DRUGS New agreement to tackle pharmaceutical pollution p.164 WORLD VIEW Vaccination the best way to measure health care p.165 DUNG OVER Rolling beetles fooled by look-alike seeds p.167 Let’s think about cognitive bias The human brain’s habit of finding what it wants to find is a key problem for research. Establishing robust methods to avoid such bias will make results more reproducible. “Ever since I first learned about confirmation bias I’ve been see-ing it everywhere.” So said British author and broadcaster Jon Ronson in So You’ve Been Publicly Shamed (Picador, 2015). You will see a lot of cognitive bias in this week’s Nature. In a series of articles, we examine the impact that bias can have on research, and the best ways to identify and tackle it. One enemy of robust science is our humanity — our appetite for being right, and our tendency to find patterns in noise, to see supporting evidence for what we already believe is true, and to ignore the facts that do not fit. The sources and types of such cognitive bias — and the fallacies they produce — are becoming more widely appreciated. Some of the prob- lems are as old as science itself, and some are new: the IKEA effect, for example, describes a cognitive bias among consumers who place artifi- cially high value on products that they have built themselves. Another common fallacy in research is the Texas sharp-shooter effect — fir- ing off a few rounds and then drawing a bull’s eye around the bullet holes. And then there is asymmetrical attention: carefully debugging analyses and debunking data that counter a favoured hypothesis, while letting evidence in favour of the hypothesis slide by unexamined. Such fallacies sound obvious and easy to avoid. It is easy to think that they only affect other people. In fact, they fall naturally into investiga- tors’ blind spots (see page 182). Advocates of robust science have repeatedly warned against cogni- tive habits that can lead to error. Although such awareness is essential, it is insufficient. The scientific community needs concrete guidance on how to manage its all-too-human biases and avoid the errors they cause. That need is particularly acute in statistical data analysis, where some of the best-established methods were developed in a time before data sets were measured in terabytes, and where choices between tech- niques offer abundant opportunity for errors. Proteomics and genom- ics, for example, crunch millions of data points at once, over thousands of gene or protein variants. Early work was plagued by false positives, before the spread of techniques that could account for the myriad hypotheses that such a data-rich environment could generate. Although problems persist, these fields serve as examples of commu- nities learning to recognize and curb their mistakes. Another example is the venerable practice of double-blind studies. But more effort is needed, particularly in what some have called evidence- ...

1. DRUGS New agreement to
tackle pharmaceutical
pollution p.164
WORLD VIEW Vaccination
the best way to measure
health care p.165
DUNG OVER Rolling beetles
fooled by look-alike
seeds p.167
Let’s think about cognitive bias
The human brain’s habit of finding what it wants to find is a
key problem for research. Establishing
robust methods to avoid such bias will make results more
reproducible.
“Ever since I first learned about confirmation bias I’ve been
see-ing it everywhere.” So said British author and broadcaster
Jon Ronson in So You’ve Been Publicly Shamed (Picador,
2015).
You will see a lot of cognitive bias in this week’s Nature. In a
series
of articles, we examine the impact that bias can have on
research, and
the best ways to identify and tackle it. One enemy of robust
science
is our humanity — our appetite for being right, and our
tendency to
find patterns in noise, to see supporting evidence for what we

2. already
believe is true, and to ignore the facts that do not fit.
The sources and types of such cognitive bias — and the
fallacies they
produce — are becoming more widely appreciated. Some of the
prob-
lems are as old as science itself, and some are new: the IKEA
effect, for
example, describes a cognitive bias among consumers who place
artifi-
cially high value on products that they have built themselves.
Another
common fallacy in research is the Texas sharp-shooter effect —
fir-
ing off a few rounds and then drawing a bull’s eye around the
bullet
holes. And then there is asymmetrical attention: carefully
debugging
analyses and debunking data that counter a favoured hypothesis,
while
letting evidence in favour of the hypothesis slide by
unexamined.
Such fallacies sound obvious and easy to avoid. It is eas y to
think that
they only affect other people. In fact, they fall naturally into
investiga-
tors’ blind spots (see page 182).
Advocates of robust science have repeatedly warned against
cogni-
tive habits that can lead to error. Although such awareness is
essential,
it is insufficient. The scientific community needs concrete
guidance on

