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Illusions of causality occur when people develop the belief that there is a causal connection between two events that are actually unrelated. Such illusions have been proposed to underlie pseudoscience and superstitious thinking, sometimes leading to disastrous consequences in relation to critical life areas, such as health, finances, and wellbeing. Like optical illusions, they can occur for anyone under well-known conditions. Scientific thinking is the best possible safeguard against them, but it does not come intuitively and needs to be taught. Teaching how to think scientifically should benefit from better understanding of the illusion of causality. In this article, we review experiments that our group has conducted on the illusion of causality during the last 20 years. We discuss how research on the illusion of causality can contribute to the teaching of scientific thinking and how scientific thinking can reduce illusion.
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published: 02 July 2015
doi: 10.3389/fpsyg.2015.00888
Edited by:
Rodney M. Schmaltz,
MacEwan University, Canada
Reviewed by:
Thomas J. Lundy,
Virtuallaboratory.Net, Inc., USA
Ana Miranda,
Universidad de Valencia, Spain
Helena Matute,
Departamento de Fundamentos
y Métodos de la Psicología,
Universidad de Deusto, Apartado 1,
48080 Bilbao, Spain
Specialty section:
This article was submitted to
Educational Psychology,
a section of the journal
Frontiers in Psychology
Received: 01 April 2015
Accepted: 15 June 2015
Published: 02 July 2015
Matute H, Blanco F, Yarritu I,
Díaz-Lago M, Vadillo MA and Barberia
I (2015) Illusions of causality:
how they bias our everyday thinking
and how they could be reduced.
Front. Psychol. 6:888.
doi: 10.3389/fpsyg.2015.00888
Illusions of causality: how they bias
our everyday thinking and how they
could be reduced
Helena Matute 1*, Fernando Blanco 1, Ion Yarritu 1, Marcos Díaz-Lago 1, Miguel A. Vadillo 2
and Itxaso Barberia 3,4
1Departamento de Fundamentos y Métodos de la Psicología, Universidad de Deusto, Bilbao, Spain, 2Primary Care and
Public Health Sciences, King’s College London, London, UK, 3Departamento de Psicología Básica, Universitat de Barcelona,
Barcelona, Spain, 4EventLab, Departamento de Personalidad, Evaluación y Tratamiento Psicológico, Universitat de
Barcelona, Barcelona, Spain
Illusions of causality occur when people develop the belief that there is a causal
connection between two events that are actually unrelated. Such illusions have been
proposed to underlie pseudoscience and superstitious thinking, sometimes leading to
disastrous consequences in relation to critical life areas, such as health, finances, and
wellbeing. Like optical illusions, they can occur for anyone under well-known conditions.
Scientific thinking is the best possible safeguard against them, but it does not come
intuitively and needs to be taught. Teaching how to think scientifically should benefit from
better understanding of the illusion of causality. In this article, we review experiments that
our group has conducted on the illusion of causality during the last 20 years. We discuss
how research on the illusion of causality can contribute to the teaching of scientific thinking
and how scientific thinking can reduce illusion.
Keywords: causal learning, cognitive biases, contingency judgment, illusion of causality, illusion of control, science
teaching, scientific methods, scientific thinking
In today’s world, there is a growing tendency to trust personal beliefs, superstitions, and
pseudoscience more than scientific evidence (Lewandowsky et al., 2012; Schmaltz and
Lilienfeld, 2014; Achenbach, 2015; Carroll, 2015; Haberman, 2015). Superstitious, magical,
and pseudoscientific thinking refer to ungrounded beliefs that are not supported by current
evidence (Lindeman and Svedholm, 2012). Many of them involve causal illusions, which are the
perception of a causal relationship between events that are actually unrelated. Examples of causal
illusions can easily be found in many important areas of everyday life, including economics,
education, politics, and health. Indeed, causal illusions and related cognitive biases such as
overconfidence, the illusion of control, and illusory correlations have been suggested as the basis
of financial bubbles (e.g., Malmendier and Tate, 2005), social stereotypes (Hamilton and Gifford,
1976; Crocker, 1981; Murphy et al., 2011), hostile driving behavior (Stephens and Ohtsuka, 2014),
social intolerance and war (Johnson, 2004; Lilienfeld et al., 2009), and public health problems such
as the increased popularity of alternative and complementary medicine (Matute et al., 2011; Blanco
et al., 2014).
For example, many reports have shown that homeopathy has no causal effect on patient health
other than a placebo effect (Shang et al., 2005; Singh and Ernst, 2008; Ernst, 2015; National Health
and Medical Research Council, 2015). Even so, 34% of Europeans believe that homeopathy is
effective (European Commission, 2005). The illusion of causality in this case arises from very simple
Frontiers in Psychology | July 2015 | Volume 6 | Article 8881
Matute et al. Scientific thinking and the illusion of causality
intuitions based on coincidences: “I take the pill. I happen to
feel better. Therefore, it works.” Many people in today’s world
have come to believe that alternative medicine is effective and
that many practices not supported by evidence are reliable simply
because “they work for them,” as they put it. That is, they feel as
if recovery was caused by the treatment (Lilienfeld et al., 2014).
Some people go even further and prefer alternative medicine over
scientific medicine. This attitude is causing serious problems for
many people, sometimes even death (Freckelton, 2012).
Despite the effort of governments and skeptical organizations
to promote a knowledge-based society, the effectiveness of such
campaigns has been limited at best (Schwarz et al., 2007; Nyhan
and Reifler, 2015). Two in five Europeans are superstitious
(European Commission, 2010), and more than 35% of Americans
believe in haunted houses or extrasensory perception (Moore,
2005). Superstitious and pseudoscientific thinking appear to be
increasing in several European countries (while not changing in
others; see European Commission, 2010). This type of thinking
often guides health, financial, and family decisions that should be
guided by contrasted knowledge and empirical evidence. In the
UK, the House of Commons Science and Technology Committee
(2010) complained in a recent report that even though the UK
Government acknowledges that “there is no credible evidence
of efficacy for homeopathy” (p. 42), the UK Government is still
funding homeopathy and providing licenses that allow for retail in
pharmacies, so that “the Government runs the risk of endorsing
homeopathy as an efficacious system of medicine” (p. 42). A
similar situation can be found in most countries. In Australia, the
National Health and Medical Research Council (2015) recently
published a comprehensive report that warns people that “there
are no health conditions for which there is reliable evidence that
homeopathy is effective” (p. 6). This report goes one step further
and attempts not only to provide evidence, but importantly, to
educate people on why personal beliefs and experiences are not
a reliable source of knowledge and why scientific methods should
always be used when assessing causality.
In the age of the Internet, when both science and pseudoscience
are at a clicks distance, many people do not know what to believe
anymore. Rejection of science seems to be growing worldwide
(Achenbach, 2015), and it is becoming increasingly difficult to
eradicate myths once they start to spread (Lewandowsky et al.,
2012; Schmaltz and Lilienfeld, 2014). There are many possible
reasons why people are rejecting science. Finding evidence-
based strategies to counteract them should become a priority
if we aim to create efficient public campaigns and policies that
may change scientific education (Schwarz et al., 2007; Gough
et al., 2011). As noted by Bloom and Weisberg (2007), resistance
to science is becoming particularly intense in societies where
pseudoscientific views are transmitted by trustworthy people.
Unfortunately, scientists are not always regarded as the most
trustworthy source in society because people often trust friends
and family more (Eiser et al., 2009). An excellent example is
the anti-vaccine crisis in 2015 and the fact that mainly rich and
well-educated parents are deciding not to vaccinate their children
despite scientific and governmental alarms (Carroll, 2015).
One of the main difficulties is that showing people the facts
is not enough to eradicate false beliefs (e.g., Yarritu and Matute,
2015). This can even sometimes have the opposite effect of
strengthening the myth (Lewandowsky et al., 2012; Nyhan and
Reifler, 2015). Moreover, teaching scientific methods does not
seem to be enough either (Willingham, 2007; Schmaltz and
Lilienfeld, 2014), as many naïve preconceptions may survive even
after extended scientific training (Shtulman and Valcarcel, 2012).
However, learning how to think scientifically and how to use
scientific methods should at least provide an important safeguard
against cognitive biases (Lilienfeld et al., 2012; Barberia et al.,
2013). Indeed, we suggest that scientific methods constitute the
best possible tool, if not the only one, that has been developed to
counteract the illusion of causality that can be found at the heart
of many of those problems. But scientific thinking and scientific
methods are not intuitive and need to be taught and practiced. A
better understanding of how the illusion of causality works should
cast light on how to improve the teaching of scientific thinking
and make it more efficient at reducing the illusion. In this article,
we review the experiments that our group has conducted on the
illusion of causality and discuss how this research can inform the
teaching of scientific thinking.
Illusions of Causality, Contingency, and
Scientific Methods
Just as there are optical illusions that make people see things larger
than they are or with different shapes or colors, there are illusions
of causality that make people perceive that one event causes
another when there is just a coincidence between them. Indeed,
just as people have no way of assessing size or weight accurately
without using specially designed tools (such as measuring tapes
or balances), they are likewise not prepared to assess causality
without tools. Scientific methods, particularly the experimental
approach, are the external tools that society has developed to
assess causality.
