Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America.
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2022 Sep 26;18(9):e1010522. doi: 10.1371/journal.pcbi.1010522. eCollection 2022 Sep.
After years of experience, humans become experts at perceiving letters. Is this visual capacity attained by learning specialized letter features, or by reusing general visual features previously learned in service of object categorization? To explore this question, we first measured the perceptual similarity of letters in two behavioral tasks, visual search and letter categorization. Then, we trained deep convolutional neural networks on either 26-way letter categorization or 1000-way object categorization, as a way to operationalize possible specialized letter features and general object-based features, respectively. We found that the general object-based features more robustly correlated with the perceptual similarity of letters. We then operationalized additional forms of experience-dependent letter specialization by altering object-trained networks with varied forms of letter training; however, none of these forms of letter specialization improved the match to human behavior. Thus, our findings reveal that it is not necessary to appeal to specialized letter representations to account for perceptual similarity of letters. Instead, we argue that it is more likely that the perception of letters depends on domain-general visual features.
经过多年的经验积累，人类成为了识别字母的专家。这种视觉能力是通过学习专门的字母特征获得的，还是通过重新利用之前用于对象分类的通用视觉特征获得的？为了探索这个问题，我们首先在视觉搜索和字母分类这两个行为任务中测量了字母的感知相似性。然后，我们分别在 26 路字母分类或 1000 路对象分类的基础上训练深度卷积神经网络，以此来分别实现可能的专门字母特征和基于对象的通用特征。我们发现，基于对象的通用特征与字母的感知相似性更具相关性。然后，我们通过用不同形式的字母训练来改变对象训练网络，实现了其他形式的经验依赖性字母专业化；然而，这些形式的字母专业化都没有改善与人类行为的匹配。因此，我们的研究结果表明，没有必要诉诸于专门的字母表示来解释字母的感知相似性。相反，我们认为，字母的感知更可能取决于通用的视觉特征。
