of control. Within this framework,
the illusion of control is seen as a deviation from the accu-
rate judgments of contingency (i.e., those based on Dp; see,
e.g., Allan & Jenkins, 1983) that should be expected when
participants learn the relationship between their behavior
and uncontrollable outcomes. Research in this field has
been interested on how people make use of the information
derived from cause-outcome pairings, regardless of whether
the cause is the behavior of the person who judges the cau-
sal relation or an external event (e.g., Allan & Jenkins,
1983; Blanco, Matute, & Vadillo, 2013; Jenkins & Ward,
1965; Kao & Wasserman, 1993; Shanks, 2007; Wasserman,
1990). From this perspective, the illusion of control has
been regarded as a special case of a more general illusion
which has been called the illusion of causality (see Matute,
Yarritu & Vadillo, 2011). Therefore, the illusion of control
is expected to work just like any other causal illusions in
which the potential cause is an external event.
When participants act (potential cause) to obtain the
outcome, their action can be successful (the outcome
occurs) or not (the outcome does not occur). These two sit-
uations are represented by cells a and b in the contingency
table (see Table 1), respectively. Similarly, if the participant
does not act to obtain the outcome (i.e., the potential cause
is absent), the outcome can occur or not. This is represented
in Table 1 by cells c and d. The potential cause in this table
does not need to be the participants behavior. Despite the
many differences among the various theories of contin-
gency judgments that attempt to explain the illusion of con-
trol and related effects (see Blanco, Matute, & Vadillo,
2011, 2012), they all agree that decades of research in this
area have shown that people do not give the same weight to
each cell in the contingency matrix (e.g., Kao &
Wasserman, 1993). Cause-outcome coincidences (i.e., cells
a) are known to be the pieces of information that have the
largest impact on contingency judgments (e.g., Anderson &
Sheu, 1995; Kao & Wasserman, 1993, Matute et al., 2011,
Smedslund, 1963; White, 2003). Thus, a variety of theories
of contingency judgments, which are clearly different from
each other (see Shanks, 2007; 2010 for comprehensive
reviews of associative, inferential, and other theoretical
accounts of contingency judgments), will nevertheless pre-
dict that any factor that contributes to a higher number cell
a events, with respect to the other cells, should promote
higher judgments.
One such factor is the probability of the outcome, p(O).
It is well known that when p(O) is high, people tend to
overestimate the relationship between the potential cause
and the outcome. This is known as the outcome-density
bias and is a key factor in the development of the illusion
of causality and of control (Allan & Jenkins, 1983; Alloy
& Abramson, 1979; Hannah & Beneteau, 2009; Matute,
1995; Msetfi et al., 2005; Tenenn & Sharp, 1983). In addi-
tion to the outcome-density bias there is the cue-density
bias which refers to an overestimation of contingency judg-
ments when the probability of the potential cause, p(C), is
high (Allan & Jenkins, 1983; Hannah & Beneteau, 2009;
Vadillo, Musca, Blanco, & Matute, 2011). While the out-
come-density bias has been widely studied, both in situation
in which participants are personally involved (i.e., the par-
ticipants behavior is the potential cause, see, e.g., Matute,
1995) and in which they are not (i.e., an external event is
the potential cause, see, e.g., Allan, Siegel, & Tangen,
2005), the effect of the probability of the cause (i.e., the
action) on the illusion of control has received less attention.
However, there is evidence supporting the idea that the
more the participants act, the greater their contingency
judgments will be (e.g., Blanco, Matute, & Vadillo, 2009;
Blanco et al., 2011; Matute, 1996).
It follows from these analyses that even when the out-
come is uncontrollable, if p(O) is high, a person who acts
frequently to obtain the outcome will experience a high
number of cause-outcome coincidences and will almost cer-
tainly develop an illusion of control (Blanco et al., 2011;
Matute, 1996). Importantly, participants who are personally
involved in trying to obtain an outcome tend to act with
higher frequency than those for which the outcome is
irrelevant, who often become mere observers (at best).
Table 1. Contingency matrix containing the four possible
cause-outcome combinations
O (outcome) O (no outcome)
C (cause) ab
C (no cause) cd
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Thus, these two variables, personal involvement and action
probability, may have often been confounded. We therefore
propose that if those two variables are tested separately
from each other, it might turn out that it is not personal
involvement per se, but probability of action, what pro-
duces the illusion.
Importantly, there is evidence suggestive that being the
one who performs the action is not even necessary. The
effect of p(C) has been demonstrated in situations in which
the potential cause is an external event (e.g., Kutzner,
Freytag, Vogel, & Fiedler, 2008; Matute et al., 2011;
Perales, Catena, Shanks, & Gonzlez, 2005; Vadillo
et al., 2011). For instance, in a recent experiment by Matute
et al. (2011), the contingency between a potential cause
(i.e., a fictitious medicine administered by a fictitious
agent) and an outcome (recovery from illness) was zero,
but p(O) was .80. For one group p(C) was .80, for the other
was .20. Both groups showed an illusion of causality but the
former group gave significantly higher judgments than the
second. Thus, being personally involved is not necessary to
develop the illusion, and a high action probability is not
necessary either. Instead, the high frequency with which
the potential cause occurs (assuming that the desired out-
come is also frequent but regardless of whom is the agent),
predicts when the illusion will occur. Nevertheless, it
should be noted that those experiments used scenarios in
which all participants were observers and did not compare
them to conditions in which participants were acting to
obtain the outcome and a true illusion of control could
develop. The present research aimed to provide such
To our knowledge, one of the very few studies that
empirically compared the illusion of control under condi-
tions in which the potential cause was the participants
behavior or an external cause was that of Alloy et al.