It could be argued that people often infer size, color, or weight
intuitively, and it is true that they can often do so relatively well
even in the absence of any external help. However, we all know that
people make errors. When facing an important or critical situation
in life, such as buying a house or deciding where to land an aircraft,
most people will not trust their intuitions but will rely on external
aids such as measuring tapes or Global Position System (GPS).
By the same reasoning, when trying to assess causality intuitively,
people can often be relatively accurate, as demonstrated in many
experiments (e.g., Ward and Jenkins, 1965; Peterson, 1980; Shanks
and Dickinson, 1987; Allan, 1993; Wasserman et al., 1993). At
the same time, however, it has also been shown in countless
experiments that under certain well-known conditions, people
can make blatant errors when judging causal relations with the
naked eye (Alloy and Abramson, 1979; Allan and Jenkins, 1983;
Lagnado and Sloman, 2006; Msetfi et al., 2007; Hannah and
Beneteau, 2009; Blanco et al., 2014). Even scientists, who are used
to thinking critically and applying rigorous methodologies in their
work, have sometimes been found to develop superstitions in
their daily life (Wiseman and Watt, 2006; Hutson, 2015) and to
forget that causality cannot be accurately assessed based on quick
intuitions (Kelemen et al., 2013; Phua and Tan, 2013).
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Matute et al. Scientific thinking and the illusion of causality
Examples of intuitive assessment of causality (sometimes
correct, sometimes biased) can be found easily in everyday life.
A company could initiate a new training program, attract more
clients, and assume that the new program was effective. Upon
carrying a good-luck charm and playing a fantastic game, one
cannot avoid feeling that the charm had a critical role in victory.
This tendency to detect causal relationships is so strong that
people infer them even when they are rationally convinced that
the causal mechanism that would make the relationship plausible
does not exist. As another example, your favorite sports team
might lose the game just as you go to the kitchen for a moment.
Despite knowing that a causal relation does not exist between
your behavior and the outcome of the game, feeling as if you were
somewhat responsible for that failure can be hard to avoid (Pronin
et al., 2006).
To counteract the illusion of causality, it is essential to
understand that the illusion is not a matter of intelligence or
personality (Wiseman and Watt, 2006). Illusions of causality can
occur for anyone, just like visual illusions. They occur because of
the way the human mind has evolved: It extracts causality from
coincidences. Thus, counteracting the illusion is a matter of being
able to use the right tools and knowing when and how to use
them. We cannot think of a better safeguard against the illusions
of causality than scientific thinking, which involves skepticism,
doubt, and rigorously applying scientific methods, particularly the
experimental approach.
The basic idea in causal judgment research is that people often
need to infer whether a relationship is causal through observing
ambiguous cases and incomplete evidence. In the simplest causal
learning situations, there are two events—a potential cause and an
outcome—that can be repeatedly paired over a series of trials. Four
different types of trials can result from the possible combinations
of the presence or absence of these two binary events. Both the
potential cause and the outcome can occur (type atrials), the
cause may occur while the outcome does not (type btrials), the
cause may not occur and the outcome still occurs (type ctrials),
and finally, neither the cause nor the outcome may occur (type d
trials). These four trial types are shown in the contingency matrix
in Table 1.
The pindex (Allan, 1980) has been broadly accepted as
a normative measure of contingency on which participants’
subjective estimations of causality should be based (e.g., Jenkins
and Ward, 1965; Shaklee and Mims, 1981; Allan and Jenkins,
1983; Shanks and Dickinson, 1987; Cheng and Novick, 1992). This
index is computed as the probability that the outcome occurs in
the presence of the potential cause, P(O|C), minus the probability
that it occurs in its absence, P(O|¬C). These probabilities can
easily be computed from the number of trials of each type (a, b, c,
d) in Table 1:
p=P(O|C)P(O|¬C) = [a/(a+b)] [c/(c+d)].
When an outcomes probability of occurrence in the presence
of a cause is larger than that without the cause, pis positive.
This means that the potential cause contributes to producing the
outcome. For instance, if the probability of recovery from a given
disease is larger when people take a given pill than when they
TABLE 1 | Contingency matrix showing the four different types of trials as
a function of whether or not the cause and the outcome are present.
Outcome present Outcome absent
Cause present a b
Cause absent c d
TABLE 2 | Contingency matrix showing a situation in which the outcome
occurs with high probability but with no contingency.
Outcome present Outcome absent
Cause present 80 20
Cause absent 80 20
For instance, 80% of the patients who took a pill recovered from a disease, but 80% of
patients who did not take the pill recovered just as well.
do not, a causal relationship is suggested and the pill probably
promotes recovery.
When an outcomes probability without the cause is larger than
that with the cause, pis negative. This means that there is a
causal relationship, but in this case, the cause does not generate
the outcome but prevents or inhibits it. For instance, when the
probability of recovery is lower when people take a given pill than
when they do not take it, there is an inhibitory or preventive causal
relationship: the pill is probably preventing recovery.
Most interesting for our present purposes are the cases in which
the contingency is null, meaning the causal relationship does
not exist. Table 2 shows an example of a fictitious situation in
which 80% of the patients who take a given pill recover from a
disease, but 80% of patients who do not take it recover just as
well. Thus, the outcome is highly probable but does not depend on
the presence or absence of the potential cause. In this and other
cases, the two probabilities are identical and pis therefore 0.
In this 0-contingency situation, there is no empirical evidence for
assuming that a causal link exists between the potential cause and
the effect. Therefore, if a number of experimental participants are
shown such a situation and asked to provide a causal judgment,
the correct answer should be that there is no causal relation,
even though the outcome might occur very frequently and the
two events might coincide often (i.e., there might be many cell a
instances; see Table 2).
Experimental participants often overestimate the degree to
which the potential cause is actually causing the outcome in null-
contingency conditions. This is known as the illusion of causality
(or the illusion of control in cases where the potential cause is the
behavior of the participant). Even though pis generally accepted
as a normative index that describes the influence of the cause over
the effect, it is now well known that participants do not always
base their estimation of causality on p(Allan and Jenkins, 1983;
Pineño and Miller, 2007). Participants will sometimes use other
indexes, and most importantly for our present purposes, their
estimations can be biased by other non-normative variables. These
variables are the focus of the present report.1
1Note that all the situations described hereafter involve neither correlation nor
causation. They should not be confounded with situations leading to the well-
known cum hoc ergo propter hoc bias, in which people assume that there is
causation when only a correlation exists.
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Matute et al. Scientific thinking and the illusion of causality
In a way, the correct and unbiased detection of whether a causal
relationship exists in any situation is equivalent to using a rigorous
experimental approach. It requires awareness that human causal
inferences are subject to biases, testing what happens when the
potential cause is not present, observing a similar number of cases
in which the cause is present and absent, being skeptical about
mere coincidences, and seeking complete information on all four
trial types. It requires knowing when and how to use all these tools.
It is important to show people the basic principles of scientific
control, as we will see in the experiments studying the illusion of
causality described below.
How to Assess the Illusion
Most of the experiments on how people detect real or illusory
causal relationships have used a variation of the same procedure:
the contingency judgment task (see special volumes by Shanks
et al., 1996; Beckers et al., 2007). This assessment procedure
is now relatively standard, which facilitates comparisons across
experiments and the evaluation of new applied strategies. This
methodology has also been used when there is a need to accurately
estimate the degree of illusion of causality that people show before
and after receiving training on scientific thinking (Barberia et al.,
2013), which is of particular interest for the present report.
In these experiments, participants are exposed to a number
of trials in which a given cause is present or absent, followed by
the presence or absence of a potential outcome (see Table 1). A
typical experiment on the illusion of causality uses a contingency
matrix similar to that in Table 2 with a null-contingency situation
and manipulation of the different cells to increase or reduce the
The cover story used in the experiments may be changed to
apply to medicine and health (e.g., Matute et al., 2011), stocks
and markets (Chapman and Robbins, 1990), foods and allergic
reactions (Wasserman et al., 1996), or plants and poisons (Cobos
et al., 2007), to name a few examples. In all cases, the aim
is to explore how certain manipulations influence the accurate
detection of causality. At the end of the experiment, participants
are asked to judge the relationship between the potential cause and
the potential outcome.
For example, a typical experiment may prompt participants
to imagine they are medical doctors. Participants are shown a
series of records of fictitious patients suffering from a disease.
They see one patient per trial on a computer monitor. Some of
these patients take a drug and some do not. Then, some of the
patients recover while others do not. In this example, the drug is
the potential cause, which might be present or absent in each trial,
and the outcome is the recovery from the disease, which might
also be present or absent in each trial. The different trial types (a, b,
c, d) shown in Table 1 are presented in random order. The number
of trials (i.e., patients) would usually be between 20 and 100, with
40 to 50 being standard. At the end of the experiment, participants
are asked to provide their personal estimation of the relationship
between the two events, typically on a scale from 0 (non-effective)
to 100 (totally effective).
Among the many possible variations of this task, there is one
that deserves special mention. This variable is the active vs.
passive role of the participant in this task. In the description
of the task so far, participants could passively observe whether
the fictitious patients took the drug and then observe whether
or not they recovered. This is analogous to vicarious learning
by observing or reading about others who have taken the drug.
