Psychological and Brain Sciences, Indiana University.
Cogn Sci. 2021 May;45(5):e12981. doi: 10.1111/cogs.12981.
We explore different ways in which the human visual system can adapt for perceiving and categorizing the environment. There are various accounts of supervised (categorical) and unsupervised perceptual learning, and different perspectives on the functional relationship between perception and categorization. We suggest that common experimental designs are insufficient to differentiate between hypothesized perceptual learning mechanisms and reveal their possible interplay. We propose a relatively underutilized way of studying potential categorical effects on perception, and we test the predictions of different perceptual learning models using a two-dimensional, interleaved categorization-plus-reconstruction task. We find evidence that the human visual system adapts its encodings to the feature structure of the environment, uses categorical expectations for robust reconstruction, allocates encoding resources with respect to categorization utility, and adapts to prevent miscategorizations.
我们探索了人类视觉系统适应感知和分类环境的不同方式。有各种关于监督（类别）和无监督感知学习的解释，以及对感知和分类之间功能关系的不同看法。我们认为，常见的实验设计不足以区分假设的感知学习机制，并揭示它们可能的相互作用。我们提出了一种相对未被充分利用的方法来研究潜在的类别对感知的影响，并使用二维交错分类加重建任务来测试不同感知学习模型的预测。我们发现有证据表明，人类视觉系统会根据环境的特征结构来调整其编码，利用类别期望进行稳健的重建，根据分类效用分配编码资源，并适应以防止错误分类。