(1985). Their conclusions were opposite to our expecta-
tions. They reported that personal involvement, and not
p(C), was the necessary factor in the development of the
illusion. However, there are several methodological issues
in their study that could explain those results. What Alloy
et al. (1985) found was that the illusion of control appeared
when participants were asked about the causal relationship
between their behavior and an outcome, and not when
asked about the predictive relationship between external
events. This result need not mean that personal involvement
is necessary for the illusion of control to occur. Alterna-
tively, it could be due to the fact that different questions
(i.e., causal vs. predictive questions) give rise to differential
judgments (Matute, Vegas & De Marez, 2002; Vadillo &
Matute, 2007; White, 2003). In addition, the difference
observed by Alloy et al. could be due to their using causes
in one group and predictors in the other, as causes and pre-
dictors have also been shown to produce different judg-
ments (PineÇo, Denniston, Beckers, Matute, & Miller,
2005). Moreover, Alloy et al. did not report the participants
number of actions. In their studies, the number and
sequence of actions and no action trials given by the partic-
ipants who were involved in getting the outcome could
have been very different from the number of cue events
presented to participants who were observers, and this dif-
ference might also explain their differential judgments. The
fact that this variable was not reported suggests that it was
not considered relevant and might have been confounded.
Ideally, and in order to compare the judgments in one case
or the other, it is necessary that the cue (whether the partic-
ipants behavior or an external event) occurs with the same
frequency and distribution in both cases. Moreover, similar
cause and effect events and similar assessment questions
should be used in both cases. The present research aimed
to provide a fairer comparison between the conditions in
which the potential cause is the participants behavior and
those in which the cause is an external event.
Experiment 1
We used a yoked design. Participants were shown the
records of fictitious patients who suffered from a fictitious
disease. Each participant in Group Active was free to
administrate a fictitious medicine to their patients. Each
participant in Group Yoked observed the sequence of
actions given by their counterpart participant in the Active
Group as well as their consequences. Therefore, the proba-
bility and sequence with which the cause occurred was
defined by Group Active. For participants in Group Active
the potential cause of the outcome was their own behavior;
for those in Group Yoked it was an external event.
The yoked design allows us to test the effect of two
variables that have often been confounded: Personal
involvement (Active vs. Yoked Group) and p(C). It is when
these two variables become disentangled, that the predic-
tions of the motivational and the cognitive approaches
become clearly different. According to the motivational
approach, if the two variables are separated from each
other, only personal involvement should affect the judg-
ments of contingency. By contrast, according to the cogni-
tive account it is p(C) that should affect the participants
judgments, regardless of whether they are actors or
Participants and Apparatus
Ninety-two anonymous volunteers participated in the
experiment in exchange for a cafeteria voucher. The
sequence of cause-outcome pairings presented to each par-
ticipant in Group Yoked was derived from the performance
of the corresponding active participant. Thus, it was neces-
sary to program the computer differently for each yoked
participant. For this reason, the first 10 participants were
assigned to Group Active. Participants were then randomly
assigned to each condition as they arrived at the laboratory,
resulting in a total of 46 participants in Group Active and
I. Yarritu et al.: Illusion of Control
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46 in Group Yoked. The experiment was run on personal
computers located in individual booths.
Procedure and Design
The task was an adaptation of the allergy task, which has
been widely used in contingency judgment research. This
task has proven to be sensitive to the effect of the illusion
of causality both when the potential cause is an external
event (e.g., Matute et al., 2011) and when it is the partici-
pants behavior (Blanco et al., 2011). As in Blanco et al.s
study, we modified the standard procedure so that it would
allow for the participants actions as potential causes. Par-
ticipants were prompted to imagine being a medical doctor,
who specialized on a rare disease called ‘‘Lindsay Syn-
drome’’. They were told about a new medicine (Batatrim)
that could cure the crises caused by the disease. Their mis-
sion was to find out whether this medicine was effective.
There were 100 learning trials (i.e., 100 fictitious patients)
before the test phase. In each trial, participants in Group
Active were free to act (to administer the medicine to a fic-
titious patient) and observe the effects. Participants in
Group Yoked saw, in each trial, whether the patient was
given the medicine (cause) as well as whether the patient
recovered (outcome). The probability of the cause for each
pair of Active-Yoked participants was thus defined by the
number of trials in which the active participant decided to
administer the medicine, divided by the total number of tri-
als. The sequence of trials in which the cause (i.e.,
Batatrim) was present or absent for the participants in
Group Yoked was also defined by the sequence of trials
in which their counterpart active participant decided to
administer the medicine. Neither the active nor the yoked
participants were aware of this feature of the design. The
occurrence of the outcome (recovery from the crises) was
independent from the participants behavior and followed
a predefined pseudorandom sequence, identical for both
groups. Therefore, the resulting sequence of cue-outcome
pairings was identical for each Active-Yoked pair of partic-
ipants. The probability of the outcome was high (.80)
because, as described above, this is known to lead to a
stronger illusion of control.