By contrast, in experiments using the active version of the task,
participants are shown patients who suffer from the syndrome
and are asked, “Would you like to give the drug to this patient?”
In these experiments, participants play an active role and decide
whether and when the potential cause is presented. After that,
the outcome (healing) occurs or does not according to a random
or a predetermined sequence that has been pre-programmed by
the experimenter. This is an analog of a person who takes a pill
to reduce pain. As we will show, some studies have attributed
a critical role to this variable, but we argue its effect might
sometimes have been confounded with other factors.
In addition to active vs. passive roles of participants, there
are many other variants that can be introduced in this task and
that have been shown to affect the participants’ estimations of
causality. Examples include changing the wording of questions
asked at the end of the experiment about the causal relationship
(Crocker, 1982; Vadillo et al., 2005, 2011; Collins and Shanks,
2006; De Houwer et al., 2007; Blanco et al., 2010; Shou and
Smithson, 2015), the order in which the different trial types
are presented (Langer and Roth, 1975; López et al., 1998), the
frequency with which judgments are requested (Collins and
Shanks, 2002; Matute et al., 2002), the description of the relevant
events as causes, predictors, or effects (Waldmann and Holyoak,
1992; Cobos et al., 2002; Pineño et al., 2005), the temporal
contiguity between the two events (e.g., Shanks et al., 1989;
Wasserman, 1990; Lagnado and Sloman, 2006; Lagnado et al.,
2007), and many other variables that fortunately are becoming well
known. In the following sections, we will focus on the variables
that seem to affect the illusion most critically in cases of null
The Probability of the Outcome
One of the first variables found to affect the overestimation of
null contingency is the probability of the outcomes occurrence.
Many null-contingency experiments have examined conditions in
which a desired outcome occurs by mere chance but with a high
probability (e.g., 75% of the occasions, regardless of whether or
not the cause is present), which were compared with conditions
in which the outcome occurs with low probability (e.g., 25% of
the occasions, also independently of the causes presence). The
illusion of a causal relationship is systematically stronger in the
high-outcome conditions than in the low-outcome conditions
(Alloy and Abramson, 1979; Allan and Jenkins, 1980, 1983;
Matute, 1995; Wasserman et al., 1996; Buehner et al., 2003; Allan
et al., 2005; Musca et al., 2010). Thus, when someone is trying to
obtain an outcome and the outcome occurs frequently, the feeling
that the action is being effective is much stronger than when the
outcome occurs rarely. This is usually called outcome-density or
outcome-frequency bias.
Knowing that the probability of the outcome affects the
perception of causality is important. This knowledge alerts us
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Matute et al. Scientific thinking and the illusion of causality
to conditions that are often more sensitive to causal illusions,
such as any disease or pain condition in which spontaneous
remissions are frequent. This explains why some life situations are
particularly vulnerable to pseudoscientific and magical thinking.
For instance, in the USA, alternative medicine is a preferred
treatment option for back pain (which has a high percentage of
spontaneous remission), with alternative practitioners providing
40% of primary care for back pain (White House Commission
on Complementary and Alternative Medicine Policy, 2002). In
contrast, alternative medicines are only seldom used to treat
disorders where the likelihood of spontaneous remission is low.
Outcome-density bias allows us to predict that null-
contingency conditions in which the desired outcome is
frequent are susceptible to producing causal illusions. However,
it is difficult to prevent those illusions, given that there is little we
can do to reduce the probability of the outcome in those cases.
In applied settings, the probability of response-independent
outcomes occurring is by definition beyond an individual’s
control. We can do nothing to change it, so our role should be to
raise awareness of the problem and teach people to be vigilant
to detect their own illusions in cases where the outcome occurs
frequently. A good habit of scientific thinking should therefore
be the best defense.
The Probability of the Cause
The probability of the cause is another variable that has been
shown to influence the illusion of causality. With all other things
being equal and given the null contingency, the illusion of a
cause producing an outcome will be significantly stronger if the
cause occurs in 75% of the occasions than when it occurs in 25%.
This effect is also called the cause-density or cause-frequency
bias and has also been shown in many experiments (Allan and
Jenkins, 1983; Wasserman et al., 1996; Perales et al., 2005; Matute
et al., 2011; Vadillo et al., 2011; Blanco et al., 2013; Yarritu et al.,
2014). The effect is particularly strong when the probability of the
outcome is high as well, since there will be more opportunities for
coincidences (Blanco et al., 2013).
Fortunately, much can be done to reduce people’s illusions
in this case. Even though the probability of the outcome is
uncontrollable in real-life situations of null contingency, the
probability of the cause is something that can be modified
relatively easily. Imagine a popular (and bogus) treatment for a
pain condition that has a high outcome probability of spontaneous
remissions. As is usually the case in alternative medicine, the
treatment involves a very high frequency of the cause (e.g., the pills
have to be taken every 2 h). Therefore, we know that this is one
of the situations in which the illusion will almost certainly occur.
As discussed, it is very difficult to just convince people that their
beliefs are false or that their favorite treatment that seems to work
so well is innocuous. Importantly, however, we might be able to
convince them to reduce the frequency with which they take the
pill so that they can at least test what happens when the cause is
absent. We know that this strategy works well in the laboratory. If
they reduce the cause’s frequency and the outcome persists, then
they can realize that the potential cause and outcome are totally
independent, and the illusion will be reduced (Blanco et al., 2012;
Yarritu et al., 2014). As we will show, this might be done through
simple instructions or through any other means that reduce the
probability of the potential cause’s occurrence.
Thus, even though it might be difficult to convince people
not to use a bogus treatment, we at least know that a good
campaign that motivates reduction of treatment frequency can
reduce the illusion. Indeed, it has been shown that it is not even
necessary that the participants themselves perform the action. In
experiments using passive exposure, participants simply observed
what happened to fictitious patients in the experiment, as if
passively watching television. The results showed that even though
participants did not perform the action themselves, observing
many vs. few cases in which the cause was present had a significant
effect on the illusion that they developed. Those observing fewer
patients who followed the treatment reported significantly weaker
illusions (e.g., Matute et al., 2011; Vadillo et al., 2011; Yarritu
et al., 2014). Thus, there is not only a tendency to use with greater
frequency those treatments that seem to be more effective, but also
a tendency to perceive more frequently used treatments as more
effective. That is, the perception of effectiveness increases usage,
but increased usage increases the perception of effectiveness as
well (Blanco et al., 2014). Therefore, to show people that a causal
relationship does not exist (e.g., that homeopathy does not work),
it suffices to ask them to use the potential cause less frequently, or
even to show them what happens to people who are not using it (by
showing them how people who do not use homeopathy recover
just the same, for example).
Cause-Outcome Coincidences
One of the conditions where most people make systematic errors
in estimating causality is when two events keep occurring together
in time or in close temporal succession, suggesting that the
first one is causing the second one (e.g., Shanks et al., 1989;
Wasserman, 1990; Lagnado and Sloman, 2006). We infer causality
in those cases, and the inference is often correct, as cause-outcome
contiguity is the main cue that humans and other animals can
use to infer causal relationships (Wasserman and Neunaber, 1986;
Shanks et al., 1989; Wasserman, 1990; Buehner, 2005; Greville and
Buehner, 2010). However, when there is no causal relationship
and the events still occur in close temporal succession, people will
also tend to infer that a causal relationship exists between them,
albeit erroneously.
Indeed, of all four trial types in the contingency matrix, people
tend to give special weight to trials in cell a, for which the cause
and the outcome coincide (Jenkins and Ward, 1965; Crocker,
1982; Kao and Wasserman, 1993). This means that even in null-
contingency situations, there might be a strong tendency to
infer a causal relationship when the number of cause-outcome
coincidences (cell atrials) is high. In fact, cell ais closely related
to the factors discussed in the previous two sections, as a high
probability of both the cause and the outcome will inevitably
produce many cell atrials. Not surprisingly, marketing campaigns
promoting the use of complementary and alternative medicine or
any other miracle products make use of this technique by always
informing potential customers about the many successful cases
after following their advice, while never mentioning those who
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Matute et al. Scientific thinking and the illusion of causality
FIGURE 1 | Results of a computer simulation of the four experimental
conditions presented by Blanco et al. (2013, Experiment 1) using the
Rescorla–Wagner learning algorithm. The simulation was conducted
using the Java simulator developed by Alonso et al. (2012). For this
simulation, the learning rate parameters were set to αcause =0.3,
αcontext =0.1, βoutcome =βoutcome =0.8.
succeeded without following their advice. The lesson revealed
by laboratory experiments is that informative campaigns from
governments and health institutions trying to counteract the
advertisement of fraudulent products clearly need to highlight
those who did not use the product (i.e., cause-absent trials—cells
cand din Table 1). It is important to transmit not only truthful
but also complete information, including all four trial types (a, b,
c, d), so that people can make informed decisions.
In sum, the probabilities of both the outcome and the cause
are variables that influence the degree of illusion of causality that
people will develop in null-contingency conditions. The higher
the probabilities of the cause and the outcome, the higher the
probability will be that the cause and outcome coincide and
the higher the probability of people inferring a causal relation.
