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Related Experiment Video

Updated: Mar 18, 2026

Studying Food Reward and Motivation in Humans

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

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

Sara Jahfari¹, Jan Theeuwes²

¹Department of Cognitive Psychology, VU University Amsterdam, Amsterdam, Netherlands. Sara.jahfari@gmail.com.

Psychonomic Bulletin & Review

|July 1, 2016

Summary

This summary is machine-generated.

Learning from rewards shapes attention. Faster learning after high rewards increases attentional capture, influencing how we respond to valuable stimuli and distractors.

Keywords:

Bayesian hierarchical modeling Q-learning Reinforcement learning Reward Visual attention

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Last Updated: Mar 18, 2026

Studying Food Reward and Motivation in Humans

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Published on: February 4, 2016

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Published on: January 23, 2017

Area of Science:

Cognitive Neuroscience
Computational Psychiatry
Reinforcement Learning

Background:

Reward learning significantly impacts attentional processes.
Understanding how reward outcomes modulate attentional capture is crucial for cognitive science.
Value-driven attentional capture links external stimuli with internal reward values.

Purpose of the Study:

To investigate the influence of learning rates from high- vs. low-value rewards on attentional capture.
To explore individual differences in reward learning and their effect on attention.
To determine how reward learning transfers to value-driven attentional capture tasks.

Main Methods:

Participants completed an instrumental learning task followed by an attentional capture task.
A hierarchical Bayesian reinforcement learning model inferred individual learning rates.
Analysis correlated learning rates with attentional capture magnitude and distractor performance.

Main Results:

High-reward learning rates strongly correlated with increased attentional capture by high-reward stimuli.
Individual differences in learning from positive and negative outcomes predicted performance with distractors.
Specific learning rates modulated the degree of value-driven attentional capture.

Conclusions:

Reward learning, particularly after positive outcomes, directly influences the automatic capture of attention.
Information updating following desired outcomes shapes future attentional deployment.
Findings offer insights into the mechanisms underlying value-driven attention and learning.