3. how to manage its all-too-human biases and avoid the errors
they cause.
That need is particularly acute in statistical data analysis, where
some of the best-established methods were developed in a time
before
data sets were measured in terabytes, and where choices
between tech-
niques offer abundant opportunity for errors. Proteomics and
genom-
ics, for example, crunch millions of data points at once, over
thousands
of gene or protein variants. Early work was plagued by false
positives,
before the spread of techniques that could account for the
myriad
hypotheses that such a data-rich environment could generate.
Although problems persist, these fields serve as examples of
commu-
nities learning to recognize and curb their mistakes. Another
example is
the venerable practice of double-blind studies. But more effort
is needed,
particularly in what some have called evidence-
based data analysis: research on what techniques
work best to establish default analytical pipelines
for cleaning and debugging data sets, selecting
models and other steps of analysis.
More specifically, science needs ways to iden-
tify the mistakes most likely to be made by nov-
ice (and not-so-novice) number crunchers. The
scientific community must design research pro-
tocols that safeguard against these errors, and
devise methods that ferret out sloppy analyses.

4. Some researchers already do this well, so one relatively simple
strategy
is to improve how knowledge and resources move from a narrow
group
of experts to the broader scientific community. If highly
respected, easy-
to-implement alternative routes are available and encouraged, it
will be
harder to cling to analyses that are rigged by conscious or
unconscious
bias to produce the results that researchers want. Funders
should sup-
port teams that are attempting to determine the best analytical
routes,
and should provide training in data analysis for others.
Institutions and
principal investigators should make such training mandatory.
Finally, the scientific community must go beyond statistical
safe-
guards, and improve researchers’ behaviour. Angst over
unreliable
research has already spurred investigations into ways to make
results
more robust. Some of the most promising address not just
techniques,
but also academic culture: laboratory and workplace habits can
discour-
age rigour, or can enforce it through blinding, preregistering
analytical
plans, crowdsourcing analysis, formally laying out null and
alternative
hypotheses, and labelling analyses as exploratory or
confirmatory.

5. Such strategies require effort, but offer significant rewards.
Blind ana-
lysis forces creative thinking as researchers struggle to find
explanations
for hypothetical results. A Comment on page 187 explores these
rewards
and offers tips for researchers ready to try it.
Crowdsourcing shows how the same data
set, analysed with different approaches, can
yield a variety of answers; it is a reminder that
single-team analysis is only part of the story. As
a Comment on page 189 reveals, crowdsourced
analyses and interdisciplinary projects can also
compare analysis techniques across disciplines, and show how
one field
might hold lessons for another. Some differences in approach
are prob-
ably down to cultural happenstance — “we have always done it
this
way” — rather than to selection of best practice. That should
change.
To ensure that such practices actually strengthen science,
scientists
must subject the strategies themselves to scientific scrutiny.
(No one
should take recommendations to counter bias on faith!) Social
sci-
entists have an important role here — studies of science in
action are
essential. Careful observation of scientists can test w hich
strategies are
most effective under what circumstances, and can explore how

6. debias-
ing strategies can best be integrated into routine scientific
practice.
Funders should support efforts to establish the best methods of
blind
analysis, crowdsourcing and reviewing registered analysis
plans, and
should help meta-scientists to test and compare these practices.
Ideally,
the utility and burdens of these strategies under varying
circumstances
would be explored and published in the peer-reviewed
literature. This
information could then be fed into much-needed training
programmes,
and so better equip the next generation of scientists to do good
science.
Finding the best ways to keep scientists from fooling
themselves
has so far been mainly an art form and an ideal. The time has
come to
make it a science. We need to see it everywhere. ■
“It is easy to
think that
fallacies only
affect other
people.”
8 O C T O B E R 2 0 1 5 | V O L 5 2 6 | N A T U R E | 1 6
3
EDITORIALS

7. THIS WEEK
NATURE.COM
For Nature’s special
collection on
reproducibility, see:
go.nature.com/huhbyr
© 2015 Macmillan Publishers Limited. All rights reserved
Reproduced with permission of the copyright owner. Further
reproduction prohibited without
permission.
This article was originally published in the Encyclopedia of
Human Behavior published by Elsevier,
and the attached copy is provided by Elsevier for the author's
benefit and for the benefit of the
author's institution, for non-commercial research and
educational use including without limitation
use in instruction at your institution, sending it to specific
colleagues who you know, and providing a
copy to your institution’s administrator.