Moreover, when the probabilities of both the outcome and the
cause are high, a special situation occurs in which a majority of the
trials involve cell aobservations. The many coincidences between
cause and outcome when both occur frequently seem to give rise
to the illusion of causality (Blanco et al., 2013). Thus, to reduce the
illusion, we should reduce the probabilities of the outcome and the
cause whenever possible and seek for complete information.
The effects discussed so far do not seem to bias only human
judgments. Computer simulations show that the probabilities
of the cause and the outcome can also bias machine-learning
algorithms designed to detect contingencies. For example,
Figure 1 shows the results of a computer simulation based on
the popular Rescorla–Wagner learning algorithm (Rescorla and
Wagner, 1972). The model tries to associate causes and outcomes
co-occurring in an environment while minimizing prediction
errors. Each of the four lines shown in Figure 1 denotes the
behavior of the model when exposed to each of the four conditions
used by Blanco et al. (2013). In this experiment, all participants
were exposed to a sequence of trials where the contingency
between a potential cause and an outcome was actually 0. The
probability of the cause was high (0.80) for half of the participants
and low (0.20) for the other half. Orthogonally, the outcome
tended to appear with a large probability (0.80) for half of the
participants and with low probability (0.20) for the other half.
As shown in Figure 1, the model tends to converge to a low
associative strength in all conditions after many trials, a natural
result given that the true contingency between cause and outcome
is 0 in all cases. However, before the model reaches that asymptote,
it is temporarily biased by both the probability of the cause and
the probability of the outcome in a way similar to that shown
in humans. The model’s ability to learn the 0 contingency is
slowest when the probabilities of the cause and outcome are both
large. Although the model predicts that the illusion will eventually
disappear if a sufficiently large number of trials is provided, the
results mimic the causal illusions found in real participants, who
also tended to show stronger illusions when both probabilities
are large. This has been found in humans using both 50 and
100 training trials (Blanco et al., 2011). This result could also
be regarded as a special form of illusory correlation, because
both people and the model infer an illusory correlation between
the potential cause and the outcome before assuming that the
correlation implies a causal relationship.
Interestingly, although there are many other variables that have
been described in the literature as influencing the illusion of
causality, a close look reveals that many also involve situations
with high probability of the outcome, high probability of the cause,
or both. Therefore, many of the factors supposedly influencing the
illusion might just be instances where these two probabilities are
affected. We describe some of these variables below.
Maximizing the Outcome vs. Testing the
Causal Relationship
As explained, some experiments have used a passive version of
the contingency learning task, while others have used an active
version. In the active version, the participant’s behavior is the
potential cause of the outcome: the participant decides when and
how often the cause is presented. Therefore, even though the
outcomes occurrence is preprogrammed in the same way in both
the active and passive versions, in the active task, participants
might increase the number of coincidences between the potential
cause and the outcome by chance as a function of when and
how often they introduce the cause (Matute, 1996; Hannah
and Beneteau, 2009). Thus, the participant could influence the
frequencies in the contingency matrix (see Table 1). In these
cases, even though the outcome is uncontrollable, the participant
may feel as if she were controlling it (Blanco et al., 2011).
This is a general feature of instrumental behavior and occurs
in real-life uncontrollable situations where the individual is free
to introduce the cause. As an example, consider rain dancing.
Rain is uncontrollable, but the frequency with which ancient
tribes danced was a decision that affected the number of cause-
outcome coincidences, which in turn could affect their perception
of causality.
This effect depends on the goal and behavior of the participants,
so it can also be at least partly modified through instructions
to change those goals and the behavior associated with them.
Matute (1996) observed that experimental participants showing
an illusion of causality were typically exposed to naturalistic-like
settings in which their goal was to maximize the outcome, so they
tended to act with high probability (of the cause). By contrast,
studies reporting accurate detection of null contingencies tended
to tell their participants not only that their goal was to assess how
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Matute et al. Scientific thinking and the illusion of causality
much control they had over the outcome (rather than maximizing
the outcome), but also how they could best assess the potential
relationship. Participants in those experiments were asked to
test whether a causal relationship existed and were instructed
that the best way to attain that goal was to introduce the cause
in about 50% of the trials. This allowed them to learn what
happened when the potential cause was present and when it was
not (Shanks and Dickinson, 1987; Wasserman, 1990). In a way, the
participants were instructed in the principles of scientific control.
By prompting them to test the outcomes in the presence and
absence of a potential cause, they accurately detected the absence
of a contingency. This suggests that accurate detection can be
promoted through direct (and rather simple) instructions given
to participants.
People tend to act frequently when trying to maximize an
outcome, which increases their illusion of causality. Instructing
people to reduce the probability of the cause (and refrain from
acting on every occasion) shows to be an effective strategy to
reduce their illusion (Blanco et al., 2012). This strategy teaches
people about what happens when they do or do not act. Not
surprisingly, this is equivalent to teaching people the basis
of scientific methods and scientific control: to test a causal
relationship between two variables, they need to learn to control
these variables and see what happens in the presence or absence
of the potential cause.
The Cost of Action—Secondary Effects
We have described a few conditions in which the probability
of the cause is explicitly reduced so that people may realize
what happens when the cause is absent. As mentioned, this is
the basis of the experimental method, which was developed to
help scientists perceive causal relationships with accuracy. But
in many naturalistic conditions, people tend to act with a very
high rate to maximize the outcome, which prevents them from
detecting null contingencies. There are many examples. When in
pain, we will be more prone to accept any remedy that might be
offered. It would be very hard, if not impossible, to ask people
to refrain from trying to achieve a desired and truly needed
There are, however, some factors that by default reduce the
probability of action. One is the cost of the action. When an action
is expensive (in terms of energy or other costs), people reduce its
frequency. Under those conditions, we can predict a reduction in
the illusion of causality. One example is provided by the secondary
effects of medication. Most drugs produce secondary effects, and
taking them has a cost. According to the discussion thus far, this
should lead to reduced intake and thus more accurate detection
of the null contingency. By the same reasoning, if a drug is not
effective but has secondary effects, people should detect its null
contingency more easily. However, specious medications that are
not effective tend to have neither primary nor secondary effects (as
with many complementary medicines such as homeopathy). This
might be one of the reasons why many people prefer alternative
medicine even when they know that they are not supported by
evidence. The presumed lack of side effects makes many people
take those medications freely and often, which should lead to a
greater illusion that they are effective, as the high probability of
the cause is known to increase the illusion.
To test this view, Blanco et al. (2014) adapted the standard
contingency learning task described above. Participants were
shown records of fictitious patients one by one through a
computer screen and decided whether to give these patients a
newly discovered drug or to do nothing. They received feedback
on whether each patient felt better or not. As in previous
experiments, there was no relationship between the drug and
healing. The critical manipulation in this experiment was that
for one group of participants, patients who took the drug always
developed secondary effects, whereas there were no secondary
effects for the other group. As expected, the participants for
whom the drug produced no secondary effects administered it
with greater frequency and were therefore not able to learn that
recovery occurred with identical probability regardless of drug
administration. Thus, this higher frequency of administration
produced stronger causal illusions in the group with no secondary
effects. The study demonstrated that not only do we tend to use
medicines that we believe to be more effective, but the simple
fact of using a medicine frequently promotes the belief that it
is effective. This generates a vicious cycle between usage and
perception of effectiveness.
These results suggest that one of the paths by which alternative
and complementary medicine is becoming so widespread is
precisely through their presumed lack of secondary effects. This
makes people use these remedies more often than conventional
ones (which almost always include secondary effects). Thus, if the
outcome occurs with the same probability when not using those
remedies, they simply cannot perceive it.
Another variable shown to have a profound effect on the
perception of null contingencies is mood. A classic study by Alloy
and Abramson (1979) showed that people who are depressed are
more accurate than non-depressed individuals in detecting their
absence of control in non-contingent conditions. This depressive
realism effect has been replicated under many different conditions
(e.g., Moore and Fresco, 2012; Kornbrot et al., 2013; Byrom et al.,
Initially, this effect was explained by assuming that depressed
people lack a series of self-serving biases that are assumed to
promote well-being in non-depressed people. Non-depressed
people would tend to feel that they have control over their
environment, which makes them feel well and protects them from
depression (Alloy and Abramson, 1988; Taylor and Brown, 1988,
1994; Alloy and Clements, 1992). An alternative view interprets
these effects not as a motivational bias, but as the consequence of
different cognitive strategies. More specifically, depressed people
have been found to differ in how they process cause-absent trials
(Msetfi et al., 2005).
Without discussing the merits of these interpretations, we
propose another interpretation that is in line with the general
framework outlined in this article and which might complement
previous proposals on depressive realism. According to Blanco
et al. (2009, 2012), one aspect of depression is greater passivity,
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Matute et al. Scientific thinking and the illusion of causality
that is, a reduced ability to initiate voluntary responses. When
depressed and non-depressed participants have been compared in
experiments on depressive realism, the frequency of occurrence
of the cause (the participant’s behavior) has usually not been
reported. Thus, non-depressed participants could be acting with
greater frequency than depressed participants to obtain the
outcome. If this were true, and assuming that the outcome does
occur with relatively high frequency in this type of experiment and
real-life situations, we can easily anticipate that non-depressed
participants will be exposed to a higher number of coincidences
(type acells, Table 1). This makes them develop the illusion that
they are controlling the outcome. In fact, Blanco et al. (2009, 2012)
showed that the depressive realism effect can be due at least in part
to the fact that depressed individuals are more passive. They act
with less frequency to obtain the outcome and are thus exposed
to fewer cause-present trials. As a consequence, their illusion of
control is lower.
