Optimality of Human Contour Integration
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| Publicado en: | PLoS Computational Biology vol. 8, no. 5 (May 2012), p. n/a |
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| Autor principal: | |
| Otros Autores: | , , , , |
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Public Library of Science
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1371/journal.pcbi.1002520 |2 doi | |
| 035 | |a 1313185841 | ||
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| 100 | 1 | |a Ernst, Udo A | |
| 245 | 1 | |a Optimality of Human Contour Integration | |
| 260 | |b Public Library of Science |c May 2012 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy. For processing and segmenting visual scenes, the brain is required to combine a multitude of features and sensory channels. It is neither known if these complex tasks involve optimal integration of information, nor according to which objectives computations might be performed. Here, we investigate if optimal inference can explain contour integration in human subjects. We performed experiments where observers detected contours of curvilinearly aligned edge configurations embedded into randomly oriented distractors. The key feature of our framework is to use a generative process for creating the contours, for which it is possible to derive a class of ideal detection models. This allowed us to compare human detection for contours with different statistical properties to the corresponding ideal detection models for the same stimuli. We then subjected the detection models to realistic constraints and required them to reproduce human decisions for every stimulus as well as possible. By independently varying the four model parameters, we identify a single detection model which quantitatively captures all correlations of human decision behaviour for more than 2000 stimuli from 42 contour ensembles with greatly varying statistical properties. This model reveals specific interactions between edges closely matching independent findings from physiology and psychophysics. These interactions imply a statistics of contours for which edge stimuli are indeed optimally integrated by the visual system, with the objective of inferring the presence of contours in cluttered scenes. The recurrent algorithm of our model makes testable predictions about the temporal dynamics of neuronal populations engaged in contour integration, and it suggests a strong directionality of the underlying functional anatomy. | |
| 650 | 2 | 2 | |a Computer Simulation |
| 650 | 1 | 2 | |a Form Perception |x physiology |
| 650 | 2 | 2 | |a Humans |
| 650 | 1 | 2 | |a Models, Neurological |
| 650 | 1 | 2 | |a Models, Statistical |
| 650 | 1 | 2 | |a Pattern Recognition, Physiological |x physiology |
| 650 | 1 | 2 | |a Perceptual Masking |x physiology |
| 653 | |a Studies | ||
| 653 | |a Anatomy & physiology | ||
| 653 | |a Statistical methods | ||
| 653 | |a Behavior | ||
| 653 | |a Experiments | ||
| 653 | |a Principles | ||
| 653 | |a Contours | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Mandon, Sunita | |
| 700 | 1 | |a Schinkel-Bielefeld, Nadja | |
| 700 | 1 | |a Neitzel, Simon D | |
| 700 | 1 | |a Kreiter, Andreas K | |
| 700 | 1 | |a Pawelzik, Klaus R | |
| 773 | 0 | |t PLoS Computational Biology |g vol. 8, no. 5 (May 2012), p. n/a | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1313185841/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1313185841/fulltext/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1313185841/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |