In:
PLOS Computational Biology, Public Library of Science (PLoS), Vol. 18, No. 7 ( 2022-7-19), p. e1010323-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1553-7358
DOI:
10.1371/journal.pcbi.1010323
DOI:
10.1371/journal.pcbi.1010323.g001
DOI:
10.1371/journal.pcbi.1010323.g002
DOI:
10.1371/journal.pcbi.1010323.g003
DOI:
10.1371/journal.pcbi.1010323.g004
DOI:
10.1371/journal.pcbi.1010323.g005
DOI:
10.1371/journal.pcbi.1010323.g006
DOI:
10.1371/journal.pcbi.1010323.s001
DOI:
10.1371/journal.pcbi.1010323.s002
DOI:
10.1371/journal.pcbi.1010323.s003
DOI:
10.1371/journal.pcbi.1010323.s004
DOI:
10.1371/journal.pcbi.1010323.s005
DOI:
10.1371/journal.pcbi.1010323.s006
DOI:
10.1371/journal.pcbi.1010323.s007
DOI:
10.1371/journal.pcbi.1010323.s008
DOI:
10.1371/journal.pcbi.1010323.s009
DOI:
10.1371/journal.pcbi.1010323.s010
DOI:
10.1371/journal.pcbi.1010323.s011
DOI:
10.1371/journal.pcbi.1010323.s012
DOI:
10.1371/journal.pcbi.1010323.s013
DOI:
10.1371/journal.pcbi.1010323.s014
DOI:
10.1371/journal.pcbi.1010323.s015
DOI:
10.1371/journal.pcbi.1010323.s016
DOI:
10.1371/journal.pcbi.1010323.s017
DOI:
10.1371/journal.pcbi.1010323.s018
DOI:
10.1371/journal.pcbi.1010323.s019
DOI:
10.1371/journal.pcbi.1010323.s020
DOI:
10.1371/journal.pcbi.1010323.s021
DOI:
10.1371/journal.pcbi.1010323.s022
DOI:
10.1371/journal.pcbi.1010323.s023
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2022
detail.hit.zdb_id:
2193340-6
Permalink