Therefore, although depressed people seem to be more
accurate than non-depressed people in their estimations of null
contingency, this does not necessarily mean that they are wiser.
Instead, this seems to be an additional consequence of the robust
role that the probability of the cause plays in enhancing or
reducing the illusions of causality. This highlights the need to
teach non-depressed people to be more passive by introducing
potential causes in some trials but not in others so that they can
learn how much control they actually have over an outcome.
Personal Involvement
The degree of personal involvement of a participant has been
proposed in many earlier reports as one of the most critical
variables influencing the results of experiments on illusions of
causality and control. In principle, the results of an experiment
should differ greatly as a function of whether the participants
are personally involved in healing as many fictitious patients
as possible, or if they are just observing whether the fictitious
patients recover after taking the drug. As mentioned, the illusion
of control has been typically interpreted as a form of self-serving
bias (Alloy and Abramson, 1988; Taylor and Brown, 1988, 1994;
Alloy and Clements, 1992). Therefore, it should not occur when
people are just observing causes and effects that have nothing to
do with their own behavior.
One of the very few studies that explicitly tested this view was
conducted by Alloy et al. (1985). For one group, the potential
cause was the participants’ behavior, while the potential cause
was a neutral event for the other. The illusion was stronger when
the potential cause was the participant’s behavior, which seemed
to support the view that personal involvement is necessary to
develop the illusion of control. However, a closer look at that
experiment reveals that the percentage of trials in which the
participants introduced the cause was not reported. This leaves
open the possibility that the participants who were personally
involved might have been acting with a high probability and thus
exposed to a higher probability of the cause than the group of
observers. If that was the case, personal involvement might have
been confounded with the probability of the cause in previous
Thus, we conducted an experiment using a yoked design so that
everything remained constant across groups except for personal
involvement. One group could administer a drug to fictitious
patients, while the other group just observed. The outcome was
uncontrollable (preprogrammed) for all participants, but each
participant in the yoked group observed just an identical sequence
of causes and outcomes generated by a counterpart in the active
group. The result was that both groups showed a similar illusion
of causality. Participants’ judgments did not differ, regardless of
whether they were active or yoked in the experiment (Yarritu
et al., 2014). Instead, participants’ judgments were influenced
by the probability of the potential cause occurring (recall that
the potential cause in this experiment was the administration of
the drug, which coincided with the behavior of the participants
themselves in the active group, whereas it coincided with the
behavior of a third party in the yoked group). Thus, it seemed
that the probability of the cause was a stronger determinant than
personal involvement in the development of the illusion.
To confirm this finding, Yarritu et al. (2014) conducted another
experiment in which they explicitly manipulated the probability
of the potential cause and the degree of personal involvement.
Half of the participants were asked to try to heal as many
patients as possible, while the other half just observed what their
partners were doing. To increase the motivation of the active
participants, they were made aware that their performance was
being monitored by peer participants observing the screen from
a cloned one in an adjacent cubicle. Orthogonally, half of the
participants in each group were given a short supply of the drug
so that they were forced to administer it with low probability to
just a few patients. The other half was given a large supply of the
drug and induced to administer the drug with high frequency. The
results confirmed our previous findings that the probability with
which the drug was given had a stronger effect on judgments than
being personally involved in trying to heal the patients (vs. just
This is not to say that all cases showing that personal
involvement increases the illusion of control can be reduced to
a probability of the cause effect. Quite possibly, however, some
confusion has existed in the literature because people who are
personally involved tend to act more often to obtain the desired
outcome than those who are not involved. The probability of
introducing the potential cause is higher in those involved, and as
previously shown, this can increase the number of coincidences
and therefore the illusion as well. As a consequence, it is necessary
to distance oneself in critical life conditions in which we are
personally involved or to let more objective and neutral observers
help us judge whether a causal relationship exists, because our
intuitions will surely be wrong.
When There are Several Potential Causes
There are times when the occurrence of coincidences is
not enough to strengthen the perception of causality. When
multiple possible causes are presented together and followed
by an outcome, causes tend to compete among themselves
for association with the outcome. Even when there might be
many cause-outcome coincidences, they may not be effective in
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Matute et al. Scientific thinking and the illusion of causality
strengthening the attribution of causality to one of these causes
if the other cause is more intense or had a previous association
with the outcome (Rescorla and Wagner, 1972). These effects of
competition between several potential causes are well known in
both humans and other animals (e.g., Shanks, 2007; Wheeler and
Miller, 2008).
Taking this idea a bit further, we could also predict that in
situations in which the outcome is independent of behavior,
informing people about potential alternative explanations for the
outcome should also reduce their illusion of causality. This idea
was tested by Vadillo et al. (2013), who instructed one of their
groups about a potential alternative explanation for the outcome.
As expected, the group with an alternative explanation to which
the outcome could easily be attributed showed a reduced illusion
of causality compared to the group that received no suggestions
about alternative explanations. Thus, informing people about the
existence of alternative causes can reduce the illusion.
However, the availability of alternative causes is not always
beneficial. There are cases in which people might choose the
wrong cause if two are available. As an example, consider a patient
taking placebo pills for better sleep. A friend tells him that the
pills are just sugar and that he should start a new treatment. He
hesitates but finally decides to follow his friends advice. He visits
a psychologist and starts following an evidence-based treatment.
However, just in case, he does not quit the placebo pills and keeps
using them as complementary therapy. Doing so implies that it
will be impossible for him to tell whether any improvement is
due to the new treatment or to the placebo pills. Even if the new
treatment works, its effect can be wrongly attributed to the old
placebo pills.
Alternative therapies are often used in addition to other
therapies under the belief that they cannot be harmful if used
in this way. Indeed, many people would agree that alternative
therapy might be harmful because it might make people not
follow effective treatments. But when used as a complement, the
alternative seems to be absolutely harmless and almost no one
would oppose this use. However, as we have been advancing,
the availability of more than one potential cause can result in
a competition between both causes, so that if one is considered
to be a strong candidate, the other will be seen as a weak one.
Indeed, many experiments with both humans and other animals
have reported that when two causes are presented together and
followed by an outcome, one of the causes having a previous
history of association with that outcome can compete with the
attribution of causal strength to the most recent cause (Shanks and
Dickinson, 1987; Shanks, 2007; Wheeler and Miller, 2008; Boddez
et al., 2014).
With this in mind, Yarritu et al. (2015) asked whether the same
effect would occur when the previous history of one of the causes
with the outcome was just illusory. They asked two groups to
assess the effectiveness of drug A in the task described in previous
sections. During Phase 1, the percentage of trials in which the
fictitious patients recovered was the same regardless of whether
they took the drug and was determined by a preprogrammed
random sequence. However, a strong illusion that the drug was
effective was induced in one group, while a weak illusion was
induced in the other group. This was done by manipulating the
probability of the cause (the fictitious patients taking the drug).
Then, in Phase 2, all participants were exposed to a series of
patients who either took a combined treatment of drug A with
a new drug B or received no treatment, after which the patients
recovered or did not. The percentage of trials in which the patients
recovered after taking the combined medication was higher than
in trials with recovery without medication, and these percentages
were identical for all participants. That is, during Phase 2 drug
B was effective. However, when asked about the effectiveness of
drug B in a subsequent test phase, the judgments were lower for
the group that had developed the strong illusion about drug A
in Phase 1. The previous illusion that medicine A was effective
reduced the ability to detect the effectiveness of medicine B
during Phase 2. This suggests that specious medication, even when
complementary, can produce more harm than people usually
Aversive Conditions: Just the Other Way
Throughout this article, we have been assuming that the illusion of
causality occurs in appetitive conditions when people are trying to
obtain a desired event. However, many superstitions and illusions
of causality take place in aversive conditions in which the outcome
is an undesired one such as bad luck or misfortune (e.g., Blum
and Blum, 1974; Aeschleman et al., 2003; Bloom et al., 2007;
Zhang et al., 2014). We should distinguish at least two types of
aversive or negative conditions in which the null contingency
may take place. The first one parallels escape and avoidance
behavior. Even though there is no contingency between cause and
outcome, it will look as if the participants’ behavior is followed
by the termination (or avoidance) of an aversive event. A common
example is touching wood to avoid misfortune. This first type of
negative superstition works in much the same way as the one with
the appetitive outcomes discussed thus far. People will typically
perform actions with high probability in their attempt to escape
or avoid an aversive stimulus as often as possible (Matute, 1995,
1996; Blanco and Matute, 2015). Therefore, most of the strategies
suggested to reduce the illusion of causality should also be useful
in these cases.