8. All other uses, reproduction and distribution, including wi thout
limitation commercial reprints,
selling or licensing copies or access, or posting on open internet
sites, your personal or institution’s
website or repository, are prohibited. For exceptions,
permission may be sought for such use
through Elsevier's permissions site at:
http://www.elsevier.com/locate/permissionusematerial
Wilke A. and Mata R. (2012) Cognitive Bias. In: V.S.
Ramachandran (ed.) The Encyclopedia of
Human Behavior, vol. 1, pp. 531-535. Academic Press.
© 2012 Elsevier Inc. All rights reserved.
Author's personal copy
Cognitive Bias
A Wilke, Clarkson University, Potsdam, NY, USA
R Mata, University of Basel, Basel, Switzerland
ã 2012 Elsevier Inc. All rights reserved.
Glossary
Bounded rationality The principle that organisms have
limited resources, such as time, information, and cognitive

9. capacity, with which to find solutions to the problems
they face.
Cognitive bias Systematic error in judgment and
decision-making common to all human beings which can be
due to cognitive limitations, motivational factors, and/or
adaptations to natural environments.
Ecological rationality The principle that there is a match
between the structure of information in the environment
and the judgment and decision-making strategies of humans
and other organisms.
Heuristic Judgment or decision-making mechanism or
cognitive shortcut that relies on little information and
modest cognitive resources.
Encyclopedia of Human Behavior, Se
Heuristics and Biases: A Short History of Cognitive Bias
In the early 1970s, Amos Tversky and Daniel Kahneman intro-
duced the term ‘cognitive bias’ to describe people’s systematic

10. but purportedly flawed patterns of responses to judgment
and decision problems. A term search for ‘cognitive bias’ in
the Social Sciences Database of ISI Web of Knowledge reveals
close to 4000 hits covering the past 35-year period and an
exponential increase in the usage over time, suggesting that
the term ‘cognitive bias’ has since gained significant influence
in the psychological and social science literatures.
Tversky and Kahneman’s research program – the heuristics
and biases program – addressed the question of how people
make decisions given their limited resources. The program was
inspired by Herbert Simon’s principle of bounded rationality.
In the late 1950s, Simon attempted to oppose the idea of
classical rationality, which was concerned mostly with the for -
malization of normative solutions to judgment and decision-
making problems through probability theory and statistics,
with the idea of bounded rationality, which addressed the
specific constraints faced by agents in their environments. For
example, humans have only limited time, information, and

11. cognitive capacity to decide which mate to choose, food to
eat, or house to buy, and so may have to rely on simple
decision strategies or heuristics to make their decisions. The
heuristics and biases program followed the bounded rational -
ity principle by attempting to identify the specific constraints or
biases associated with human judgment and decision-making.
The heuristics and biases program was inspired by research
on perceptual biases, and proposed that the human cognitive
system like the perceptual system was designed to make infer-
ences about the external world based on imperfect cues that
could lead to errors in some situations. The program thus
generated a straightforward and productive research paradigm,
which can be described as follows. First, participants were
presented with a reasoning problem to which corresponded
a normative answer from probability theory or statistics.
Next, participants’ responses were compared with the solution
entailed by these norms, and the systematic deviations (biases)
found between the responses and the normative solutions were

12. listed. Finally, the biases were explained as the consequence
of the use of heuristics or simple cognitive principles. Using
this strategy, researchers in the heuristics and biases program
have produced an extensive catalog of norm violations.
We present a partial list in Table 1 that spans the judgment
and decision-making, social, and memory research domains.
Naturally, the goal was to provide explanations of these viola-
tions due to reliance on a small set of cognitive principles, the
most popular judgment and decision mechanisms proposed
being representativeness (a judgment is based on how much the
hypothesis resembles available data), availability (a judgment
is based on how easily an example can be brought to mind),
and anchoring-and-adjustment (a judgment is based on a
specific
value or anchor and then adjusted to account for other factors).
The heuristics and biases program represents the most
influential psychological research program to emerge in the
last 40 years, and its merit lies in showing the shortcomings

