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A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning

Lookup NU author(s): Mehdi Sanayei, Professor Alexander ThieleORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© The Author(s) 2025.Visual perceptual learning (VPL), defined as long-term improvement in a visual task, is considered a crucial tool for elucidating underlying visual and brain plasticity. Previous studies have proposed several neural models of VPL, including changes in neural tuning or in noise correlations. Here, to adjudicate different models, we propose that all neural changes at single units can be conceptualized as geometric transformations of population response manifolds in a high-dimensional neural space. Following this neural geometry approach, we identified neural manifold shrinkage due to reduced trial-by-trial population response variability, rather than tuning or correlation changes, as the primary mechanism of VPL. Furthermore, manifold shrinkage successfully explains VPL effects across artificial neural responses in deep neural networks, multivariate blood-oxygenation-level-dependent signals in humans and multiunit activities in monkeys. These converging results suggest that our neural geometry approach comprehensively explains a wide range of empirical results and reconciles previously conflicting models of VPL.

Publication metadata

Author(s): Cheng Y-A, Sanayei M, Chen X, Jia K, Li S, Fang F, Watanabe T, Thiele A, Zhang R-Y

Publication type: Article

Publication status: Published

Journal: Nature Human Behaviour

Year: 2025

Pages: epub ahead of print

Online publication date: 30/03/2025

Acceptance date: 20/02/2025

Date deposited: 14/04/2025

ISSN (electronic): 2397-3374

Publisher: Nature Research

URL: https://doi.org/10.1038/s41562-025-02149-x

DOI: 10.1038/s41562-025-02149-x

Data Access Statement: All data to reproduce the figures in the Article and its Supplementary Information are available via GitHub at https://github.com/ Yu-AngCheng/neural_geometry_VPL. The raw human fMRI and monkey physiological data used in this study were all published previously14,37. Requests for other datasets should be directed to the original authors who collected the data. The code for training neural networks, stimulus generation and neural geometry analysis is publicly available via GitHub at https://github.com/Yu-AngCheng/neural_geometry_VPL

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