The second type parallels a punishment condition. The
participant’s behavior does not produce bad luck, but it seems as
if this were the case. For instance, sitting in row number 13 or
seeing a black cat is considered to bring bad luck. These cases work
differently from the ones described. When aversive consequences
follow an action (or seem to follow them), people tend to reduce
their behavior (i.e., the probability of the cause). This is important
for understanding the difference between these punishment-like
conditions and the appetitive ones. People tend to act with less
frequency in this case, in contrast to appetitive conditions. Thus,
to help them realize that the outcome is independent of their
behavior, our strategy should be opposite to that used in appetitive
To test this view, Matute and Blanco (2014) used the same
instructions shown to reduce illusions in appetitive conditions
in punishment-like conditions. The result was that asking people
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Matute et al. Scientific thinking and the illusion of causality
to reduce the probability of the cause (i.e., their actions) while
warning them about potential alternative causes of the outcome,
increased, rather than reduced, the illusion. The reason isprobably
that when aversive uncontrollable outcomes follow behavior and
people act frequently, as they do by default, their behavior seems
to be punished, so they feel they are not controlling the outcome.
However, when a potential alternative cause for those aversive
outcomes is available and people reduce the frequency with which
they act, and even so the aversive outcomes keep occurring,
it becomes clearer for people that they are not responsible for
the occurrence of the outcomes. They no longer feel that their
behavior is being punished and thus their illusion of control
increases. Therefore, rather than instructing people to reduce the
number of trials in which the cause is present, the best strategy
in punishment-like conditions is asking people to increase cause-
present trials. In that way, it will be easier for them to detect that
they have no control. As an example, rather than asking people
to refrain from selecting row number 13th we should ask them
to select row number 13th with greater frequency so that their
negative illusion can be reduced.
Developing an Educational Strategy
There has been a long debate in scientific education on whether
students learn better through personal discovery or through
more traditional instruction, but recent reports are showing
that to learn about scientific methods, direct instruction is
preferred (e.g., Klahr and Nigam, 2004). Thus, the advantages of
direct instruction could be used to prevent illusions of causality.
However, one serious potential problem is that people may
ignore recommendations because they are not motivated based on
beliefs that they are free from biases. Many people can recognize
cognitive biases in other people but are terrible at recognizing
their own biases (Pronin et al., 2004, 2002). We can hypothesize
that making people realize their perception of causality is far from
perfect will motivate them to learn about scientific methods that
would help them assess causality accurately.
Following this idea, Barberia et al. (2013) told a group of
adolescents that they had developed a miracle product that would
help them improve their physical and cognitive abilities. This was
conducted quite theatrically, and the teenagers were allowed to try
the properties of the product (a piece of regular ferrite) through
several cognitive and physical exercises. Once the adolescents
were made to believe that the bogus product was really effective,
they were ready to listen to possible ways in which they could have
assessed the efficacy of the ferrite more accurately. They received
a seminar on scientific methods emphasizing the importance of
controlling for extraneous or confounding variables. They were
also shown what the product really was and how they had been
victims of their own biases. Finally, their illusion of causality
was assessed using the standard procedure described in previous
The result was that the students that had undergone the
intervention (including both the experience with the bogus
product and the tutorial on experimental methods) reduced
their probability of introducing the cause (their probability of
acting) and developed a significantly weaker causal illusion in
the standardized judgmental task compared to a control group
of naïve participants who did not receive the intervention. We
cannot be sure of the key aspect in the success of the procedure, but
we conjecture that making the students aware of their own biases
might be critical in enhancing the impact that the more academic
explanation of scientific methods had over their behavior and
thinking. Future interventions should focus on disentangling the
key components that made the intervention successful, such as the
role of the initial phase. The results suggest that contrary to the
extended view that cognitive biases cannot be counteracted, there
are procedures that work and need to be documented, as they can
help teach people how to think more scientifically and reduce their
causal illusions.
We are not aware of many other systematic studies on reducing
the illusion of causality or even attempts to reduce other biases
(e.g., Arkes, 1991; Larrick, 2004; Schmaltz and Lilienfeld, 2014).
As Lilienfeld et al. (2009) state, it is strange that with so much
research being conducted on cognitive biases in the last decades
that so little has been done in relation to debiasing. We hope that
we have contributed to developing evidence-based strategies that
can improve the teaching of scientific methods and reduce the
illusion of causality.
Several recommendations on how to reduce the illusion of
causality can be extracted from the experiments reported. First, we
know that the illusion of cause and effect will be weaker when the
desired outcome rarely occurs. Although this variable is usually
beyond our control in null-contingency conditions, it provides an
important cue to anticipate the conditions under which it is more
likely to observe superstitions and causal illusions.
The illusion will also be weaker when the probability of the
cause is low. This is a variable that can be controlled and has shown
to affect the illusion of causality in many experiments in many
different laboratories (Allan and Jenkins, 1983; Wasserman et al.,
1996; Perales et al., 2005; Vadillo et al., 2011). Therefore, it can
be used to reduce the illusion. Specious product advertisements
mention only the cases in which the cause is present (product use)
and the product works effectively (cell ainstances in Table 1).
Thus, one very simple strategy that governments could use is to
make sure that the data from those cases without the potential
cause are also presented during those marketing campaigns. A
related and even better strategy that governments should use is
teaching people how to use the data. Governments could teach
people to realize that they need to ask for information about cause-
absent cases when such information is not readily available. In
other words, governments could teach people (not just science
students) how to make better use of scientific thinking in their
everyday life.
The experiments also showed that the effect of the probability
of the cause (cause-density bias) is also observed indirectly when
someone is depressed because they are more passive. By extension,
this effect is also observed under any other conditions that might
make people reduce their default tendency to introduce a cause
in almost any occasion. Other examples are cases in which the
potential cause is costly or has undesired collateral effects. People
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Matute et al. Scientific thinking and the illusion of causality
do not introduce the cause often in those cases and thus can learn
what happens when they do nothing. Likewise, the illusion is also
weaker when people are just observing the cause and the effect
co-occur than when their own behavior is the potential cause.
Moreover, the illusion is stronger when people are trying to obtain
an outcome, such as when a doctor in an emergency room is trying
to help patients. This contrasts with cases of people trying to find
their degree of causality, such as when a scientist is testing the
effect of a drug on a health problem.
In addition, there are cases in which the results are reversed,
such as when the cause is followed by an undesired event, as in
punishment-like conditions. There are also cases in which the
principles of cue competition apply because there is more than
one potential cause, with some causes preventing attribution to
other causes simultaneously occurring. These cue competition
effects reduce the illusion in some cases and enhance it in others.
This means that there are many variables that are currently very
well known, have been tested in many experiments, and have
predictable effects. Thus, we can now anticipate the cases that
will produce a stronger or a weaker illusion in most people.
This knowledge could be used to make people more alert when
illusions are most probable. We also know from the experiments
that in all cases, the illusion can be reduced if people are taught
to reduce the probability of introducing the potential cause
by learning the basic principles of scientific control and the
experimental paradigm.
With this in mind, we outlined an educational strategy as
an example of what could be done to provide adolescents with
a tool to become less vulnerable to the illusion of causality.
The basic idea is not new. It should consist of teaching how
to think scientifically and apply basic scientific principles to
areas of their own interest. The procedure includes a strategy to
motivate teenagers to learn how to protect themselves from their
own biases. This is a critical part of teaching scientific thinking.
Many people have no interest in learning about scientific thinking
because they are not aware that they need to correct their own
biases in their everyday life. However, once people are shown
how fallible their intuitions are, they should be willing to learn
about tools to prevent errors. One way to motivate people is by
defeating them before offering the tutorial (Barberia et al., 2013).
Another is by showing them examples of common superstitions,
pseudosciences, and myths that they might believe so that they
can learn what their error is and how to use scientific thinking to
overcome the error (Schmaltz and Lilienfeld, 2014).
We have reduced most conditions leading to the attenuation
of the illusion of causality in the literature to cases in which the
cause was presented with a probability closer to 50%. Controlling
the probability of the potential cause is a very important
factor in reducing the illusion, which has been shown in many
different experiments. Indeed, this approach is at the heart of the
experimental method. If we look at how scientists manipulate the
probability of a cause when they perform an experiment, they
might avoid having 50 participants in one group and 5 in another,
or they might try to give cause-present and cause-absent groups
a similar weight and size. However, this is obviously not the only
factor. Reduction in cognitive biases and ungrounded beliefs has
been demonstrated by encouraging a more analytical and distant
style of thinking, as opposed to the default of an intuitive, fast,
and more emotional way of thinking in situations that are not
necessarily contaminated with a high probability of the potential
cause (Frederick, 2005; Kahneman, 2011; Stanovich, 2011; Ziegler
and Tunney, 2012; Evans and Stanovich, 2013). This includes
a reduction of religious and teleological beliefs (Gervais and
Norenzayan, 2012; Kelemen et al., 2013), as well as a reduction of
several cognitive biases such as confirmation biases (Galinsky and
Moskowitz, 2000; Galinsky and Ku, 2004) and framing (Keysar
et al., 2012; Costa et al., 2014). Our preliminary research suggests
that this approach based on the literature on general cognitive
biases can also help reduce the illusion of causality (Díaz-Lago and
Matute, 2014).
