ArticlePLoS computational biology2022
General object-based features account for letter perception.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Identification of letters distorted by physiologically-inspired spatial scrambling.PLoS computational biology · 2026Article
- Self-supervised learning yields representational signatures of category-selective cortex.Communications biology · 2026Article
- Infants have rich visual categories in ventrotemporal cortex at 2 months of age.Nature neuroscience · 2026Article
- Are the early stages of orthographic processing universal? Insights from masked priming with Semitic words.Psychonomic bulletin & review · 2025Article
- Embracing digital innovation in neuroscience: 2023 in review at NEUROCCINO.Brain structure & function · 2024Review
- Severe processing capacity limits for sub-lexical features of letter strings.Attention, perception & psychophysics · 2024Article
- A unified model for cross-modal plasticity and skill acquisition.Frontiers in neuroscience · 2024Review
- Rethinking cortical recycling in ventral temporal cortex.Trends in cognitive sciences · 2024Review
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
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4 authors.
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Abstract
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.
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