Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves
Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract...
| Main Authors: | , , |
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| Format: | Article |
| Language: | en_US |
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American Physical Society
2013
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| Online Access: | http://hdl.handle.net/1721.1/79587 https://orcid.org/0000-0001-8420-8973 |
| _version_ | 1826215314802606080 |
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| author | Shouval, Harel Z. Agarwal, Animesh Gavornik, Jeffrey |
| author2 | Picower Institute for Learning and Memory |
| author_facet | Picower Institute for Learning and Memory Shouval, Harel Z. Agarwal, Animesh Gavornik, Jeffrey |
| author_sort | Shouval, Harel Z. |
| collection | MIT |
| description | Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber’s law remains unknown. This work presents a simple theory explaining the conditions under which Weber’s law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber’s law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber’s law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber’s law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber’s law and may represent a general governing principle relating perception to neural activity. |
| first_indexed | 2024-09-23T16:23:21Z |
| format | Article |
| id | mit-1721.1/79587 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:23:21Z |
| publishDate | 2013 |
| publisher | American Physical Society |
| record_format | dspace |
| spelling | mit-1721.1/795872022-10-02T07:51:35Z Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves Shouval, Harel Z. Agarwal, Animesh Gavornik, Jeffrey Picower Institute for Learning and Memory Gavornik, Jeffrey Weber’s law, first characterized in the 19th century, states that errors estimating the magnitude of perceptual stimuli scale linearly with stimulus intensity. This linear relationship is found in most sensory modalities, generalizes to temporal interval estimation, and even applies to some abstract variables. Despite its generality and long experimental history, the neural basis of Weber’s law remains unknown. This work presents a simple theory explaining the conditions under which Weber’s law can result from neural variability and predicts that the tuning curves of neural populations which adhere to Weber’s law will have a log-power form with parameters that depend on spike-count statistics. The prevalence of Weber’s law suggests that it might be optimal in some sense. We examine this possibility, using variational calculus, and show that Weber’s law is optimal only when observed real-world variables exhibit power-law statistics with a specific exponent. Our theory explains how physiology gives rise to the behaviorally characterized Weber’s law and may represent a general governing principle relating perception to neural activity. National Institutes of Health (U.S.) (Grant R01MH093665) 2013-07-11T18:45:50Z 2013-07-11T18:45:50Z 2013-04 2012-12 Article http://purl.org/eprint/type/JournalArticle 0031-9007 1079-7114 http://hdl.handle.net/1721.1/79587 Shouval, Harel Z., Animesh Agarwal, and Jeffrey P. Gavornik. Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves. Physical Review Letters 110, no. 16 (April 2013). © 2013 American Physical Society https://orcid.org/0000-0001-8420-8973 en_US http://dx.doi.org/10.1103/PhysRevLett.110.168102 Physical Review Letters Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Physical Society APS |
| spellingShingle | Shouval, Harel Z. Agarwal, Animesh Gavornik, Jeffrey Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title | Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title_full | Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title_fullStr | Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title_full_unstemmed | Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title_short | Scaling of Perceptual Errors Can Predict the Shape of Neural Tuning Curves |
| title_sort | scaling of perceptual errors can predict the shape of neural tuning curves |
| url | http://hdl.handle.net/1721.1/79587 https://orcid.org/0000-0001-8420-8973 |
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