As Lilienfeld et al. (2009) have noted, designing a worldwide
strategy to reduce cognitive biases would be the greatest
contribution that psychology could make to humanity, as it would
eliminate so much suffering and intolerance. We have reviewed
some of the evidence about two of these biases—the illusion
of causality and the illusion of control—and how they can be
reduced. We hope that this will contribute to increasing awareness
of these biases and of ways to effectively reduce them in real life. Of
course, this is not to say that analytical thinking should always be
preferred over fast intuition. As many have already noted, there are
times when intuitive judgments are more accurate than analytical
ones (Kruglanski and Gigerenzer, 2011; Phua and Tan, 2013;
Evans, 2014). Thus, the aim when teaching scientific methods
should not only be mastering the ability to think scientifically, but
perhaps most importantly, the ability to detect when this mode of
thinking should be used.
Support for this research was provided by Grant PSI2011-
26965 from the Dirección General de Investigación of the
Spanish Government and Grant IT363-10 from the Departamento
de Educación, Universidades e Investigación of the Basque
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Conflict of Interest Statement: The authors declare that the research was
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Frontiers in Psychology | July 2015 | Volume 6 | Article 88814
... Analyzing Cherry Picking Filter out data that is not conducive to the author's intended narrative [10] The Simpson's paradox high-level aggregation of data leads to wrong conclusion [21] Visualizing Break Conventions Create unusual charts that mislead people to analyze them with conventions [13,31,45] Concealing Uncertainty Conceal the uncertainty in the chart to cover up the low quality of the data [50] Scripting Text-visualization misalignment The message of the text differs from that of the visualization it refers to [26,27] Text Wording The degree of text intensity can bias readers' memory of graph [43] Illusions of causality The text makes incorrect causal inductions on chart information [32,35,35] Obmiting context Omitting the context needed to understand the story [14] Manipulating order Manipulate the reading order through layout, resulting in order bias [16] Arranging Obfuscation Make it difficult for readers to extract visual information through chaotic layout [13] Reading Personal bias Political attitudes, beliefs and other personal factors lead to misperception of facts [19,38] Figure 3: The production-consumption process of narrative visualization ...
... Analyzing Cherry Picking Filter out data that is not conducive to the author's intended narrative [10] The Simpson's paradox high-level aggregation of data leads to wrong conclusion [21] Visualizing Break Conventions Create unusual charts that mislead people to analyze them with conventions [13,31,45] Concealing Uncertainty Conceal the uncertainty in the chart to cover up the low quality of the data [50] Scripting Text-visualization misalignment The message of the text differs from that of the visualization it refers to [26,27] Text Wording The degree of text intensity can bias readers' memory of graph [43] Illusions of causality The text makes incorrect causal inductions on chart information [32,35,35] Obmiting context Omitting the context needed to understand the story [14] Manipulating order Manipulate the reading order through layout, resulting in order bias [16] Arranging Obfuscation Make it difficult for readers to extract visual information through chaotic layout [13] Reading Personal bias Political attitudes, beliefs and other personal factors lead to misperception of facts [19,38] Figure 3: The production-consumption process of narrative visualization ...
Misinformation has disruptive effects on our lives. Many researchers have looked into means to identify and combat misinformation in text or data visualization. However, there is still a lack of understanding of how misinformation can be introduced when text and visualization are combined to tell data stories, not to mention how to improve the lay public's awareness of possible misperceptions about facts in narrative visualization. In this paper, we first analyze where misinformation could possibly be injected into the production-consumption process of data stories through a literature survey. Then, as a first step towards combating misinformation in data stories, we explore possible defensive design methods to enhance the reader's awareness of information misalignment when data facts are scripted and visualized. More specifically, we conduct a between-subjects crowdsourcing study to investigate the impact of two design methods enhancing text-visualization integration, i.e., explanatory annotation and interactive linking, on users' awareness of misinformation in data stories. The study results show that although most participants still can not find misinformation, the two design methods can significantly lower the perceived credibility of the text or visualizations. Our work informs the possibility of fighting an infodemic through defensive design methods.
... Belief-consistency refers to the motivations described above, while negative information refers to a natural tendency for loss aversion which reflects an evolutionary vigilance mechanism [74]. Predictive information is that which provides insights about future events, but this is often derived in very biased ways, such that illusory correlations [155] or false causalities [96] are sometimes perceived. This characteristic is perhaps most relevant in the context of conspiratorial misinformation. ...
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Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for ve-racity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and rad-icalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinfor-mation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.
... Several studies have shown that people's contingency judgments are indeed correlated with normative contingency (Beckers et al., 2009;Cheng & Novick, 1992;Gopnik et al., 2001). However, systematic departures from normative contingency have also been reported in the literature (Blanco et al., 2011;Matute et al., 2015;Shanks, 1995;Kao & Wasserman, 1993;Ward & Jenkins, 1965). Different factors such as the relative frequency of a potential cause or the outcome have been shown to bias participant's judgments (Musca et al., 2010;Blanco et al., 2013; see Matute et al., 2019, for a recent overview of biasing factors in contingency learning tasks). ...
Prior knowledge has been shown to be an important factor in causal judgments. However, inconsistent patterns have been reported regarding the interaction between prior knowledge and the processing of contingency information. In three studies, we examined the effect of the plausibility of the putative cause on causal judgments, when prior expectations about the rate at which the cause is accompanied by the effect in question are explicitly controlled for. Results clearly show that plausibility has a clear efect that is independent of contingency information and type of task (passive or active). We also examined the role of strategy use as an individual difference in causal judgments. Specifically, the dual-strategy model suggests that people can either use a Statistical or a Counterexample strategy to process information. Across all three studies, results showed that Strategy use has a clear effect on causal judgments that is independent of both plausibility and contingency.
... The results obtained in the present study show that understanding and predicting beliefs in pseudoscience is a complex process, as recently suggested (Fasce & Pic o, 2019b). Although our results need to be replicated and studied in-depth, the analytical approaches provided in this study could be useful for any type of proposal or intervention aiming at reducing pseudoscience acceptance (Barberia et al., 2013(Barberia et al., , 2018Lilienfeld et al., 2012Lilienfeld et al., , 2014Matute et al., 2015;McLean & Miller, 2010;Wilson, 2018). ...
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The spread of pseudoscience (PS) is a worrying problem worldwide. The study of pseudoscience beliefs and their associated predictors have been conducted in the context of isolated pseudoscience topics (e.g., Complementary and alternative medicine). Here, we combined individual differences (IIDD) measures (e.g., Personality traits, thinking styles) with measures related with the information received about PS: familiarity and disproving information (DI) in order to explore potential differences among pseudoscience topics in terms of their associated variables. These topics differed in their familiarity, their belief rating and their associated predictors. Critically, our results not only show that DI is negatively associated with pseudoscience beliefs but that the effect of various IIDD predictors (e.g., Analytic thinking) depends on whether DI had been received. This study highlights the need to control for variables related to information received about pseudoscientific claims to better understand the effect of other predictors on different pseudoscience beliefs topics. This article is protected by copyright. All rights reserved.
... In these tasks, people are generally good at accurately judging causal relationships when there is a genuine objective relationship between the events [3,4], e.g., if the fictitious drug is genuinely associated with improvement relative to no drug. However, people are much worse at these tasks when no genuine relationship exists between the events (e.g., illusory causation, see [5] for a review) often tending to overestimate the causal relationship, pointing to systematic biases in contingency learning and causal belief formation. ...
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Beliefs about cause and effect, including health beliefs, are thought to be related to the frequency of the target outcome (e.g., health recovery) occurring when the putative cause is present and when it is absent (treatment administered vs. no treatment); this is known as contingency learning. However, it is unclear whether unvalidated health beliefs, where there is no evidence of cause–effect contingency, are also influenced by the subjective perception of a meaningful contingency between events. In a survey, respondents were asked to judge a range of health beliefs and estimate the probability of the target outcome occurring with and without the putative cause present. Overall, we found evidence that causal beliefs are related to perceived cause–effect contingency. Interestingly, beliefs that were not predicted by perceived contingency were meaningfully related to scores on the paranormal belief scale. These findings suggest heterogeneity in pseudoscientific health beliefs and the need to tailor intervention strategies according to underlying causes.
... Pseudoscientific information and pseudosciences are no strangers to these factors, and many people have used fake news or alternative therapies as an internal psychological resource to (1) gain a greater sense of control (Šrol, Ballová Mikušková & Čavojová, 2021), (2) reduce stress levels in the face of the uncertainty caused by the coronavirus crisis (Á Escolà-Gascón et al., 2020), and (3) find meanings that allow them to speculate about the causality of everything that happened and is happening within this framework of the pandemic crisis (Escolà-Gascón, 2020). This irrational search for meanings is called causal illusions and is a common psychological response to situations of maximum uncertainty (Matute et al., 2015). ...
