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Extracting statistical information about shapes in the visual environment.

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Title: Extracting statistical information about shapes in the visual environment.
Authors: Hansmann-Roth, Sabrina1 Sabrina@hi.is, Chetverikov, Andrey2, Kristjánsson, Árni1
Source: Vision Research. May2023, Vol. 206, pN.PAG-N.PAG. 1p.
Subjects: Visual environment, Optical information processing, Form perception, Visual discrimination, Visual perception
Abstract: It is well known that observers can use so-called summary statistics of visual ensembles to simplify perceptual processing. The assumption has been that instead of representing feature distributions in detail the visual system extracts the mean and variance of visual ensembles. But recent evidence from implicit testing using a method called feature distribution learning showed that far more detail of the distributions is retained than the summary statistic literature indicates. Observers also encode higher-order statistics such as the kurtosis of feature distributions of orientation and color. But this sort of learning has not been shown for more intricate aspects of visual information. Here we tested the learning of distractor ensembles for shape, using the feature distribution learning method. Using a linearized circular shape space, we found that learning of detailed distributions of shape does not occur for this shape space while observers were able to learn the mean and range of the distributions. Previous demonstrations of feature distribution learning involved simpler feature dimensions than the more complex shape space tested here, and our findings may therefore reveal important boundary conditions of feature distribution learning. [ABSTRACT FROM AUTHOR]
Copyright of Vision Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: <searchLink fieldCode="DE" term="%22Visual+environment%22">Visual environment</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+information+processing%22">Optical information processing</searchLink><br /><searchLink fieldCode="DE" term="%22Form+perception%22">Form perception</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+discrimination%22">Visual discrimination</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink>
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  Data: It is well known that observers can use so-called summary statistics of visual ensembles to simplify perceptual processing. The assumption has been that instead of representing feature distributions in detail the visual system extracts the mean and variance of visual ensembles. But recent evidence from implicit testing using a method called feature distribution learning showed that far more detail of the distributions is retained than the summary statistic literature indicates. Observers also encode higher-order statistics such as the kurtosis of feature distributions of orientation and color. But this sort of learning has not been shown for more intricate aspects of visual information. Here we tested the learning of distractor ensembles for shape, using the feature distribution learning method. Using a linearized circular shape space, we found that learning of detailed distributions of shape does not occur for this shape space while observers were able to learn the mean and range of the distributions. Previous demonstrations of feature distribution learning involved simpler feature dimensions than the more complex shape space tested here, and our findings may therefore reveal important boundary conditions of feature distribution learning. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Vision Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.visres.2023.108190
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      – Code: eng
        Text: English
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        PageCount: 1
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      – SubjectFull: Visual environment
        Type: general
      – SubjectFull: Optical information processing
        Type: general
      – SubjectFull: Form perception
        Type: general
      – SubjectFull: Visual discrimination
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      – SubjectFull: Visual perception
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      – TitleFull: Extracting statistical information about shapes in the visual environment.
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            – D: 01
              M: 05
              Text: May2023
              Type: published
              Y: 2023
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