Task learning increases information redundancy of neural responses in macaque visual cortex
2026-03-07 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how the brain changes its activity when monkeys learn new visual tasks. They tested two ideas: one says learning makes brain signals less repetitive, and the other says learning spreads information more evenly across brain cells. Their results support the second idea, showing that learning actually increases overlapping information among neurons, which helps each neuron carry more useful info. This suggests the brain processes sensory info by building a model of the world rather than just distinguishing things.
neural redundancyBayesian inferencevisual discriminationcortical area V4neuronal population responsessensory processinggenerative modelsdiscriminative inference
Authors
Shizhao Liu, Anton Pletenev, Ralf M. Haefner, Adam C. Snyder
Abstract
How does the brain optimize sensory information for decision-making in new tasks? One hypothesis suggests learning reduces redundancy in neural representations to improve efficiency, while another, based on Bayesian inference, predicts 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 sensory processing in the brain reflects a generative rather than discriminative inference process.