13. of classical economic approaches and the value of a bounded
rationality perspective on understanding human judgment.
The heuristics and biases program has, however, been criti -
cized. First, researchers have argued that there are no unequiv-
ocal norms for defining rational judgments and decisions. For
example, there are different concepts of probability espoused
by statisticians and philosophers that imply different norms,
which makes deviations from one hard to interpret as error
or bias. Second, the program has been criticized for presenting
only vague models of human reasoning. For example, the
representativeness, availability, and anchoring-and-adjustment
heuristics proposed by Tversky and Kahneman do not provide
quantitative predictions of people’s judgments and it is often
unclear which heuristic is applied under which condition.
Third, the heuristics and biases program has been criticized
for focusing on people’s initial responses to judgment problems
rather than providing opportunity for learning from experi -
ence. For example, some anomalies to classical decision theory

14. are eliminated if people have substantial experience with a
decision problem. Similarly, many classic paradigms in this
tradition involve participants’ responses to situations described
in word vignettes, which are not ecologically valid and thus
may offer inadequate insights about everyday decision-making.
531
cond Edition (2012), vol. 1, pp. 531-535
Table 1 Examples of common cognitive biases
Cognitive bias Short description
Confirmation bias The tendency to selectively search for or
interpret information in a way that confirms one’s
preconceptions or hypotheses
Conjunction fallacy The tendency to assume that specific
conditions are more probable than a single general one
Endowment effect The tendency that people often demand more
to give up on an object than they would be willing to pay to
acquire it
Fundamental
attribution error
The tendency to overemphasize personal factors and
underestimate situational factors when explaining other
people’s

15. behavior
Gambler’s fallacy The tendency to think that future
probabilities are changed by past events, when in reality they
are unchanged (e.g., series of
roulette wheel spins)
Halo effect The tendency for a person’s positive or negative
traits to extend from one area of their personality to another in
others’
perceptions of them
Hindsight bias* A memory distortion phenomenon by which
with the benefit of feedback about the outcome of an event,
people’s recalled
judgments of the likelihood of that event are typically closer to
the actual outcome than their original judgments were
Hot-hand fallacy* The expectation of streaks in sequences of
hits and misses whose probabilities are, in fact, independent
(e.g., coin tosses,
basketball shots)
Illusory correlation The tendency to identify a correlation
between a certain type of action and effect when no such
correlation exists
In-group bias The tendency for people to give preferential
treatment to others they perceive to be members of their own
group
Mere exposure effect The tendency by which people develop a
preference for things merely because they are familiar with
them
Asterisks refer to examples that are discussed in the main text.
532 Cognitive Bias

16. Author's personal copy
This view echoes well Egon Brunswik’s argument for the study
of the mind by relying on the informational cues present in
natural environments.
Ecological Rationality: Putting Cognitive Biases in an
Environmental Context
One fundamental criticism of the heuristics and biases pro-
gram is that it has severely neglected the ecology of judgment
and decision processes. The principle of bounded rationality is
deeply associated with the idea that cognitive systems are
fundamentally adapted to their environments – either through
individual learning or by design through natural selection.
Simon illustrated this with a metaphor: mind and environ-
ment as blades of a pair of scissors. Similar thoughts have
been espoused by a number of other theorists. For example,
Roger Shepard saw human vision as reflecting regularities of

17. the physical world. John Anderson advanced the idea that
memory is structured so as to mimic the probability of infor-
mation occurring in the world and thus being needed by the
organism.
In the late 1990s, Gerd Gigerenzer, Peter Todd, and the
ABC Research Group presented a research program – the fast
and frugal heuristics program – that extended the principle
of bounded rationality and gave new breadth to the idea of
cognitive bias. The fast and frugal heuristics program empha-
sized the principle of ecological rationality, that is, how the
success of reasoning strategies depends on the structure of
the environment. A good example of this principle is demon-
strated by the United Parcel Service (UPS) Right Turn Policy:
UPS, an international shipping company, delivers millions
of packages every year in numerous delivery trucks. The right
turn policy involves carefully mapping out routes for all deliv-
eries to reduce the number of left-hand turns each truck makes,
which helps reduce accidents as well as save fuel, thus max-

18. imizing overall profits. Naturally, this strategy works well in
the United States and other countries where traffic keeps to the
right. One would predict, however, that the right turn policy
Encyclopedia of Human Behavior, Sec
would have the opposite results in countries, such as England,
India, or Hong Kong, where people drive on the left.
The fast and frugal heuristics program has proposed an
alternative research paradigm to the heuristics and biases’
one. The program starts by analyzing the statistical structure
of a specific task environment people face and then – based on
the analysis – derives attributes of the cognitive models of
reasoning that perform well in that environment. In sum, this
program holds that exploring the characteristics of the environ-
ment will contribute to our understanding of what reasoning
processes people follow and when and why these processes
work well.
According to the fast and frugal heuristics program, a cog-
nitive bias is the tendency to solve problems using a particular