After completing all 100 training trials, participants
were presented with the following question: To what extent
do you think that Batatrim was effective in healing the cri-
ses of the patients you have seen? In the illusion of control
experiments, participants are usually asked about the extent
to which they believe that their behavior was effective in
controlling the outcome. Because the potential cause in
our experiment was an external event for half of the partic-
ipants, we substituted the standard controllability wording
for the more general ‘‘effectiveness’’ phrasing. This allowed
us to present the same question to all participants.
The answers were given by clicking on a 0–100 scale,
anchored at 0 (definitely NOT)and100(definitely YES).
Results and Discussion
The mean p(C) was collected from the actions of active par-
ticipants, so the value was the same for both Active and
Yoked Groups. The mean and the standard error of the
mean were 0.59 and 0.03, respectively. We conducted a
multiple regression analysis including personal involve-
ment, p(C), the interaction between these two factors and
the actual experienced
contingency as predictors of the
judgments. The backward elimination method for the
regression analysis was used. This method tests a series
of regression models, excluding in each new model the
worst predictor of the previously tested model according
to a statistical criterion (p .10). Following this strategy
reduces the risk of failing to detect a relationship that actu-
ally exists (see Menard, 1995). The results of this analysis
can be seen in Table 2. According to this method, actual
experienced contingency, personal involvement, and the
Personal Involvement · p(C) interaction were excluded,
in that order, as predictors of the participantsjudgments.
The final and most parsimonious model contained only
In order to further assess the influence of p(C), the sam-
ple was then classified as a function of the number of
actions given by the Active Group, that is, their p(C). We
selected participants who were below or equal to the
33.33 percentile of this variable (Low p(C), a probability
below or equal to 0.50) and participants who were over
or equal to the 66.66 percentile (High p(C), a probability
above or equal to 0.68). The mean judgments for each
p(C) condition in each of the two personal involvement
conditions can be seen in Figure 1. A 2 (probability of
the cause: High vs. Low) · 2 (Personal Involvement: Ac-
tive vs. Yoked) analysis of variance (ANOVA) showed a
main effect of p(C), F(1, 60) = 14.08, p <.001,g
All other effects were nonsignificant, largest
F(1, 62) = 1.91, g
= .03. Thus, as expected from the cog-
nitive account, it was the frequency with which the cause
occurred (be it the participants behavior or an external
event) that favored a higher or lower illusion, and not the
fact that some participants acted and others observed.
Despite the fact that these results clearly suggest that the
key factor in the development of the illusion of control is
p(C), there are reasons why a definitive claim in favor of
the cognitive hypothesis must be taken with caution. First,
it could be argued that the active participants in this exper-
iment were not really engaged in the task. Given that par-
ticipants were prompted to detect the relationship
between the medicine and recovery from the crises, and
not to obtain the outcome (i.e., recovery), it is possible that
Because participants in the Active Group are free to act in each trial and the occurrence of the outcome event is predefined in a pseudo-
random sequence, there is some degree of variance in the contingency to which participants are actually exposed, but previous research has
shown that this variance does not influence participants judgments (Blanco, Matute, & Vadillo, 2011). Nevertheless, and despite this
variance being identical for both groups in the present research, we preferred to include this variable in the regression analysis.
4 I. Yarritu et al.: Illusion of Control
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Hogrefe OpenMind License
their motivation to control the outcome was low. Second, it
could be argued that the personal involvement and the
probability-of-the-cause factors did not have the same
chances to affect participants judgments. In this experi-
ment p(C) was a continuous variable derived from the
action rate of Group Active, while personal involvement
was a dichotomous variable that resulted from the experi-
mental manipulation. The greater number of levels of the
cognitive variable, p(C), in comparison to the involvement
variable, could have favored the observation of a significant
correlation between judgments and p(C). The next experi-
ment addresses these concerns.
Experiment 2
Two are the main modifications that we introduced in
Experiment 2 with respect to the involvement factor, and
one with respect to the cognitive factor. First, we tried to
better motivate active participants by changing the overall
goal of the task. In this experiment we explicitly informed
all participants that the main goal of the task was to obtain
as many outcomes as possible. That is, to heal as many
patients as possible. Second, we manipulated personal
involvement using the actor-observer procedure commonly
used in the self-serving literature. To do so, we used an on-
line yoked procedure in which, at the time the active partic-
ipant was performing the experiment on his or her com-
puter, the yoked participant was observing everything
(i.e., both the decisions of the active participant and their
outcomes) in a cloned screen. In this case both active and
yoked participants were aware of this feature. That is, the
relevance for self-esteem of the active participant in this
experiment did not come only from their agent role and
their motivation to obtain more outcomes but also from
being observed.
