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.
Highlights
Reward learning is known to influence the automatic capture of attention
Reward-driven attentional biases are known to develop as a consequence of implicit stimulus–reward associations learned in the past
Influential learning theories suggest that when an organism receives new information, current beliefs are updated in proportion to the difference between expected and actual outcomes
Summary
This study examined how the rate of learning, after high- or low-value reward outcomes, can influence future transfers into value-driven attentional capture. 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. It is well known that attention can be captured automatically by salience, past experiences, and learned reward associations (Awh, Belopolsky, & Theeuwes, 2012). Reinforcement studies provide a possible explanation for this effect: cognitive models that are used to predict trial-to-trial learning behavior generally show higher rates for positive outcomes relative to negatives ones (Frank et al, 2007; Kahnt et al, 2009). Stimulus beliefs are updated more instantly after positive outcomes and might underlie the stronger development of attentional capture for high-reward value
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