19. cognitive tool or heuristic. Crucially, it sees the selection of a
particular heuristic not necessarily as the product of cognitive
limitations but rather as a bet on the part of the organism
about the structure of the environment in which it finds itself.
One metaphor that guides the fast and frugal heuristics pro-
gram is that of the mind as an adaptive toolbox of simple
decision mechanisms, a repertoire of strategies, with each strat-
egy tuned to a particular environment. A model of mind based
on an adaptive toolbox is, therefore, boundedly rational in the
sense of relying on few cognitive resources, and ecologically
rational in the sense of being tuned to characteristics of natural
environments.
Some have suggested that the differences between the
Heuristics and Biases and the Fast and Frugal Heuristics pro-
grams are not substantive, boiling down to a disagreement
between those that stress that the human mind is fallible and
those who claim that it is often accurate. One clear contribu-
tion of the Fast and Frugal Heuristics program has been, how-

20. ever, to emphasize the role of environment and specify the
statistical properties of environments that make particular cog-
nitive biases or heuristics successful. In addition, the focus
on ecological rationality has spurned new approaches that
emphasize the role of environment and sampling in deter-
mining adaptive behavior. Specifically, recent approaches are
devoted to understanding the role of sampling in generating
ond Edition (2012), vol. 1, pp. 531-535
Cognitive Bias 533
Author's personal copy
bias with less focus on the cognitive apparatus and more on
environmental stimuli. For example, people’s risk judgments
of low probability events are often inflated. One possibility is
that such bias is due to selective memory retrieval. However,
an unbiased memory may also produce inflated judgments
of risk due to biased media coverage of natural catastrophes

21. and accidents. Current and future work on cognitive bias is
concerned with the role of biased sampling in both the external
environment and the internal cognitive apparatus.
Evolutionary Rationality: Understanding Why
Cognitive Biases Occur
The concept of ecological rationality describes the match
between structure and representation of information in the
environment on one side, and the simple decision-making
algorithms such as heuristics on the other. Whenever this
match exists, heuristics can perform well. Evolutionary ratio-
nality holds, however, that it is important to consider the
match between mind and the past environments in which
the mind evolved. In other words, evolutionary rationality
attempts to sketch the evolutionary origins of cognitive bias.
Some evolutionary scientists have followed the Heuristics
and Biases program approach of using errors to study cognitive
bias. The underlying principle behind such research strategy is

22. that while people can make rapid adaptive decisions using
simple and reliable cues, they are still at risk of making errors.
However, these researchers have tried to introduce the role
of costs to theories of cognitive biases. The argument goes
that eliminating errors altogether is rare, if ever possible, but
the costs associated with certain errors may lead organisms to
systematically commit one type of error over another. This
principle is at the heart of error management theory – a theory
that applies evolutionary logic to signal detection theory. Imag-
ine the problem of reliably identifying a recurrent danger in the
environment such as poisonous snakes. For any given relevant
percept (e.g., a long slender object on the ground), one must
make a decision: snake present or snake absent. Because of the
dire consequences of being bitten by a venomous snake, it is
better to have a low evidentiary threshold for inferring that
long slithering objects are snakes so as to identify every snake
you encounter, than to require too much evidence and occa-
sionally incur a costly surprise. Because both types of errors

23. cannot be minimized at the same time, asymmetries in the
costs of two types of errors (false positives and false negatives)
should lead systems to be biased in the direction of the least
costly error.
Examples of such biases can be found in auditory percep-
tion. For example, listeners perceive tones with rising intensity
to change faster than equivalent tones falling in intensity –
an effect termed auditory looming. Auditory looming has
also been found to occur in nonhuman primates and is
well explained in an error management theory framework.
The enhanced saliency of rising intensities associated with
approaching objects causes listeners to reliably underestimate
object arrival time. The bias occurs with tones but not broad-
band noise showing some specificity for sound that provides
reliable single-source information and made almost exclusively
by biological organisms. Of course, any time a bias affects
Encyclopedia of Human Behavior, Se
perception of the physical environment, there are risks of mis -