Table 2. Results of backward elimination regression analysis
Model 1 Model 2 Model 3 Model 4
Predictor b t(91) p b t(91) p b t(91) p b t(91) p
Cause probability 0.54 3.90 0.001 0.48 5.25 0.001 0.48 5.20 0.001 0.483 5.23 0.001
Cause Probability · Personal Involvement 0.41 1.64 0.104 0.41 1.65 0.102 0.03 0.37 0.711
Personal involvement 0.41 1.62 0.109 0.41 1.63 0.107
Experienced contingency 0.08 0.57 0.569
Summary R
F(4, 91) pR
F(3, 91) pR
F(2, 91) pR
F(1, 91) p
0.26 7.61 0.001 0.26 10.11 0.001 0.23 13.59 0.001 0.23 27.31 0.001
High p(C) Low p(C)
Mean Judgments
Probability of the Cause
Figure 1. Mean judgments given by participants of
Experiment 1 in the Active and Yoked groups as a
function of p(C), high or low. Error bars denote the
standard error of the mean.
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With respect to the cognitive factor, in Experiment 1 we
did not manipulate the probability of acting. Instead we
simply measured it. Thus, in order to further clarify the
effect of this factor, in Experiment 2 we manipulated the
probability with which active participants acted (i.e.,
administered the medicine). This manipulation featured
two levels, high and low, thereby also assuring that both
the personal involvement and the cognitive factor (proba-
bility of the cause) had the same chances to affect the judg-
ments of participants.
As in the previous experiment, the predictions of the
two approaches to the illusion of control are also clearly
different from each other in Experiment 2. From the moti-
vational approach, it is expected that the illusion of control
will be larger when participants judge the effects of their
own behavior (active participants) than when they judge
the effects of the behavior of others (yoked participants).
From the cognitive approach, there is no reason to expect
that differences should emerge as a function of whether
the potential cause is the participants behavior or some-
body elses behavior. From this perspective, only p(C) is
expected to influence the judgments.
Participants and Apparatus
One hundred anonymous volunteers were paid 5fortheir
participation. They were run in pairs in individual booths.
For each pair of participants, one of them was randomly
assigned to the active cubicle (clearly labeled ‘‘Participant
A’’ on the wall above the screen, and including a mouse
in addition to the computer screen). The other one was
assigned to the yoked cubicle (labeled ‘‘Participant B’’
and containing only a screen). The two screens were con-
nected to the same computer so that they showed identical
information at all times.
Procedure and Design
This experiment used an adaptation of the task used in
Experiment 1. To manipulate the personal involvement
and the probability of the cause in a more comparable man-
ner, the experiment used a 2 · 2 factorial design. Partici-
pants of the two involvement conditions were exposed to
exactly the same contingency information and were both
told that the goal of the active participants was to heal as
many (fictitious) patients as possible. The instructions that
they received were also identical, with the following para-
graph stating what each of them should do: ‘‘If you are par-
ticipant ‘‘A’’ you will have to decide whether or not to
administrate Batatrim to each patient. If you are partici-
pant ‘‘B’’ you will have to observe those decisions and their
The probability of the cause was also manipulated in
two levels. Participants in the High condition had a maxi-
mum of seven doses of Batatrim for every 10 patients (tri-
als). Participants in the Low condition had a maximum of
three doses for every 10 patients. Participants were told that
every 10 patients they would get a new supply of seven (or
three) doses. They were also requested to use them all.
Thus, some participants were asked to respond in 30% of
the trials (Low p(C) Group) while others were requested
to respond in 70% of trials (High p(C) Group).
As in the previous experiment, the probability of the
outcome (recovery) was high (.80) regardless of whether
or not the cause was presented. The outcome was presented
in a predefined pseudorandom sequence. Once the training
phase was finished participants gave their effectiveness
judgment. The test question was the same as in Experiment
1 but was administered using paper and pencil because each
pair of participants shared the same computer. Once partic-
ipants wrote their judgment, they received a second sheet of
paper with the following question, aimed to assess whether
the involvement manipulation had been effective: To what
extent did you feel involved in the healing of the patients?
The answers for both questions were given using a 0–100
scale, anchored at 0 (definitely NOT)and100(definitely
Results and Discussion
As the active participants were free to administer Batatrim
in each trial (always within the limits of the number of
doses imposed by the experimental manipulation), that is,
some of them could choose not to act, we first needed to
ensure that their action rates coincided with those planned
for each condition. To do so, we imposed a selection crite-
rion of action rate that must be satisfied by each active par-
ticipant in order to include his/her data (and that of the
corresponding yoked participant) in the analyses. This cri-
terion is that all active participants must give at least
95% of all possible actions. In the Low p(C) Group the
limit is 30 doses and they were asked to use them all.
Therefore, if the active participant in this condition admin-
istrates the medicine in less than 27 trials (95% of 30), the
data of this pair of participants is removed from subsequent
analysis. For the High p(C) condition, the criterion is that
the active participant must administrate the medicine in
63 trials or more (95% of 70). These criteria were satisfied
by 39 of the 50 pairs of participants (a total of 78 partici-
pants). Of these 78 participants, 40 (20 active and 20
yoked) were in the Low p(C) condition and 38 (19 active
and 19 yoked) were in the High p(C) condition.
We next conducted an analysis of the answers to the
question that we added at the end of the experiment to
check whether the involvement manipulation had been
effective. Means (and standard errors of the means) in this
question for the active and yoked participants were 67.38
We also conducted an alternative analysis with the complete sample, including those participants who did not comply with the data
selection criterion. The results of this alternative analysis do not differ from the analysis presented here.
