title: Sensitivity to value-driven attention is predicted by how we learn from value.
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.
authors.affiliation: Department of Cognitive Psychology, VU University Amsterdam, Amsterdam, Netherlands. Sara.jahfari@gmail.com.
authors.forename: Sara
authors.initials: S
authors.lastname: Jahfari
authors.affiliation: Department of Cognitive Psychology, VU University Amsterdam, Amsterdam, Netherlands.
authors.forename: Jan
authors.initials: J
authors.lastname: Theeuwes
mesh_terms: D000293:Adolescent; D000328:Adult; D001288:Attention; D001499:Bayes Theorem; D003118:Color Perception; D003216:Conditioning, Operant; D005246:Feedback; D005260:Female; D006801:Humans; D007206:Individuality; D008297:Male; D009042:Motivation; D010364:Pattern Recognition, Visual; D011337:Probability Learning; D011930:Reaction Time; D012054:Reinforcement, Psychology; D012201:Reward; D055815:Young Adult
keywords: Bayesian hierarchical modeling; Q-learning; Reinforcement learning; Reward; Visual attention