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The prevalence of pseudoscientific beliefs and fake news increased during the coronavirus crisis. Misinformation streams such as these potentially pose risks to people's health. Thus, knowing how these pseudoscientific beliefs and fake news impact the community of internists may be useful for improving primary care services. In this research, analyses of stress levels, effectiveness in detecting fake news, use of critical thinking (CP), and attitudes toward pseudosciences in internists during the COVID-19 crisis were performed. A total of 1129 internists participated. Several multiple regression models were applied using the forward stepwise method to determine the weight of CP and physicians' attitudes toward pseudosciences in predicting reductions in stress levels and facilitating the detection of fake news. The use of critical thinking predicted 46.9% of the reduction in stress levels. Similarly, skeptical attitudes and critical thinking predicted 56.1% of the hits on fake news detection tests. The stress levels of physicians during the coronavirus pandemic were clinically significant. The efficacy of fake news detection increases by 30.7% if the individual was a physician. Study outcomes indicate that the use of critical thinking and skeptical attitudes reduce stress levels and allow better detection of fake news. The importance of how to promote critical and skeptical attitudes in the field of medicine is discussed.
... Identifiée comme un défaut de calibration préjudiciable à la qualité de la prise de décision, la surconfiance se manifeste dans de nombreuses tâches d'évaluation de probabilités, où les individus surestiment leur niveau de connaissance (Lichtenstein et al., 1982). Cependant, le phénomène de surconfiance disparaîtrait lorsqu'on demande aux individus d'exprimer leur degré de confiance en leurs réponses, non plus pour un événement isolé mais en termes de fréquence sur un ensemble d'événements (Gigerenzer, 1991 (Blanco, 2017;Matute et al., 2015), l'illusion de contrôle est une illusion causale qui a d'abord été décrite comme un biais optimiste et auto-amplifiant (Langer, 1975, Langer & Roth, 1975, ou comme un biais égocentrique (Alloy & Abramson, 1979;Alloy & Clements, 1992;Taylor & Brown, 1988, 1994 situations. Si pour certains elle peut se manifester dans toutes situations, y compris celles où un certain niveau de contrôle effectif est possible (Taylor & Brown, 1988, 1994, pour la majorité, on ne peut parler d'illusion de contrôle que dans les situations entièrement déterminées par le hasard (Budescu & Bruderman, 1995;Steenbergh et al., 2002;Zuckerman et al., 1996). ...
Si près d’un français sur deux joue au moins une fois par an, on remarque spécifiquement, entre 2010 et 2014, une augmentation de 11,5% du nombre de joueurs parmi les 45-75 ans (Observatoire Des Jeux [ODJ], 2015). Les aînés de 55 à 64 ans sont d’ailleurs les premiers consommateurs de jeux de hasard et d’argent (Institut National de la Statistique et des Etudes Economiques [INSEE], 2016). Peu d’auteurs ont toutefois investigué la question du vieillissement des joueurs dans les JHA, impliquant un manque de données empiriques conséquent (Tse et al., 2012). Pourtant, les jeux de hasard et d’argent (JHA) font l’objet d’un domaine d’étude qui connaît un essor important depuis les années 2000. En plus d'une grande quantité de travaux sur la population générale, de nombreuses recherches ont porté sur les adolescents et les jeunes, considérés comme une population vulnérable (Kairouz et al., 2013). Vulnérables eux aussi (Subramaniam et al. 2015 ; Tse et al. 2012 ; Wainstein et al. 2008), les aînés constituent une population préoccupante en raison de leur exposition à la fois à des offres de jeu de plus en plus abondantes et à de puissants facteurs de risque spécifiques à l'âge. En l’absence de référents théoriques permettant d’appréhender le renouvellement des conduites de jeu des aînés, deux facteurs déterminants ont été convoqués dans cette thèse : l’illusion de contrôle et la prise de risque. Concept polysémique, l'illusion de contrôle demeure à ce jour encore discutable, en termes de définition et de mesure, malgré le grand nombre d’études l’ayant examiné (Masuda, Sakagami, & Hirota, 2002). Cette thèse a ainsi poursuivi un double objectif : élaborer et valider une échelle multidimensionnelle de l’illusion de contrôle dont le format matriciel (Bonnel, 2016) met en exergue les valences affectives positives et négatives ; identifier les mécanismes cognitifs spécifiques à l'âge qui sous-tendent le comportement de jeu dans le vieillissement normal. Les perspectives temporelles (Zimbardo & Boyd, 1999) constituant par ailleurs un bon indicateur des comportements à risque dans un certain nombre de domaines (e.g., santé, environnement), les relations entre âge, perspectives temporelles (PT), illusion de contrôle et prise de risque ont été interrogées. Au bilan, les résultats suggèrent que les aînés constituent une population spécifique en termes de cognitions et de comportements liés au jeu, sous certaines conditions. L'inclusion des PT dans les évaluations des comportements à risque permettrait de développer des mesures préventives sur mesure, destinées à empêcher ou diminuer le risque que les aînés développent un problème de jeu, dont les conséquences sont plus délétères pour cette population.
During the Trump administration, the American economy experienced some of the best results since World War II. Unemployment and poverty were at record lows and the stock market was booming. At the same time, the fiscal deficit and public debt increased, while the harm done to the rules-based international trading system is yet to be seen. Using historical data, the aim of this paper is to provide a comparative overview of some of the hallmarks of Trump’s economic policy taking a critical look at each one of them: tax cuts, deregulation, and protectionism. While the pre-pandemic U.S. economy was in some aspects truly impressive, it is difficult to disentangle to what extent this was owed to the president's economic policies and to what extent this was simply due to a positive external environment, which should be the subject of future scientific research.
Background We have previously presented two educational interventions aimed to diminish causal illusions and promote critical thinking. In both cases, these interventions reduced causal illusions developed in response to active contingency learning tasks, in which participants were able to decide whether to introduce the potential cause in each of the learning trials. The reduction of causal judgments appeared to be influenced by differences in the frequency with which the participants decided to apply the potential cause, hence indicating that the intervention affected their information sampling strategies. Objective In the present study, we investigated whether one of these interventions also reduces causal illusions when covariation information is acquired passively. Method Forty-one psychology undergraduates received our debiasing intervention, while 31 students were assigned to a control condition. All participants completed a passive contingency learning task. Results We found weaker causal illusions in students that participated in the debiasing intervention, compared to the control group. Conclusion The intervention affects not only the way the participants look for new evidence, but also the way they interpret given information. Teaching implications Our data extending previous results regarding evidence-based educational interventions aimed to promote critical thinking to situations in which we act as mere observers.
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2007 by Alison Gopnik and Laura Schulz. All rights reserved. Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning.
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.
An informal study was conducted to demonstrate the prevalence and relationship to specific background variables of prevailing superstitions. A questionnaire containing 24 superstitious beliefs or practices was independently completed by 132 adults. Each superstition was rated as to whether it had strong, partial, or no influence for the individual, and a total score was obtained. The highest possible score was 48, and the range found in the sample was 0 to 46. The mean total superstition score for women was higher than for men, the difference being statistically significant (p.05). A moderately substantial negative correlation (p.01) was found between superstitious belief and amount of formal education. It is suggested that, particularly in current times, the sense of control inherent in superstitious belief and practice has a therapeutic value in the reduction of anxiety. This value may account for the survival of common superstitions in spite of centuries of advance in scientific knowledge.
This book attempts to resolve the Great Rationality Debate in cognitive science-the debate about how much irrationality to ascribe to human cognition. It shows how the insights of dual-process theory and evolutionary psychology can be combined to explain why humans are sometimes irrational even though they possess remarkably adaptive cognitive machinery. The book argues that to characterize fully differences in rational thinking, we need to replace dual-process theories with tripartite models of cognition. Using a unique individual differences approach, it shows that the traditional second system (System 2) of dual-process theory must be further divided into the reflective mind and the algorithmic mind. Distinguishing them gives a better appreciation of the significant differences in their key functions: the key function of the reflective mind is to detect the need to interrupt autonomous processing and to begin simulation activities, whereas that of the algorithmic mind is to sustain the processing of decoupled secondary representations in cognitive simulation. The book then uses this algorithmic/reflective distinction to develop a taxonomy of cognitive errors made on tasks in the heuristics and biases literature. It presents the empirical data to show that the tendency to make these thinking errors is not highly related to intelligence. Using a tripartite model of cognition, the book shows how, when both are properly defined, rationality is a more encompassing construct than intelligence, and that IQ tests fail to assess individual differences in rational thought. It then goes on to discuss the types of thinking processes that would be measured if rational thinking were to be assessed as IQ has been.
Some authors questioned the ecological validity of judgmental biases demonstrated in the laboratory. One objection to these demonstrations is that evolutionary pressures would have rendered such maladaptive behaviors extinct if they had any impact in the "real world." I attempt to show that even beneficial adaptations may have costs. I extend this argument to propose three types of judgment errors-strategy-based errors, association-based errors, and psychophysical based errors-each of which is a cost of a highly adaptive system. This taxonomy of judgment behaviors is used to advance hypotheses as to which debiasing techniques are likely to succeed in each category.
The study of the mechanism that detects the contingency between events. in both humans and non-human animals, is a matter of considerable research activity. Two broad categories of explanations of the acquisition of contingency information have received extensive evaluation: rule-based models and associative models. This article assesses the two categories of models for human contingency judgments. The data reveal systematic departures in contingency judgments from the predictions of rule-based models. Recent studies indicate that a contiguity model of Pavlovian conditioning is a useful heuristic for conceptualizing human contingency judgments.