Optimal policy for attention-modulated decisions explains human fixation behavior.
decision-making
diffusion models
human
neuroscience
optimality
visual attention
Journal
eLife
ISSN: 2050-084X
Titre abrégé: Elife
Pays: England
ID NLM: 101579614
Informations de publication
Date de publication:
26 03 2021
26 03 2021
Historique:
received:
24
09
2020
accepted:
17
03
2021
pubmed:
27
3
2021
medline:
14
9
2021
entrez:
26
3
2021
Statut:
epublish
Résumé
Traditional accumulation-to-bound decision-making models assume that all choice options are processed with equal attention. In real life decisions, however, humans alternate their visual fixation between individual items to efficiently gather relevant information (Yang et al., 2016). These fixations also causally affect one's choices, biasing them toward the longer-fixated item (Krajbich et al., 2010). We derive a normative decision-making model in which attention enhances the reliability of information, consistent with neurophysiological findings (Cohen and Maunsell, 2009). Furthermore, our model actively controls fixation changes to optimize information gathering. We show that the optimal model reproduces fixation-related choice biases seen in humans and provides a Bayesian computational rationale for this phenomenon. This insight led to additional predictions that we could confirm in human data. Finally, by varying the relative cognitive advantage conferred by attention, we show that decision performance is benefited by a balanced spread of resources between the attended and unattended items.
Identifiants
pubmed: 33769284
doi: 10.7554/eLife.63436
pii: 63436
pmc: PMC8064754
doi:
pii:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIMH NIH HHS
ID : R01 MH115554
Pays : United States
Organisme : James S. McDonnell Foundation
ID : 220020462
Informations de copyright
© 2021, Jang et al.
Déclaration de conflit d'intérêts
AJ, RS, JD No competing interests declared
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