24. applying it to irrelevant objects that could lead to any variety of
costly errors. The degree to which this is true will largely
determine how advantageous the bias will be, and thus its
impact over evolutionary time. In the case of auditory loom-
ing, the costs of false alarms (e.g., wasting time by being ready
too early) are relatively low compared to the costs of misses
(i.e., not being prepared for an approaching object). The dif-
ference in these costs allows for the selection of a bias that
causes people to systematically overestimate a reliable auditory
cue of movement toward a listener.
Examples of Research on Cognitive Biases
In this section, we introduce two examples of research on
cognitive bias. The first example focuses on search in the exter -
nal world and how people’s perceptions of events or their
co-occurrence may be biased toward frequent, natural distribu-
tions. In this example, cognitive bias arises from experimenters
observing an organism’s behavior or judgments in environ-
ments that are very atypical compared to those experienced
across phylogenetic and/or ontogenetic time. The second

25. example focuses on biases in internal search from memory
and emphasizes that cognitive bias may occur both due to
cognitive limitations and motivational factors. For example,
an individual’s inaccurate recall of poor past performance
may be due to poor memory and/or a motivation to preserve
a positive view of the self.
Foraging, Hot Hands, and the Structure of the Environment
The work of Andreas Wilke and colleagues on human foraging
behavior in patchy environments, illustrates that an awareness
of ancestral conditions can be key to understanding human
decision-making strategies. When resources are distributed
in patches (i.e., areas with a high density of the resource
surrounded by areas with low density), animals are required
not only to make decisions on where to forage, but also on
how long they should forage in a particular patch as resources
diminish. Biologists have studied simple decision mechanisms
that solve this problem of patch time allocation and identified
resource environments where these mechanisms work well.

26. Different patch-leaving strategies are necessary because
resource
environments differ in how resources are distributed across
patches. The number of resource items within a patch can either
be similar (evenly dispersed distributions), completely random
(Poisson distributions), or some patches may only contain a
few items while others will be very resource rich (aggregated
distributions). Wilke and colleagues tested how well humans
can adapt their patch-leaving behavior when faced with such
resource distributions in a computerized foraging game. The
results showed that participants applied patch-leaving rules
that were particularly appropriate for aggregated environments
also in other types of environments (e.g., those with evenly
dispersed and Poisson distributions). Were research partici -
pants ecologically irrational?
This finding is less puzzling once one considers that aggre-
gation in space and time, rather than dispersion, is likely to
have been the norm for most of the natural resources humans

27. cond Edition (2012), vol. 1, pp. 531-535
534 Cognitive Bias
Author's personal copy
encountered over evolutionary time. Species of plants and
animals rarely, if ever, distribute themselves in a purely ran-
dom manner in their natural environment, because individual
organisms are not independent of one another: Whereas
mutual attraction leads to aggregation for some species, mutual
repulsion leads to regularity (dispersed environments) in
others. Most often, these deviations from randomness are in
the direction of aggregation, because aggregation offers consid-
erable benefits such as a common habitat, mating and parent-
ing, or the benefits of group foraging. Since humans have been
hunters and gatherers for a very long part of their history, it
could well be that our evolved psychology is adapted to assume
such aggregated resource distributions as the default. Thus,

28. participants in the foraging experiment might have behaved
evolutionarily rationally.
The idea that humans expect aggregation in space and time
also helps to explain why apparent misconceptions of proba-
bility, such as the hot-hand fallacy, may not reflect fundamen-
tal shortcomings of the human mind but rather adaptation to
the statistical structure of natural environments. The hot-hand
fallacy occurs when research subjects expect lucky streaks in
hits and misses in everything from basketball to coin tosses
when in fact the probabilities of events are independent. When
a basketball player hits many shots in a row, for instance, the
natural expectation is that he has a ‘hot hand’ and will shoot
another successfully. People are often surprised to discover that
this strong intuition does not square with the reality that the
success of the next shot is determined independently from
the shot before it.
The foraging example presented above hints at an
explanation for the hot-hand phenomenon based on limited