6 I. Yarritu et al.: Illusion of Control
Experimental Psychology 2013 2013 Hogrefe Publishing. Distributed under the
Hogrefe OpenMind License
(4.02) and 46.36 (4.21), respectively. A 2 (Probability of
the Cause) · 2 (Personal Involvement) ANOVA found
that, as expected, the degree of personal involvement which
the participants felt toward the task was higher for the
active participants than for the yoked participants,
F(1, 74) = 10.75, p < .005, g
= .13. Also as expected,
the main effect of p(C) and the interaction were nonsignif-
icant, largest F(1, 74) = 0.94, g
= .01. That is, the
involvement manipulation worked as planned.
The critical results are the mean judgments of effective-
ness for each condition. These are shown in Figure 2. The
figure suggests that judgments did not differ between active
and yoked participants. Judgments were higher in the High
than in the Low p(C) condition. A 2 (Probability of the
Cause) · 2 (Personal Involvement) ANOVA confirmed
these findings. As expected, a significant main effect of
p(C) was found, F(1, 74) = 16.41, p < .001, g
and no main effect of personal involvement nor an interac-
tion was observed, largest F(1, 74) = 0.47, g
Therefore, and consistent with our hypothesis, participants
judgments of contingency were affected by p(C) and not by
personal involvement.
The results of this experiment are congruent with those
of Experiment 1. Moreover, in this case it is difficult to
question the validity of the personal involvement manipula-
tion. As shown by the manipulation check, the experimental
manipulation affected the extent to which participants felt
motivated toward the task. Importantly, this difference
between active and yoked participants did not affect their
judgments, which were only affected by p(C). This finding
leads us to suspect that previous results that have been
attributed to personal involvement may not always be due
to a direct effect of motivational factors on contingency
estimation. Instead, the present results suggest that the
apparent effect of personal involvement on judgments
might be due to the higher probability of action of partici-
pants who are more personally involved.
General Discussion
The results of the two experiments presented here provide
little support for the motivational approach. From this
approach it is argued that people must be personally
involved in trying to obtain the outcome, and their self-
esteem at risk, for the illusion to occur (Alloy et al.,
1985; Thompson, 1999; Thompson et al., 1998). This claim
lies on the idea that the illusion of control is a self-serving
bias that activates when the relationship judged is relevant
to self-esteem (e.g., Alloy & Abramson, 1979; Dudley,
1999; Koenig et al., 1992). However, we did not find an ef-
fect of personal involvement when it was tested indepen-
dently of p(C). Participants of the Yoked Group showed
the illusion of control even though their judgments were
not relevant to protect their self-esteem. Moreover, we
found a strong effect of p(C). As we have noted earlier, this
p(C) effect could explain the results that had been often
attributed to personal involvement in previous research, gi-
ven that participants who are more involved tend to per-
form more actions to obtain the outcome.
Alloy et al. (1985) had previously reported an investiga-
tion in which, as in the present one, personal involvement
and p(C) had been separated from each other. They re-
ported that participants who judged the predictive value
of an external event did not show a significant overestima-
tion of contingency, while participants who judged the
capacity of their own behavior to control the outcome did
so. Alloy et al. concluded that people overestimate contin-
gency only when they are judging their own behavior be-
cause only this is relevant for self-protection. The present
results do not support their conclusions. Instead, the differ-
ences observed by Alloy and her colleagues could be due,
as mentioned in the Introduction, to the different assess-
ment question that they used in each case (Matute et al.,
2002; Vadillo & Matute, 2007; White, 2003), or to the fact
that they used causes in one group and predictors in the
other (see PineÇo et al., 2005, for differences between
them). In addition, Alloy et al. did not report the number
of attempts (i.e., actions) performed by participants in the
active condition, nor the value of p(C) presented to passive
participants. The influence of this factor has proven to be
significant in the present research and personal involvement
has not. As our results show, when p(C) was high, the illu-
sion was high as well. This is in line with previous studies
in which the influence of p(C) was tested. Indeed, this p(C)
effect is often described more generally as the probability
of the cue effect, or the cue-density effect, as it occurs with
either causes or predictors as cue events; see, e.g., Blanco
et al., 2011, 2013; Hannah & Beneteau, 2009; Matute,
1996; Matute et al., 2011; Perales et al., 2005; Vadillo
et al., 2011).
As noted in the Introduction, another factor that is
known to favor the illusion of control is p(O). Thus, we
used a situation in which this probability was always high.
Given that p(O) is high in cases in which the illusion
occurs, the effect of p(C) appears to be due to the fact that
ahighp(C) makes it very likely that the cause and the out-
come coincide in many trials (see Blanco et al., 2011,
High p(C) Low p(C)
Mean Judgments
Probability of the Cause
Figure 2. Mean judgments given by Active and Yoked
groups of Experiment 2 in each p(C) group, high or low.
Error bars denote the standard error of the mean.
I. Yarritu et al.: Illusion of Control
2013 Hogrefe Publishing. Distributed under the Experimental Psychology 2013
Hogrefe OpenMind License
2013). Moreover, it is well known that these cause-effect
coincidences tend to have more weight on the perception
of causal relations than trials in which only the cause or
the outcome occurs (e.g., Kao & Wasserman, 1993). As
noted in the Introduction, this result is predicted by many
different theories of contingency judgments (see Blanco
et al. 2011, 2012).
