Suboptimal human inference can invert the bias-variance trade-off for decisions with asymmetric evidence.
PLoS Comput Biol
; 18(7): e1010323, 2022 07.
Article
em En
| MEDLINE
| ID: mdl-35853038
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.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
1_ASSA2030
Base de dados:
MEDLINE
Assunto principal:
Cognição
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
PLoS Comput Biol
Ano de publicação:
2022
Tipo de documento:
Article