Presentation | 2009-01-20 A Neural Network Model Explaining the Motion Detection Sensitivity Enhanced and Degraded by Induced Motion Satohiro TAJIMA, Hiromasa TAKEMURA, Ikuya MURAKAMI, Masato OKADA, |
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Abstract(in English) | Motion in the visual context is known to cause a repulsive bias in the perception of the target motion. This phenomenon is called 'induced motion'. Recently, Takemura and Murakami (VSS, 2008) reported that the detection sensitivity to the target motion can be enhanced (or degraded) by adding a slow (or fast) surrounding motion in an orthogonal direction, even though here the illusory motion component caused by the surround is not relevant to the task. It is not computationally clear how the task-irrelevant component of motion modulates the subject's sensitivity to motion detection. In the present study we analyzed as well as simulated the effects of encoding biases on the detection performance by modeling the stochastic neural population activities. We found that the motion detection performance of the ideal observer was enhanced or degraded by a task-irrelevant, additive motion component, replicating the properties observed in the real subjects. This means that the enhancement and degradation found in detection sensitivity can be understood as consequences of the noisy neural encoding that limits the resolution of information transmission in the cortical visual processing pathway. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Motion perception / Induced motion / Neural population coding / Noise / Maximum likelihood estimation / Ideal observer / Detection sensitivity |
Paper # | NC2008-99 |
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Committee | NC |
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Conference Date | 2009/1/12(1days) |
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Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A Neural Network Model Explaining the Motion Detection Sensitivity Enhanced and Degraded by Induced Motion |
Sub Title (in English) | |
Keyword(1) | Motion perception |
Keyword(2) | Induced motion |
Keyword(3) | Neural population coding |
Keyword(4) | Noise |
Keyword(5) | Maximum likelihood estimation |
Keyword(6) | Ideal observer |
Keyword(7) | Detection sensitivity |
1st Author's Name | Satohiro TAJIMA |
1st Author's Affiliation | Department of Complexity Science and Engineering, The University of Tokyo() |
2nd Author's Name | Hiromasa TAKEMURA |
2nd Author's Affiliation | Department of Life Sciences, The University of Tokyo |
3rd Author's Name | Ikuya MURAKAMI |
3rd Author's Affiliation | Department of Life Sciences, The University of Tokyo |
4th Author's Name | Masato OKADA |
4th Author's Affiliation | Department of Complexity Science and Engineering, The University of Tokyo:Brain Science Institute, RIKEN |
Date | 2009-01-20 |
Paper # | NC2008-99 |
Volume (vol) | vol.108 |
Number (no) | 383 |
Page | pp.pp.- |
#Pages | 6 |
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