29. experience with evolutionarily novel events like coin tosses,
and gambling that involve random events. Instead, one can
ask about the structure of objects and events surrounding
important adaptive problems faced by our ancestors, and
what kinds of adaptations might have been shaped by selec-
tion. Evolutionary behavioral scientists would argue that
many of these – plants, animals, human settlements, and
even weather – would have been organized in an aggregated,
clumpy fashion – not perfectly at random (independent) like
those in Las Vegas. Thus, the default human expectation is
aggregation, clumpiness, and nonindependence. To explore
this hypothesis, Wilke devised additional computer tasks in
which the subject could forage for fruits, coin tosses, and
several other kinds of resources, and present them to American
undergraduates and a South American indigenous population
of hunter-horticulturalists (the Shuar). In each population,
subjects exhibited the hot-hand phenomenon for all resource
types, despite the fact that the resources were distributed

30. randomly by the computer. The one exception found was for
coin tosses for the American students only for which the hot-
hand expectation was reduced though not altogether elimi -
nated. This suggests that the expectation of aggregation in
space and time may be the psychological default that is over -
come only through extensive experience with truly independent
random phenomena like coin tosses. This runs in contrast to the
original explanation offered for the hot-hand phenomenon –
that it is attributable to biased sampling by the mind – and
instead suggests it is a consequence of the minds’ adaptation to
the distribution of resources in the natural environment.
Encyclopedia of Human Behavior, Sec
Memory Biases: Cognitive and Motivational Determinants
Would humans be better off if we had been blessed with
superior cognitive abilities, such as unfailing memories? One
view on the rather limited cognitive capacities of the human
mind is that limitations, such as forgetting, have functional
significance. Some researchers, like John Anderson, have sug-

31. gested that the function of memory is not simply to store
information, but rather provide relevant information in spe-
cific situations. According to this view, the human memory
system is organized such that it facilitates the retrieval of infor -
mation that is recent, frequent, and relevant to the current
context. In other words, memory is designed to provide the
information we are most likely to need. Many man-made
information systems are built in such way. For example, com-
puter applications usually incorporate a timesaving feature as
follows: When a user tries to open a document file, the applica -
tions presents a ‘file buffer,’ a list of recently opened files from
which the user can select. Whenever the desired file is included
on the list, the user is spared the effort of searching through the
file hierarchy. For this device to work efficiently, the applica -
tion must provide the user with the desired file. It does so by
‘forgetting’ files that are considered unlikely to be needed on
the basis of the assumption that the time since a file was last
opened is negatively correlated with its likelihood of being

32. needed now. In other words, such a system has a bias toward
information that is likely to be needed.
Although memory systems are very often efficient, they can
sometimes fail because forgetting and sensitivity to contextual
knowledge may lead to systematic error. The hindsight bias is
one of the most frequently cited and researched cognitive
biases in the psychological literature. Hindsight bias is a type
of memory distortion in which, with the benefit of feedback
about the outcome of an event, people’s recalled judgments are
typically closer to the outcome of the event than their original
judgments were. Research on hindsight bias is particularly
important because it is a ubiquitous phenomenon and one
with potentially detrimental consequences in applied settings,
such as law and medicine.
In the 1970s, Baruch Fischoff was concerned with profes-
sionals such as clinicians’ or politicians exaggerated feeling of
having known all along how patients’ recovery or elections
were
going to turn out. To study this issue empirically, Fischhoff

33. asked participants to assess the probabilities of various possible
outcomes concerning upcoming events, for example, President
Nixon’s historic trips to China and the Soviet Union (e.g., Pres.
Nixon will meet Chairman Mao; Pres. Nixon will announce that
the trip was a success). After the trips, participants were asked
to
recall their predictions. Results showed that participants tended
to exaggerate what they had known in foresight.
There are two common experimental designs that have been
used in the psychological literature. In the memory design, par -
ticipants first make judgments concerning some stimuli, then
receive feedback on some or all of the items, and are finally
asked to recall the original judgments. In the hypothetical
design,
participants first receive feedback concerning some or all of the
items and are then asked to say what they would have estimated
had they not been given feedback. Empirical results using either
design have shown that recalled or hypothetical estimates are
commonly biased toward the feedback information.

