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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.

Author's personal copy

Cognitive Bias A Wilke, Clarkson University, Potsdam, NY, USA R Mata, University of Basel, Basel, Switzerland

Glossary Bounded rationality The principle that organisms have

limited resources, such as time, information, and cognitive

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

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

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

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

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

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 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

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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

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-

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

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-

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

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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

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

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

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-

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

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.

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

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534 Cognitive Bias

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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,

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

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

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-

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

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

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.

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Cognitive Bias 535

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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-

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

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.