# Task learning increases information redundancy of neural responses in macaque visual cortex

arXiv: https://arxiv.org/abs/2603.07369 · HTML: https://mlsanity.com/paper/2603.07369/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 67 references that are in the map.
- First posted on arXiv 7 Mar 2026.

## Builds on (most cited first, 2 of 2)

- [On Simplicity and Complexity in the Brave New World of Large-Scale Neuroscience](https://mlsanity.com/paper/1503.08779/) (2015)
- [The implications of perception as probabilistic inference for correlated neural variability during behavior](https://mlsanity.com/paper/1409.0257/) (2014)

## Abstract (arXiv)

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.