Lecture 3 - Functional Biases in Social Cognition Flashcards
Temporary Goals
These may influence how risk seeking / risk averse someone is.
Self protection goal -> risk averse
Mate attraction goal -> risk taking
Presence of attractive women found to increase risk-taking behavour in young males (Ronay & von Hippel, 2010).
Summary
Humans are not wholly rational creatures.
However rather than being errors, these biases may serve an adaptive function they may be design features rather than design flaws.
Important to understand the “evolution of error” to be able to balance personal, social, political and economic risks (Johnson et al, 2013).
Signal Detection
True positive = hit
False positive = false alarm
False negative = miss
True negative = correct rejection
False negatives are generally more costly.
Biological example of signal detection
Immune response:
more likely to respond when no pathogen present e.g hay fever, than no responding when a real danger is present e.g. immune dysfunction.
Psychological example
Disease detection:
More likely to avoid people who look like they have a disease as it would be more costly to be exposed to a potential pathogen than assume that they are healthy and it is just a cosmetic issue.
Smoke Detector Principle
Nesse (2005)
Smoke detectors make many systematic errors (false positives) but they are functional.
It may not be accurate but it is adaptive.
Error Management Theory
Haselton & Buss (2000)
When making a decision under uncertainty = risk involved. Have evolved to a bias towards those risks that are least reproductively costly.
In these examples the costliness of the errors depends on your sex. For women it is more costly to have a partner that is uncommitted, for men it is more costly to miss out on an opportunity to reproduce.
Johnson et al (2013) EMT offers a framework for understand decision making under uncertainty.
Kin recognition biases
Feeler & Navarette (2004)
FN = failing to detect kin. May be more costly (committing incest).
The costs may be higher for females. Women show greater aversion to incest. Find sex with a friend almost as disgusting, Relevance - women display more false negatives as it is more costly for them to have a disabled child to rear.
Committment under-perception bias
Haselton & Buss (2000)
For women it is more costly to conceive a child with a man that is uncommitted. Cyrus et al (2011) - Found that post-menopausal women do not show this bias. Suggest that this is therefore a functional adaptation.
Clinical implications of EMT
Tolin et al (2014)
Exaggerated cognitive biases may lead to anxiety disorders.
Sexual over-perception bias
Haselton &Buss (2000). Men tend to over-perceive women as expressing sexual interest. Phenomena does not occur with own sister.
Maner et al (2005)
Men primed with mating goals perceived neutral faces of women as being sexually aroused.
Evaluation - not clear whether bias is in behavior or cognition.
Hostile intentions
Haselton & Nettle (2006)
FN = failure to detect hostility
FP = assume hostile intentions when not present.
FN more costly so people biased towards seeing anger in hostile faces and assuming conspiracies.
Taken to extremes may account for paranoid personality disorders.
Beliefs about one’s own talents
Biased towards believing one is talented when one is not.
May contribute to narcissistic personality disorder.
Individual differences
People differ in how risk seeking vs risk averse they are.
Young males are esp biased towards risk taking behavior as they have the most to gain.
Definition of bias
Systematic errors in judgement or inference.
Descriptive theories / models
Describing and explaining how people actually make decisions and judgments.
Normative / prescriptive theories
The optimal way of making judgments - what rational beings would do.
Why do we have biases?
Processing of information is energetically expensive. Heuristics act as short-cuts which save time and energy. however, these lead to systematic errors.
However…
Normative theories assume that the main function of cognition is to generate true beliefs. BUT inaccurate beliefs may be more ADAPTIVE
Tversky & Kahneman (1974)
First to show that humans are not rational beings and that many decisions and judgments are made quickly and unconsciously via heuristics.
Evaluation:
Supported with neuro evidence - different areas activated during controlled and automatic processes (Lieberman, 2007).
Criticised for using evidence that could support a single-systems approach (Girgerenzer, 2010).
2 x 2 matrix
2 correct responses, 2 errors. 1 error is more costly than the other.
E.g. in science we are biased towards making less type I than type II errors are the former are more costly (seeing an effect where there isn’t one).
In-group / out-group biases
Just because we are prone to these biases doesn’t mean we have to act on them. They can be overcome through education and culture. We are living in the era of lowest ever prejudice.
Representativeness Heuristic
Tversky & Kahneman (1974)
For use with categories - e.g. deciding whether someone is a criminal. Compare individual against a PROTOTYPE of that category.
Ignorance of Base Rates
Tversky & Kahneman (1974)
An example of representativeness heuristic.
Rather than making decisions based on base rates and probabilities, people tend to substitute similarity. Kahneman called this WYSIATI (What you see is all there is). When posed with difficult questions we often unconsciously substitute it for as easier one and answer that instead!
Conjunction fallacy
An example of representativeness heuristic.
Breaks rules of probability. E.g. “Bill is intelligent but unimaginative!” people more likely to rate him as an accountant who plays jazz for a hobby than just having jazz as a hobby. In reality the probability of the 2 things occurring together is less than one occuring alone but because people are judging Bill on how representative he is of accountants and jazz players they make an error of judgement.
Availability Heuristic
Availability = ease with which something can be brought to mind.
AH = using this availability when making decisions.
E.g. violent deaths and accidents are easily remembered and brought to mind so people mistakenly believe they are more likely to die in an air crash than from a stroke, which is one of the biggest causes of death.
Criticism of bias reseach
Jussim (2012)
Bias research presents people as low wattage (not that bright, lazy - > irrational conclusions about world). He believes that the data actually shows people as being high-wattage (bright, engaged, energetic => valid conclusions about the world).
Sees biases as only short-lived and weak.
Reviews major contributions to bias literature and believes data supports stereotypes as accurate.
Many studies not replicable (find PLOS one study).
Accuracy studies have received less funding and attention than bias studies.
Evaluation -
