Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework
When adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture and motion may tell us where these visual illusions...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2015-10-01
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| Series: | Frontiers in Human Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00557/full |
| _version_ | 1818150773166440448 |
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| author | Valero eLaparra Jesús eMalo |
| author_facet | Valero eLaparra Jesús eMalo |
| author_sort | Valero eLaparra |
| collection | DOAJ |
| description | When adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture and motion may tell us where these visual illusions come from. However such empirical models of gain control do not explain why these mechanisms work in this apparently dysfunctional manner.Current normative explanations of aftereffects based on scene statistics derive gain changes by (1) invoking decorrelation and linear manifold matching/equalization, or (2) using nonlinear divisive normalization obtained from parametric scene models. These principled approaches have different drawbacks: the first is not compatible with the known saturation nonlinearities in the sensors and it cannot fully accomplish information maximization due to its linear nature. In the second, gain change is almost determined a priori by the assumed parametric image model linked to divisive normalization.In this study we show that both the response changes that lead to aftereffects and the nonlinear behavior can be simultaneously derived from a single statistical framework: the Sequential Principal Curves Analysis (SPCA). As opposed to mechanistic models, SPCA is not intended to describe how physiological sensors work, but it is focused on explaining why they behave as they do. Nonparametric SPCA has two key advantages as a normative model of adaptation: (i) it is better than linear techniques as it is a flexible equalization that can be tuned for more sensible criteria other than plain decorrelation (either full information maximization or error minimization); and (ii) it makes no a priori functional assumption regarding the nonlinearity, so the saturations emerge directly from the scene data and the goal (and not from the assumed function). It turns out that the optimal responses derived from these more sensible criteria and SPCA are consistent with dysfunctional behaviors such as aftereffects. |
| first_indexed | 2024-12-11T13:28:15Z |
| format | Article |
| id | doaj.art-ee63a860dc6b43c980bb6123fef9545d |
| institution | Directory Open Access Journal |
| issn | 1662-5161 |
| language | English |
| last_indexed | 2024-12-11T13:28:15Z |
| publishDate | 2015-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Human Neuroscience |
| spelling | doaj.art-ee63a860dc6b43c980bb6123fef9545d2022-12-22T01:05:24ZengFrontiers Media S.A.Frontiers in Human Neuroscience1662-51612015-10-01910.3389/fnhum.2015.00557108197Visual Aftereffects and Sensory Nonlinearities from a single Statistical FrameworkValero eLaparra0Jesús eMalo1Universitat de ValenciaUniversitat de ValenciaWhen adapted to a particular scenery our senses may fool us: colors are misinterpreted, certain spatial patterns seem to fade out, and static objects appear to move in reverse. A mere empirical description of the mechanisms tuned to color, texture and motion may tell us where these visual illusions come from. However such empirical models of gain control do not explain why these mechanisms work in this apparently dysfunctional manner.Current normative explanations of aftereffects based on scene statistics derive gain changes by (1) invoking decorrelation and linear manifold matching/equalization, or (2) using nonlinear divisive normalization obtained from parametric scene models. These principled approaches have different drawbacks: the first is not compatible with the known saturation nonlinearities in the sensors and it cannot fully accomplish information maximization due to its linear nature. In the second, gain change is almost determined a priori by the assumed parametric image model linked to divisive normalization.In this study we show that both the response changes that lead to aftereffects and the nonlinear behavior can be simultaneously derived from a single statistical framework: the Sequential Principal Curves Analysis (SPCA). As opposed to mechanistic models, SPCA is not intended to describe how physiological sensors work, but it is focused on explaining why they behave as they do. Nonparametric SPCA has two key advantages as a normative model of adaptation: (i) it is better than linear techniques as it is a flexible equalization that can be tuned for more sensible criteria other than plain decorrelation (either full information maximization or error minimization); and (ii) it makes no a priori functional assumption regarding the nonlinearity, so the saturations emerge directly from the scene data and the goal (and not from the assumed function). It turns out that the optimal responses derived from these more sensible criteria and SPCA are consistent with dysfunctional behaviors such as aftereffects.http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00557/fullunsupervised learningmotion aftereffectcolor aftereffecttexture aftereffectScene statisticsCurves analysis |
| spellingShingle | Valero eLaparra Jesús eMalo Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework Frontiers in Human Neuroscience unsupervised learning motion aftereffect color aftereffect texture aftereffect Scene statistics Curves analysis |
| title | Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework |
| title_full | Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework |
| title_fullStr | Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework |
| title_full_unstemmed | Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework |
| title_short | Visual Aftereffects and Sensory Nonlinearities from a single Statistical Framework |
| title_sort | visual aftereffects and sensory nonlinearities from a single statistical framework |
| topic | unsupervised learning motion aftereffect color aftereffect texture aftereffect Scene statistics Curves analysis |
| url | http://journal.frontiersin.org/Journal/10.3389/fnhum.2015.00557/full |
| work_keys_str_mv | AT valeroelaparra visualaftereffectsandsensorynonlinearitiesfromasinglestatisticalframework AT jesusemalo visualaftereffectsandsensorynonlinearitiesfromasinglestatisticalframework |