The main contribution of the present experiments is that
the effects of personal involvement and probability of the
cause are tested independently of each other. Even though
the predictions of the motivational and the cognitive
approaches can often be identical (because increased motiva-
tion produces more active behavior), when these two vari-
ables are tested separately, the predictions of the two
approaches become clearly different. The motivational
approach predicts, for these cases, that only those who act
to obtain the outcome should develop the illusion. The cogni-
tive approach predicts that only p(C) should influence the
illusion. In our experiments, the judgments of participants
who were involved in obtaining the outcome can be directly
contrasted to the judgments of those who simply observed the
identical events. Under these conditions, the results showed
that the probability of the potential cause was the only vari-
able that clearly influenced the participants judgments.
Although our results suggest that personal involvement
has no influence on the illusion of control, we must
acknowledge that our conclusions are based on the absence
of significant differences with respect to this variable. It is
possible that our participants were not sufficiently engaged
in the task, so that their performance was actually irrelevant
for self-esteem. Nevertheless, in the absence of more con-
vincing evidence about the role of personal involvement
in the illusion of control, it seems more parsimonious to
assume that a single process (biased contingency detection
due to a high probability of the cause) is responsible for the
illusions previously attributed to personal involvement
(Alloy et al., 1985). Indeed, Matute, Vadillo, Blanco, and
Musca (2007) have shown that even an artificial learning
system using a very simple and popular learning algorithm
such as the Rescorla and Wagner (1972) model will devel-
op these illusions when the outcome occurs frequently and
the system acts frequently. On the other hand, although the
influence of self-protection may not be ruled out in all cases
in which people develop illusions of control, what our re-
sults show is that this influence is not necessary to account
for all instances of the illusion of control reported in the lit-
erature. In any situation in which personal involvement
may translate into more active behavior, psychologists need
to be aware that the increase in p(C), rather than a need to
protect self-esteem, may be producing the illusion.
In closing, it is important to note that even though the
motivational approach is normally presented as an explana-
tion of the illusion of control, it does not really provide such
explanation. That is, it predicts that the illusion will be
stronger when people are more personally involved, but
does not attempt to explain how the illusion takes place
(see Matute & Vadillo, 2012, for d iscussion). If this is
acknowledged, our proposal becomes perfectly compatible
with the motivational framework. The p(C) explanation we
have advanced aims to provide just such underlying
Support for this research was provided by Grant No.
PSI2011-26965 from Direccin General de Investigacin
of the Spanish Government and Grant No. IT363-10 from
the Basque Government. Ion Yarritu was supported by fel-
lowship BES-2008-009097 from the Spanish Government.
We would like to thank Fernando Blanco, Pablo Garaizar,
Cristina Orgaz, Nerea Ortega-Castro, and Sara Steegen for
illuminating discussions.
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Received December 25, 2011
Revision received May 17, 2013
Accepted May 22, 2013
Published online August 16, 2013
Helena Matute
Departamento de Fundamentos y Mtodos de la Psicologa
Universidad de Deusto
Apartado 1
48080 Bilbao
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... One of these possible strategies would be to disseminate accurate information on pseudotherapies' effectiveness to both the population and health-related interest groups, based on the "information deficit model" [22]. For these campaigns to be effective, it is important to take into account the psychological factors that induce belief in pseudotherapies' effectiveness, such as the illusion of causality [20,25] and the illusion of control [26] and show the mechanisms by which an ineffective intervention can be correlated with apparent efficacy [27,28]. The cultural factors that help to legitimize pseudotherapies and their use should also be considered, especially narratives about their efficacy [24]. ...
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... Individuals regard themselves as causal agents when trying to get an outcome. Under illusion of control (IOC) or illusory control, people often perceive more control than they have and notice covariation where none exists (Presson & Benassi, 1996), so that randomly determined events are frequently interpreted as dependent on the subject's action (Biner et al., 2009;Langer, 1975;Langer & Roth, 1975;Yarritu et al., 2014). The phenomenon has been demonstrated by diverse lines of evidence: laboratory experiments, observed behavior in games of chance such as lotteries, and interviews that include self-reports of real-world behavior (Presson & Benassi, 1996;Stefan & David, 2013). ...
Illusion of control (IOC) is a bias in the judgment of personal success with implications to learning theories and health policies; some important questions in the investigation of IOC may be related to traditional measures in the field, namely self-assessment using Likert scales about the sense of control. Statistical process control (SPC) and Shewhart charts are methods developed to monitor and control industrial processes, never applied in psychological studies before. The present two studies investigated the use of the technique of Shewhart charts in the analysis of IOC. The purpose was to explore the use of SPC and Shewhart charts in the analysis of data sequences from psychological experiments; the objective was to analyze the results of reaction time (RT) data sequences plotted in SPC charts, in comparison with self-assessment judgments from an IOC task. Participants were 63 undergraduate students (Study 1) and 103 mine workers (Study 2) instructed to try to control a traffic light on a computer by pressing or not the keyboard. Higher probabilities of the successful outcome generated judgments of illusion and shifts (due to cognitive activity) in the charts of RT; lower probabilities resulted in null illusion and RT presented a random and stable profile. Patterns for different groups emerged in Shewhart charts. SPC can contribute to the analysis of the behavior of sequences of data in psychological studies, so that the charts indicate changes and patterns not detected by traditional ANOVA and other linear models.
