Task learning increases information redundancy of neural responses in macaque visual cortex
Summary
How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests that learning reduces redundancy in neural representations to improve efficiency, whereas another, based on Bayesian inference, predicts that learning increases redundancy by distributing information across neurons. We tested these hypotheses by tracking population responses in macaque cortical area V4 as monkeys learned visual discrimination tasks. We found strong support for the Baye
Content
# Task learning increases information redundancy of neural responses in macaque visual cortex
*Published: 2026 Mar 5*
How does the brain optimize sensory information for decision-making in new
tasks? One hypothesis suggests that learning reduces redundancy in neural
representations to improve efficiency, whereas another, based on Bayesian
inference, predicts that learning increases redundancy by distributing
information across neurons. We tested these hypotheses by tracking population
responses in macaque cortical area V4 as monkeys learned visual discrimination
tasks. We found strong support for the Bayesian predictions: Task learning
increased redundancy in neural responses over weeks of training and within
single trials. This redundancy did not reduce information but instead increased
the information carried by individual neurons. These insights suggest that
sensory processing in the brain reflects a generative rather than discriminative
inference process.
DOI: 10.1126/science.adw7707