Perceptual learning improves discrimination but does not reduce distortions in appearance.
Human perceptual sensitivity often improves with training, a phenomenon known as "perceptual learning." Another important perceptual dimension is appearance, the subjective sense of stimulus magnitude. Are training-induced improvements in sensitivity accompanied by more accurate appearance...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012980 |
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| author | Sarit F A Szpiro Charlie S Burlingham Eero P Simoncelli Marisa Carrasco |
| author_facet | Sarit F A Szpiro Charlie S Burlingham Eero P Simoncelli Marisa Carrasco |
| author_sort | Sarit F A Szpiro |
| collection | DOAJ |
| description | Human perceptual sensitivity often improves with training, a phenomenon known as "perceptual learning." Another important perceptual dimension is appearance, the subjective sense of stimulus magnitude. Are training-induced improvements in sensitivity accompanied by more accurate appearance? Here, we examined this question by measuring both discrimination (sensitivity) and estimation (appearance) responses to near-horizontal motion directions, which are known to be repulsed away from horizontal. Participants performed discrimination and estimation tasks before and after training in either the discrimination or the estimation task or none (control group). Human observers who trained in either discrimination or estimation exhibited improvements in discrimination accuracy, but estimation repulsion did not decrease; instead, it either persisted or increased. Hence, distortions in perception can be exacerbated after perceptual learning. We developed a computational observer model in which perceptual learning arises from increases in the precision of underlying neural representations, which explains this counterintuitive finding. For each observer, the fitted model accounted for discrimination performance, the distribution of estimates, and their changes with training. Our empirical findings and modeling suggest that learning enhances distinctions between categories, a potentially important aspect of real-world perception and perceptual learning. |
| format | Article |
| id | doaj-art-bc8f655000814aa19dfab4b7d6aa7ec1 |
| institution | OA Journals |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-bc8f655000814aa19dfab4b7d6aa7ec12025-08-20T02:27:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-04-01214e101298010.1371/journal.pcbi.1012980Perceptual learning improves discrimination but does not reduce distortions in appearance.Sarit F A SzpiroCharlie S BurlinghamEero P SimoncelliMarisa CarrascoHuman perceptual sensitivity often improves with training, a phenomenon known as "perceptual learning." Another important perceptual dimension is appearance, the subjective sense of stimulus magnitude. Are training-induced improvements in sensitivity accompanied by more accurate appearance? Here, we examined this question by measuring both discrimination (sensitivity) and estimation (appearance) responses to near-horizontal motion directions, which are known to be repulsed away from horizontal. Participants performed discrimination and estimation tasks before and after training in either the discrimination or the estimation task or none (control group). Human observers who trained in either discrimination or estimation exhibited improvements in discrimination accuracy, but estimation repulsion did not decrease; instead, it either persisted or increased. Hence, distortions in perception can be exacerbated after perceptual learning. We developed a computational observer model in which perceptual learning arises from increases in the precision of underlying neural representations, which explains this counterintuitive finding. For each observer, the fitted model accounted for discrimination performance, the distribution of estimates, and their changes with training. Our empirical findings and modeling suggest that learning enhances distinctions between categories, a potentially important aspect of real-world perception and perceptual learning.https://doi.org/10.1371/journal.pcbi.1012980 |
| spellingShingle | Sarit F A Szpiro Charlie S Burlingham Eero P Simoncelli Marisa Carrasco Perceptual learning improves discrimination but does not reduce distortions in appearance. PLoS Computational Biology |
| title | Perceptual learning improves discrimination but does not reduce distortions in appearance. |
| title_full | Perceptual learning improves discrimination but does not reduce distortions in appearance. |
| title_fullStr | Perceptual learning improves discrimination but does not reduce distortions in appearance. |
| title_full_unstemmed | Perceptual learning improves discrimination but does not reduce distortions in appearance. |
| title_short | Perceptual learning improves discrimination but does not reduce distortions in appearance. |
| title_sort | perceptual learning improves discrimination but does not reduce distortions in appearance |
| url | https://doi.org/10.1371/journal.pcbi.1012980 |
| work_keys_str_mv | AT saritfaszpiro perceptuallearningimprovesdiscriminationbutdoesnotreducedistortionsinappearance AT charliesburlingham perceptuallearningimprovesdiscriminationbutdoesnotreducedistortionsinappearance AT eeropsimoncelli perceptuallearningimprovesdiscriminationbutdoesnotreducedistortionsinappearance AT marisacarrasco perceptuallearningimprovesdiscriminationbutdoesnotreducedistortionsinappearance |