34. ond Edition (2012), vol. 1, pp. 531-535
Cognitive Bias 535
Author's personal copy
At present, there is no single theory that can explain all
patterns of data and moderator variables that have been
studied in laboratory or real-world settings (e.g., expertise,
experimental materials). One potential reason for this is that
multiple processes are involved in producing the effect. In fact,
there is largely consensus that the bias is multiply determined,
and involves both cognitive and motivational factors.
Regarding cognitive factors, the prevalent idea is that both
processes of retrieval and reconstruction play a role. For exam-
ple, when reporting the original judgment participants are
likely to both try to retrieve the specific memory of the event
as well as reconstruct the original judgment process. Accord-

35. ingly, the hindsight bias effect can occur by new information
(feedback) biasing (1) the retrieval cues used to query memory
for the original judgment, (2) the reconstruction of the judg-
ment process, (3) or both. This view also suggests a prominent
role for inhibition processes. Accordingly, research shows that
individuals with strong inhibitory deficits have more difficul-
ties inhibiting feedback about the outcome of an event from
entering working memory and thus show increased hindsight
bias. As expected, this is particularly the case when the correct
response is either in sight or accessible in working memory at
the time of the attempt to recall one’s original response.
In addition, there is evidence that hindsight bias may serve
motivational goals. For example, people seem to change the
perceived probabilities of events so that negative events appear
inevitable as a way to mitigate disappointment and personal
blame. However, this seems to occur mostly in situations
people can control and in situations that are unexpected,
suggesting that such phenomena should be interpreted in the

36. light of people’s attempts at preparing for future events. In
other words, these forms of hindsight bias can be seen as
arising from the use of a sense-making process, whereby people
integrate all they know about a topic into a coherent mental
model. In this light, human memory is not so much designed
Encyclopedia of Human Behavior, Se
to accurately reconstruct the past as it is to make sense of it to
better deal with the future.
See also: Cognition and Personality; Defense Mechanisms;
Judgment.
Further Reading
Anderson JR and Milson R (1989) Human memory: An adaptive
perspective.
Psychological Review 96: 703–719.
Blank H, Musch J, and Pohl RF (2007) Hindsight bias: On being
wise after the event.
Social Cognition 25: 1–9.
Fiedler K and Juslin P (2006) Information Sampling and
Adaptive Cognition.
Cambridge: Cambridge University Press.
Gigerenzer G, Todd PM, and the ABC Research Group (1999)
Simple Heuristics that

37. Make Us Smart. New York: Oxford University Press.
Gilovich T, Griffin DW, and Kahneman D (2002) The
Psychology of Intuitive Judgment:
Heuristics and Biases. Cambridge: Cambridge University Press.
Gilovich T, Vallone R, and Tversky A (1985) The hot hand in
basketball: On the
misperception of random sequences. Cognitive Psychology 17:
295–314.
Haselton MG and Buss DM (2000) Error management theory: A
new perspective on
biases in cross-sex mind reading. Journal of Personality and
Social Psychology
78: 81–91.
Kahneman D, Slovic P, and Tversky A (1982) Judgment Under
Uncertainty: Heuristics
and Biases. New York: Cambridge University Press.
Scheibehenne B, Wilke A, and Todd PM (2010) Expectations of
clumpy resources
influence predictions of sequential events. Evolution and
Human Behavior.
Shepard RN (2001) Perceptual-cognitive universals as
reflections of the world.
Behavioral and Brain Sciences 24: 581–601.
Simon HA (1956) Rational choice and the structure of the
environment. Psychological
Review 63: 129–138.
Todd PM, Gigerenzer G, and the ABC Research Group (in
press) Ecological Rationality:

38. Intelligence in the World. New York: Oxford University Press.
Tversky A and Kahneman D (1974) Judgment under uncertainty:
Heuristics and biases.
Science 185: 1124–1131.
Wilke A and Barrett HC (2009a) The hot hand phenomenon as a
cognitive adaptation to
clumped resources. Evolution and Human Behavior 30: 161–
169.
Wilke A, Hutchinson JMC, Todd PM, and Czienskowski U
(2009b) Fishing for the right
words: Decision rules for human foraging behavior in internal
search tasks.
Cognitive Science 33: 497–529.
cond Edition (2012), vol. 1, pp. 531-535
Cognitive BiasGlossaryHeuristics and Biases: A Short History
of Cognitive BiasEcological Rationality: Putting Cognitive
Biases in an Environmental ContextEvolutionary Rationality:
Understanding Why Cognitive Biases OccurExamples of
Research on Cognitive BiasesForaging, Hot Hands, and the
Structure of the EnvironmentMemory Biases: Cognitive and
Motivational DeterminantsFurther Reading