Effective learning is accompanied by high dimensional and efficient representations of neural activity

TL;DR

This study reveals that quick learners exhibit higher dimensional and more efficient neural representations during value learning, characterized by compact embeddings and distinct brain response patterns, advancing understanding of neural coding efficiency.

Contribution

The paper introduces geometric tools to quantify neural response organization, linking high-dimensionality and efficiency to learning speed in human brain responses.

Findings

Quick learners have higher neural response dimensionality.

Fast learners show more compact neural embeddings.

Neural response patterns differ significantly with learning speed.

Abstract

A fundamental cognitive process is the ability to map value and identity onto objects as we learn about them. Exactly how such mental constructs emerge and what kind of space best embeds this mapping remains incompletely understood. Here we develop tools to quantify the space and organization of such a mapping, thereby providing a framework for studying the geometric representations of neural responses as reflected in functional MRI. Considering how human subjects learn the values of novel objects, we show that quick learners have a higher dimensional geometric representation than slow learners, and hence more easily distinguishable whole-brain responses to objects of different value. Furthermore, we find that quick learners display a more compact embedding of their neural responses and hence have a higher ratio of their task-based dimension to their embedding dimension -- consistent with a greater efficiency of cognitive coding. Lastly, we investigate the neurophysiological drivers of high dimensional patterns at both regional and voxel levels, and we complete our study with a complementary test of the distinguishability of associated whole-brain responses. Our results demonstrate a spatial organization of neural responses characteristic of learning, and offer a suite of geometric measures applicable to the study of efficient coding in higher-order cognitive processes more broadly.