ArticleProceedings of the National Academy of Sciences of the United States of America2017
Visual perception as retrospective Bayesian decoding from high- to low-level features.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A circuit model for transsaccadic space updating and mislocalization.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Perceptual learning improves discrimination but does not reduce distortions in appearance.PLoS computational biology · 2025Article
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- Metacognitive evaluation of postdecisional perceptual representations.Journal of vision · 2024Article
- Reference induces biases in late visual processing.Scientific reports · 2023Article
- Memory matching features bias the ensemble perception of facial identity.Frontiers in psychology · 2022Article
- Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers.Entropy (Basel, Switzerland) · 2021Article
- Effects of order on memory of event times.Scientific reports · 2021Article
- Sequence structure organizes items in varied latent states of working memory neural network.eLife · 2021Article
- Categorical judgments do not modify sensory representations in working memory.PLoS computational biology · 2021Article
- The virtual loss function in the summary perception of motion and its limited adjustability.Journal of vision · 2021Article
- Mental geometry of perceiving 3D size in pictures.Journal of vision · 2020Article
- Scene Regularity Interacts With Individual Biases to Modulate Perceptual Stability.Frontiers in neuroscience · 2019Article
- Picture perception reveals mental geometry of 3D scene inferences.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
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4 authors.
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Abstract
When a stimulus is presented, its encoding is known to progress from low- to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low- to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.
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