{'fraction_non_alphanumeric': 0.016325112886418896, 'fraction_numerical': 0.0006946856547412296, 'mean_word_length': 6.2544080604534, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Exemplar learning reveals the representational origins of expert category perception

Significance Vision science has uncovered several behavioral signatures of highly skilled perception. Subsequent inferences assume that well-differentiated object representations permit fine-grained visual discrimination. However, it remains unknown how representations develop and what governing principles drive changes in representational space more generally. Here, we explore how previous category experience constrains change in representational space during novel exemplar learning. These experiments implicate three types of representational change by which individuals develop visual expertise and the manner in which subsequent exposure within the same domain influences representations. Our findings demonstrate that previous category experience influences the selectivity, locality, and change in dimensionality of representational space that occurs during learning. Together, this evidence reveals a representational mechanism by which highly skilled visual perception emerges. Irrespective of whether one has substantial perceptual expertise for a class of stimuli, an observer invariably encounters novel exemplars from this class. To understand how novel exemplars are represented, we examined the extent to which previous experience with a category constrains the acquisition and nature of representation of subsequent exemplars from that category. Participants completed a perceptual training paradigm with either novel other-race faces (category of experience) or novel computer-generated objects (YUFOs) that included pairwise similarity ratings at the beginning, middle, and end of training, and a 20-d visual search training task on a subset of category exemplars. Analyses of pairwise similarity ratings revealed multiple dissociations between the representational spaces for those learning faces and those learning YUFOs. First, representational distance changes were more selective for faces than YUFOs; trained faces exhibited greater magnitude in representational distance change relative to untrained faces, whereas this trained–untrained distance change was much smaller for YUFOs. Second, there was a difference in where the representational distance changes were observed; for faces, representations that were closer together before training exhibited a greater distance change relative to those that were farther apart before training. For YUFOs, however, the distance changes occurred more uniformly across representational space. Last, there was a decrease in dimensionality of the representational space after training on YUFOs, but not after training on faces. Together, these findings demonstrate how previous category experience governs representational patterns of exemplar learning as well as the underlying dimensionality of the representational space.