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

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

Artificial neural network · Perception · Population · Visual perception · Computer Science · Neural and Behavioral Psychology Studies · Neural dynamics and brain function · Neuroscience · Psychology · Visual perception and processing mechanisms · Artificial Intelligence

• When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education

  Open Access•Peng Wang, Olga Viberg et al.•Educational Psychology Review•2026

  Add to list