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Objective To answer the question: Why do people consent to being vaccinated with novel vaccines against SARS-CoV-2? Design Representative survey. Setting Online panel. Participants 1032 respondents of the general German population. Method A representative survey among German citizens in November/December 2021 that resulted in 1032 complete responses on vaccination status, sociodemographic parameters and opinions about the COVID-19 situation. Results Almost 83% of the respondents were vaccinated. The major motivation was fear of medical consequences of an infection and the wish to lead a normal life again. The major motivation to be not vaccinated was the fear of side effects and scepticism about long-term effectiveness and safety. Sixteen per cent of vaccinated respondents reported some serious side effect, while more than 30% reported health improvements, mostly due to the relief of psychological stress and social reintegration. We also validated a ‘Corona Orthodoxy Score—COS’ consisting of seven items reflecting opinions on COVID-19. The scale is reliable (alpha=0.76) and unidimensional. The COS was a highly significant predictor of vaccination status and readiness to be vaccinated in a multivariable logistic regression model. Those who were vaccinated were more likely to live in smaller households (OR=0.82, p=0.024), had a higher income (OR=1.27, p<0.001), a higher COS score (OR 1.4, p<0.0001) and used less alternative media (OR=0.44, p=0.0024) and scientific publications (OR=0.42, p=0.011) as information sources. Conclusions The major motives for being vaccinated are fear of medical symptoms and the wish to lead a normal life. Those not wanting to be vaccinated cite a lack of knowledge regarding long-term safety and side effects as reasons. This can likely only be overcome by careful and active long-term efficacy and safety monitoring.
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The study shows the erroneous perception of probability biased by illusion of prediction possibility. Even if the probability of winning is known, people make different decisions concerning joining a game depending on who is fulfilling the task of predicting the result of a random game. The aim of this paper is to test whether people think that it is possible to predict outcomes of a random event and if so, whether they believe that it is possible to achieve better results when applying positive stimulation. By conducting an experimental study with real payoffs (although not monetary), authors of the research tested the hypothesis saying that people who decide to participate in a risky game are more willing to do so if its outcome depends on the decision of somebody whose gain is connected to the outcome of the game than when it is made by somebody who neither gains nor losses anything by playing the game. Over 700 hundred students were asked to participate in a game in which they could gain or lose points needed to pass a course. The task involved guessing the outcomes of two coin tosses. The scenarios differed in terms of the risk level, the person who was guessing and the remuneration. More people were willing to take part in the game according to the scenario where participants could guess outcomes themselves rather than when someone else was guessing. Also, in the game with a higher risk level, more people were willing to play in the scenario in which someone else made a guess but was rewarded for an accurate guess rather than in the scenario where someone else who made a guess was not rewarded for it. The findings of the study allow for a conclusion that participants believed that someone incentivized was able to guess better, i.e. reduced the risk of losing than a person who was not incentivized.
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This study aims to examine the effect of crisis management approaches with the illusion of control and optimism bias of educational administrators. The illusion of control is that people tend to overestimate their capacity to control events. Optimism bias is a cognitive bias that causes a person to believe that they are less likely to experience a negative event. The high level of human relationships along with with pupils density, parents, teachers and administrators; As an open system, educational institutions are places where crises and traumatic events are quitely likely to happen. For this reason, educational administrators should not be caught in the illusion of control and optimism bias. Otherwise, educational institutions may be caught unprepared for crises. The absence of any research on this subject in our country makes this study important. This qualitative study was carried out online with a semi-structured interview form with 24 educators who are currently working as education administrators and had management experience in the past. The answers obtained were systematically analyzed with content analysis method. The results revelead that twenty educational administrators participating in this study experienced the illusion of control and three were experienced the optimism bias. It is seen that managers give different answers on crisis and crisis management issues and they do not have full knowledge of the field. The fact that managers are theoretically and practically equipped in crisis management will make educational institutions safer against possible crisis environments. In order to achieve this, it would be beneficial for the Ministry of National Education and provincial national education directorates to provide training to managers by experts in their fields.
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Expectations are probabilistic beliefs about the future that shape and influence our perception, affect, cognition, and behavior in many contexts. This makes expectations a highly relevant concept across basic and applied psychological disciplines. When expectations are confirmed or violated, individuals can respond by either updating or maintaining their prior expectations in light of the new evidence. Moreover, proactive and reactive behavior can change the probability with which individuals encounter expectation confirmations or violations. The investigation of predictors and mechanisms underlying expectation update and maintenance has been approached from many research perspectives. However, in many instances there has been little exchange between different research fields. To further advance research on expectations and expectation violations, collaborative efforts across different disciplines in psychology, cognitive (neuro)science, and other life sciences are warranted. For fostering and facilitating such efforts, we introduce the ViolEx 2.0 model, a revised framework for interdisciplinary research on cognitive and behavioral mechanisms of expectation update and maintenance in the context of expectation violations. To support different goals and stages in interdisciplinary exchange, the ViolEx 2.0 model features three model levels with varying degrees of specificity in order to address questions about the research synopsis, central concepts, or functional processes and relationships, respectively. The framework can be applied to different research fields and has high potential for guiding collaborative research efforts in expectation research.
