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title: How visual learning happens in the brain | daily.dev
description: MIT McGovern Institute researchers combined brain activity recordings with artificial neural network modeling to study how visual learning reshapes the brain....
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# How visual learning happens in the brain

**[MIT News](https://daily.dev/sources/mit)** · 6 min read · 54 upvotes · 3 comments

## Summary

MIT McGovern Institute researchers combined brain activity recordings with artificial neural network modeling to study how visual learning reshapes the brain. By training animals and brain-architecture-inspired AI models on the same object recognition tasks, they found subtle but reliable changes in the inferior temporal (IT) cortex during learning. The AI models mirrored these biological changes, validating their use as tools for predicting how training affects perception. Most learning-related changes occurred outside the IT cortex, pointing to downstream brain areas as key contributors. The findings suggest that learning one object type slightly alters IT cortex representations in ways that may improve recognition of related features while potentially hindering others — insights that could inform educational strategies for diverse learners, including those with atypical sensory processing.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://news.mit.edu/2026/how-visual-learning-happens-in-the-brain-0714>

## Questions this post answers

### Does the brain's visual cortex change when you learn to recognize a new type of object?

Yes, but only subtly. Recordings from the inferior temporal (IT) cortex in trained versus untrained animals showed the broad pattern of activity stayed largely similar, but reliable, subtle differences emerged in how neurons responded to newly learned object categories, with most of the learning-related reorganization occurring in brain areas downstream of the IT cortex.

_daily.dev surfaces neuroscience and AI-model research for engineers building brain-inspired systems._

### Can artificial neural networks trained with gradient descent predict how the brain learns to recognize objects?

Artificial neural networks mapped to the IT cortex and trained with gradient descent reproduced learning-related changes that matched those observed in animal brains, even though gradient descent is considered biologically implausible as a direct model of brain learning. Only models whose learning behavior matched the animals' showed this parallel reorganization in their IT-like stage.

_developers exploring biologically inspired ai models can track findings like this via daily.dev._

## Community discussion

Top comments from developers on daily.dev.

**@hardik15** · 7 upvotes

> The picture looks more like a AI neural network learning than a brain neural network, as now our brain used to see AI everywhere

**@lgsx** · 0 upvotes

> It is interesting to see parallels between human neurons and the connections made in neural networks. Even in some emergent behavior when it comes to perceived flaws and "hallucinations" found in neural networks.

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---

Tags: [#machine-learning](https://daily.dev/tags/machine-learning), [#neural-networks](https://daily.dev/tags/neural-networks)

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