{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T01:00:09Z","timestamp":1773536409058,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010323","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T00:00:00Z","timestamp":1659052800000}}],"reference-count":52,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T00:00:00Z","timestamp":1658188800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01MH115557-01"],"award-info":[{"award-number":["R01MH115557-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Solutions to challenging inference problems are often subject to a fundamental trade-off between: 1) bias (being systematically wrong) that is minimized with complex inference strategies, and 2) variance (being oversensitive to uncertain observations) that is minimized with simple inference strategies. However, this trade-off is based on the assumption that the strategies being considered are optimal for their given complexity and thus has unclear relevance to forms of inference based on suboptimal strategies. We examined inference problems applied to rare, asymmetrically available evidence, which a large population of human subjects solved using a diverse set of strategies that varied in form and complexity. In general, subjects using more complex strategies tended to have lower bias and variance, but with a dependence on the form of strategy that reflected an inversion of the classic bias-variance trade-off: subjects who used more complex, but imperfect, Bayesian-like strategies tended to have lower variance but higher bias because of incorrect tuning to latent task features, whereas subjects who used simpler heuristic strategies tended to have higher variance because they operated more directly on the observed samples but lower, near-normative bias. Our results help define new principles that govern individual differences in behavior that depends on rare-event inference and, more generally, about the information-processing trade-offs that can be sensitive to not just the complexity, but also the optimality, of the inference process.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010323","type":"journal-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T17:36:30Z","timestamp":1658252190000},"page":"e1010323","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6774-2143","authenticated-orcid":true,"given":"Tahra L.","family":"Eissa","sequence":"first","affiliation":[]},{"given":"Joshua I.","family":"Gold","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1975-3913","authenticated-orcid":true,"given":"Kre\u0161imir","family":"Josi\u0107","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2835-9416","authenticated-orcid":true,"given":"Zachary P.","family":"Kilpatrick","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,7,19]]},"reference":[{"issue":"3","key":"pcbi.1010323.ref001","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1038\/s41562-018-0297-4","article-title":"A bias\u2013variance trade-off governs individual differences in on-line learning in an unpredictable environment","volume":"2","author":"CM Glaze","year":"2018","journal-title":"Nature Human Behaviour"},{"key":"pcbi.1010323.ref002","volume-title":"Information science and statistics","author":"CM Bishop","year":"2006"},{"key":"pcbi.1010323.ref003","author":"S Tibshirani","year":"2009"},{"issue":"3","key":"pcbi.1010323.ref004","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1037\/0033-295X.112.3.610","article-title":"How forgetting aids heuristic inference","volume":"112","author":"LJ Schooler","year":"2005","journal-title":"Psychological review"},{"key":"pcbi.1010323.ref005","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-psych-120709-145346","article-title":"Heuristic decision making","volume":"62","author":"G Gigerenzer","year":"2011","journal-title":"Ann Rev Psychol"},{"issue":"1","key":"pcbi.1010323.ref006","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neuron.2012.03.016","article-title":"Not noisy, just wrong: the role of suboptimal inference in behavioral variability","volume":"74","author":"JM Beck","year":"2012","journal-title":"Neuron"},{"issue":"47","key":"pcbi.1010323.ref007","doi-asserted-by":"crossref","first-page":"10143","DOI":"10.1523\/JNEUROSCI.3478-17.2018","article-title":"Visual evidence accumulation guides decision-making in unrestrained mice","volume":"38","author":"O Odoemene","year":"2018","journal-title":"Journal of Neuroscience"},{"issue":"430","key":"pcbi.1010323.ref008","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1080\/01621459.1995.10476572","article-title":"Bayes factors","volume":"90","author":"RE Kass","year":"1995","journal-title":"Journal of the american statistical association"},{"key":"pcbi.1010323.ref009","volume":"29","author":"G Tavoni","year":"2019","journal-title":"What is optimal in optimal inference? 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