Introduction: Depressive realism literature suggests that depressed individuals’ negative self-view is correlated with less self-serving positivity bias. Also, research suggests some social cognitive advantages for individuals with subclinical levels of depression (dysphoria), especially in identifying negative emotions. This study tested the hypothesis that individuals with dysphoric symptoms show less of a truth bias and are more accurate at detecting deception. Moreover, this effect was expected to be stronger in positive statements (I like) than in negative (I dislike) statements. Finally, a lower judgment confidence and a more accurate assessment of their lie detection ability were expected to be found in individuals with dysphoric symptoms. Methods: Two hundred-sixty-nine participants judged the veracity of 24 video statements. Analyses tested the hypotheses with three different measures of depression: the IPIP-240 Depression Subscale, the PHQ-9, and the DESC-I. Results: In contrast to the assumptions, results found no evidence that individuals with dysphoric symptoms were better at identifying false and true messages in general. While higher scores of the DESC-I were negatively correlated with accuracy in lie detection, the IPIP-240 and the PHQ-9 were found to be not significantly correlated with lie detection accuracy. While for like statements individuals with dysphoric symptoms and individuals without (measured with the DESC-I) were not different in accuracy, individuals with dysphoric symptoms had lower accuracy scores in dislike statements than individuals without. Moreover, the PHQ-9 found lower measures of judgment confidence in individuals with dysphoric symptoms compared to individuals without, while the other depression measurements showed no significant differences. Furthermore, no evidence for a more accurate assessment of lie detection ability in individuals with dysphoric symptoms was found. Discussion: Results and directions for future research are discussed.
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Causal learning is the process by which people and animals gradually learn to predict the most probable effect for a given cause and to attribute the most probable cause for the events in their environment. Learning causal relationships between the events in our environment and between our own behavior and those events is critical for survival. From learning what causes fire (so that we could either produce or prevent the occurrence of fire at will) to learning what causes rain, what causes cancer, or what caused that particular silly accident that we had with the car a few days ago, both the history of humankind and our individual history are full of examples in which causal learning is crucial. But, as can be said for other forms of learning as well, causal learning is not free of errors. Systematic biases and errors are known to occur under certain conditions. One of such common biases is the illusion of control. The illusion of control can be defined as the belief that one’s behavior is the cause of a desired event that is actually independent of it. Illusions of control are an important factor in the development of superstitions. For instance, the superstitious belief that by dancing one can produce rain, is normally accompanied by the illusion of controlling rain.
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Recent research has shown superstitious behaviour and illusion of control in human subjects exposed to the negative reinforcement conditions that are traditionally assumed to lead to the opposite outcome (i.e. learned helplessness). The experiments reported in this paper test the generality of these effects in two different tasks and under different conditions of percentage (75% vs. 25%) and distribution (random vs. last-trials) of negative reinforcement (escape from uncontrollable noise). All three experiments obtained superstitious behaviour and illusion of control and question the generality of learned helplessness as a consequence of exposing humans to uncontrollable outcomes.
Associative and statistical theories of causal and predictive learning make opposite predictions for situations in which the most recent information contradicts the information provided by older trials (e.g., acquisition followed by extinction). Associative theories predict that people will rely on the most recent information to best adapt their behavior to the changing environment. Statistical theories predict that people will integrate what they have learned in the two phases. The results of this study showed one or the other effect as a function of response mode (trial by trial vs. global), type of question (contiguity, causality, or predictiveness), and postacquisition instructions. That is, participants are able to give either an integrative judgment, or a judgment that relies on recent information as a function of test demands. The authors concluded that any model must allow for flexible use of information once it has been acquired.
The learned helplessness hypothesis is criticized and reformulated. The old hypothesis, when applied to learned helplessness in humans, has two major problems: (a) It does not distinguish between cases in which outcomes are uncontrollable for all people and cases in which they are uncontrollable only for some people (univervsal vs. personal helplessness), and (b) it does not explain when helplessness is general and when specific, or when chronic and when acute. A reformulation based on a revision of attribution theory is proposed to resolve these inadequacies. According to the reformulation, once people perceive noncontingency, they attribute their helplessness to a cause. This cause can be stable or unstable, global or specific, and internal or external. The attribution chosen influences whether expectation of future helplessness will be chronic or acute, broad or narrow, and whether helplessness will lower self-esteem or not. The implications of this reformulation of human helplessness for the learned helplessness model of depression are outlined.
One hundred and fifty participants played a computer task in which points were either gained (reinforcement) or lost (punishment) randomly on 75%, 50%, or 25% of trials. Despite the noncontingent nature of the task, participants frequently suggested superstitious rules by which points were either gained or lost. Rules were more likely to be suggested and supported higher confidence ratings under conditions of maximal reinforcement or minimal punishment, and participants gaining points tended to express more rules than did those losing points. Superstitious rule generation was in no way related to a person's locus of control, as measured by Rotter's Internal-External Scale. Participants losing points were more accurate in keeping track of their total number of points than were participants gaining points. Results are discussed in terms of reinforcement and punishment's effects on the stimulus control of rule-governed behavior, and comparisons are drawn with the illusion of control and learned helplessness literature.