Sensitivity to value-driven attention is predicted by how we learn from value

S. Jahfari, J.L. Theeuwes

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Reward learning is known to influence the automatic capture of attention. This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. Participants performed an instrumental learning task that was directly followed by an attentional capture task. A hierarchical Bayesian reinforcement model was used to infer individual differences in learning from high or low reward. Results showed a strong relationship between high-reward learning rates (or the weight that is put on learning after a high reward) and the magnitude of attentional capture with high-reward colors. Individual differences in learning from high or low rewards were further related to performance differences when high- or low-value distractors were present. These findings provide novel insight into the development of value-driven attentional capture by showing how information updating after desired or undesired outcomes can influence future deployments of automatic attention.

Original languageEnglish
Pages (from-to)408-415
Number of pages8
JournalPsychonomic Bulletin and Review
Volume24
Issue number2
Early online date29 Jun 2016
DOIs
Publication statusPublished - Apr 2017

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