Work

Unsupervised visual learning is revealed for task-irrelevant natural scenes due to reduced attentional suppression effects in visual areas

Year: 2026

Type: article

Abstract: Unsupervised learning—learning through repeated exposure without instruction or reward—is central to both machine learning and human cognition, including language acquisition and statistical learning. However, its role in visual perceptual learning (VPL) remains debated, as previous studies have not shown VPL for task-irrelevant but visible features, particularly in artificial stimuli. Here, we show that task-irrelevant exposure to natural scene images induces robust VPL, while artificial images that lack complex structure characteristics of natural scene images, known as higher-order statistics, do not. Behavioral and fMRI results suggest that although unsupervised learning underlies VPL, it can be suppressed by top-down attention. Higher-order statistics may evade this suppression, possibly because their slower processing reaches visual areas beyond V1 outside the optimal temporal window for attentional suppression. These findings suggest that unsupervised learning underlies VPL, but its occurrence depends on both higher-order stimulus structure and the brain’s attentional gating mechanisms. In this study, the authors show that task-irrelevant exposure to natural scenes, but not matched artificial images, induced visual perceptual learning. Behavioral and fMRI results suggest that higher-order image statistics help learning evade top-down attentional suppression beyond V1.

Source: Nature Communications

DOI: 10.1038/s41467-026-72918-3

Authors: Takeo Watanabe, Yuka Sasaki, Takuro Zama, Julian R. Matthews, Daiki Ogawa, Kazuhisa Shibata

Institutions: Brown University, RIKEN Center for Brain Science, RIKEN, Nagoya University

Language: English

Topic: Visual perception and processing mechanisms

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $6,990