Approach-Induced Biases in Human Information Sampling
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Abstract
Information sampling is often biased towards seeking evidence that confirms one's prior beliefs. Despite such biases being a pervasive feature of human behavior, their underlying causes remain unclear. Many accounts of these biases appeal to limitations of human hypothesis testing and cognition, de facto evoking notions of bounded rationality, but neglect more basic aspects of behavioral control. Here, we investigated a potential role for Pavlovian approach in biasing which information humans will choose to sample. We collected a large novel dataset from 32,445 human subjects, making over 3 million decisions, who played a gambling task designed to measure the latent causes and extent of information-sampling biases. We identified three novel approach-related biases, formalized by comparing subject behavior to a dynamic programming model of optimal information gathering. These biases reflected the amount of information sampled ("positive evidence approach"), the selection of which information to sample ("sampling the favorite"), and the interaction between information sampling and subsequent choices ("rejecting unsampled options"). The prevalence of all three biases was related to a Pavlovian approach-avoid parameter quantified within an entirely independent economic decision task. Our large dataset also revealed that individual differences in the amount of information gathered are a stable trait across multiple gameplays and can be related to demographic measures, including age and educational attainment. As well as revealing limitations in cognitive processing, our findings suggest information sampling biases reflect the expression of primitive, yet potentially ecologically adaptive, behavioral repertoires. One such behavior is sampling from options that will eventually be chosen, even when other sources of information are more pertinent for guiding future action.
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The factors that determine when to stop gathering information and commit to a final decision, as well as which options to sample from, remain an active area of investigation.
“the factors that determine when to stop gathering information and commit to a final decision, as well as which options to sample from, remain an active area of investigation1,4,9–11.”
From Interpretable abstractions of artificial neural networks predict behavior and neural activity during human information gathering (2026) · cites this paper as [11]
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- Authors
- Hunt, L. T., Rutledge, R. B., Malalasekera, W. M. N., Kennerley, S. W., & Dolan, R. J.
- Affiliations
- Wellcome Centre for Human Neuroimaging (GB)
- University College London (GB)
- Corresponding author
- Hunt, Laurence T.
- ORCID
- Venue
- PLOS Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2016-11-10
- Volume
- 14
- Issue
- 11
- Pages
- e2000638
- ISSN
- 1544-9173 (ISSN-L)
- 1545-7885
- PMC
- PMC5104460
- OpenAlex
- W2951328700
- Cited by
- 60
- References
- 45
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 9 · 2021: 14 · 2020: 4 · 2019: 11 · 2018: 6 · 2017: 3 · 2016: 1
- FWCI
- 6.07
- Type
- journal article
- Language
- en
- Update notices
- correction (2017-11-30)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- Wellcome Trust (091593, 098362/Z/12/Z, 091593/Z/10/Z, 091593/Z/10/, 096689/Z/11/Z, 098362, 098362/Z/12/Z, 098830/Z/12/Z, /Z/11/Z, 096689, 091593/Z/10/Z, 098830, 098362/Z/12/, 101252/Z/13/Z)
- Astor Foundation
- Rosetrees Trust
- Max-Planck-Gesellschaft (098362/Z/12/Z)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Keywords
- information sampling, confirmation bias, behavioral biases, dynamic programming model, gambling task, individual differences, bounded rationality, behavioral repertoire
- MeSH terms
- Choice Behavior, Decision Making, Humans, Information Services, Bias
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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