收录解读
This Nature Communications paper reopens the question of unsupervised visual perceptual learning by showing that task-irrelevant natural scenes can produce learning where artificial images do not.
The proposed mechanism is a timing interaction between higher-order natural-scene statistics and top-down attentional suppression, with slower processing beyond V1 escaping the suppression window.
The AI relevance is conceptual but strong: it clarifies when unsupervised exposure can shape visual representations and how attention gates learning from irrelevant streams.
For the repository, this is a selective cognitive-neuroscience inclusion because it connects natural-scene statistics, attention, and unsupervised representation learning.
原始摘要与中文对照
中文对照翻译
任务无关的自然场景的无监督视觉学习被揭示，这归因于视觉区域中注意抑制效应的减弱。无监督学习——通过重复接触而非指令或奖励进行的学习——是机器学习和人类认知（包括语言习得和统计学习）的核心。然而，其在视觉知觉学习 (VPL) 中的作用仍存在争议，因为以往的研究尚未显示任务无关但可见的特征（特别是在人工刺激中）存在 VPL。在本研究中，我们表明任务无关地接触自然场景图像会诱导稳健的 VPL，而缺乏自然场景图像复杂结构特征（即高阶统计特性）的人工图像则不会。行为学和fMRI结果表明，尽管无监督学习是VPL的基础，但它可能受到自上而下注意的抑制。高阶统计特性可能规避这种抑制，这可能是因为其较慢的处理速度使其在注意抑制的最佳时间窗口之外到达V1以外的视觉区域。这些发现表明，无监督学习是VPL的基础，但其发生取决于高阶刺激结构和大脑的注意门控机制。
原始摘要
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.