Individual Differences in the Perception of Probability
| Autor: | Michael Woodford, Mel W. Khaw, Luminita Stevens |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Normal Distribution
Individuality Social Sciences Task (project management) Mathematical and Statistical Techniques Learning and Memory Cognition 0302 clinical medicine Econometrics Range (statistics) Psychology Probability Estimation Biology (General) Extreme value theory media_common Mathematics Ecology Experimental Design Statistics 05 social sciences Variation (linguistics) Computational Theory and Mathematics Research Design Modeling and Simulation Physical Sciences Probability distribution Research Article QH301-705.5 media_common.quotation_subject Decision Making Context (language use) Research and Analysis Methods 050105 experimental psychology Normal distribution Judgment Cellular and Molecular Neuroscience 03 medical and health sciences Probability theory Perception Genetics Learning Humans Computer Simulation 0501 psychology and cognitive sciences Statistical Methods Molecular Biology Ecology Evolution Behavior and Systematics Probability Cognitive Psychology Biology and Life Sciences Behavioral pattern Bayes Theorem Probability Theory Probability Distribution Cognitive Science 030217 neurology & neurosurgery Forecasting Neuroscience Overconfidence effect |
| Zdroj: | PLoS Computational Biology PLoS Computational Biology, Vol 17, Iss 4, p e1008871 (2021) |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3446790 |
| Popis: | In recent studies of humans estimating non-stationary probabilities, estimates appear to be unbiased on average, across the full range of probability values to be estimated. This finding is surprising given that experiments measuring probability estimation in other contexts have often identified conservatism: individuals tend to overestimate low probability events and underestimate high probability events. In other contexts, repulsive biases have also been documented, with individuals producing judgments that tend toward extreme values instead. Using extensive data from a probability estimation task that produces unbiased performance on average, we find substantial biases at the individual level; we document the coexistence of both conservative and repulsive biases in the same experimental context. Individual biases persist despite extensive experience with the task, and are also correlated with other behavioral differences, such as individual variation in response speed and adjustment rates. We conclude that the rich computational demands of our task give rise to a variety of behavioral patterns, and that the apparent unbiasedness of the pooled data is an artifact of the aggregation of heterogeneous biases. Author summary Humans often misrepresent probabilities, frequencies, and proportions they encounter—either overestimating or underestimating the true underlying values. Understanding the cognitive and neural representation of such quantities is important, as probabilities are present in all kinds of impactful decisions—e.g., as we assess the likelihood of a dangerous event or the probable returns on a financial investment. Despite the ubiquitous observation that humans are biased in estimating probabilities, a new laboratory task reportedly elicits unbiased performance. This task involves predicting the likelihood of a green ring emerging from a box containing red and green rings; as subjects draw rings and report their estimates, the ring composition of the box itself changes infrequently. Here we perform a novel and thorough analysis of individuals’ performance on one such experiment, showing that people are indeed biased in idiosyncratic ways. These patterns are unexamined by studies focusing on average performance: some people routinely report an exaggerated range of probabilities, some favor intermediate values, while others are approximately unbiased. These biases persist across hours of experience and accompany other tendencies such as quickness in response. We show that subjects’ idiosyncrasies can be understood as arising from various cognitive mechanisms; nonetheless, subjects’ reports are best described as resembling a distorted version of the optimal statistical estimate. |
| Databáze: | OpenAIRE |
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