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Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

Groen II, Ghebreab S, Lamme VA, Scholte HS - PLoS Comput. Biol. (2012)

Bottom Line: We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis).Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions.These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroscience Group, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

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Results of RDM analysis.(A), Maximum and mean Euclidean distance for the subject-averaged RDM: for both measures, highest dissimilarity between images was found at 101 ms after stimulus-onset. (B), Mean RDM across subjects at the moment of maximal Euclidean distance. Each cell of the matrix reflects the dissimilarity (red = high, blue = low) between two individual images, whose category is indexed on the x- and y-axis. (C), Dissimilarity matrices based on difference in contrast statistics between individual images. Color values indicate the summed difference between two individual images in beta and gamma (Weibull statistics), intercept and slope (Fourier statistics), skewness and kurtosis (distribution statistics). (D), Correlation between the RDM and each of the three dissimilarity matrices at each time-point. Highest correlation is found for Weibull statistics at 109 ms. Shaded areas reflect 95% confidence intervals obtained from a percentile bootstrap on the dissimilarity values.
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pcbi-1002726-g007: Results of RDM analysis.(A), Maximum and mean Euclidean distance for the subject-averaged RDM: for both measures, highest dissimilarity between images was found at 101 ms after stimulus-onset. (B), Mean RDM across subjects at the moment of maximal Euclidean distance. Each cell of the matrix reflects the dissimilarity (red = high, blue = low) between two individual images, whose category is indexed on the x- and y-axis. (C), Dissimilarity matrices based on difference in contrast statistics between individual images. Color values indicate the summed difference between two individual images in beta and gamma (Weibull statistics), intercept and slope (Fourier statistics), skewness and kurtosis (distribution statistics). (D), Correlation between the RDM and each of the three dissimilarity matrices at each time-point. Highest correlation is found for Weibull statistics at 109 ms. Shaded areas reflect 95% confidence intervals obtained from a percentile bootstrap on the dissimilarity values.

Mentions: To demonstrate how these matrices can convey information about categorical properties of evoked responses, we selected the time-point at which maximal dissimilarities were found (Fig. 7A; 101 ms after stimulus-onset). In this subject-averaged RDM (Fig. 7B), we observe clustering by category: the matrix appears to consist of small blocks of 16×16 images that are minimally dissimilar amongst themselves (diagonal values), but that tend to differ from other categories (off-diagonal values). Moreover, differences between these blocks show that some categories are more dissimilar than others. Specifically, opaque categories (upper left quadrant) differ from one another and from transparent categories (lower left/upper right quadrant) whereas the transparent categories themselves tend to be minimally dissimilar (lower right quadrant).


Spatially pooled contrast responses predict neural and perceptual similarity of naturalistic image categories.

Groen II, Ghebreab S, Lamme VA, Scholte HS - PLoS Comput. Biol. (2012)

Results of RDM analysis.(A), Maximum and mean Euclidean distance for the subject-averaged RDM: for both measures, highest dissimilarity between images was found at 101 ms after stimulus-onset. (B), Mean RDM across subjects at the moment of maximal Euclidean distance. Each cell of the matrix reflects the dissimilarity (red = high, blue = low) between two individual images, whose category is indexed on the x- and y-axis. (C), Dissimilarity matrices based on difference in contrast statistics between individual images. Color values indicate the summed difference between two individual images in beta and gamma (Weibull statistics), intercept and slope (Fourier statistics), skewness and kurtosis (distribution statistics). (D), Correlation between the RDM and each of the three dissimilarity matrices at each time-point. Highest correlation is found for Weibull statistics at 109 ms. Shaded areas reflect 95% confidence intervals obtained from a percentile bootstrap on the dissimilarity values.
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC3475684&req=5

pcbi-1002726-g007: Results of RDM analysis.(A), Maximum and mean Euclidean distance for the subject-averaged RDM: for both measures, highest dissimilarity between images was found at 101 ms after stimulus-onset. (B), Mean RDM across subjects at the moment of maximal Euclidean distance. Each cell of the matrix reflects the dissimilarity (red = high, blue = low) between two individual images, whose category is indexed on the x- and y-axis. (C), Dissimilarity matrices based on difference in contrast statistics between individual images. Color values indicate the summed difference between two individual images in beta and gamma (Weibull statistics), intercept and slope (Fourier statistics), skewness and kurtosis (distribution statistics). (D), Correlation between the RDM and each of the three dissimilarity matrices at each time-point. Highest correlation is found for Weibull statistics at 109 ms. Shaded areas reflect 95% confidence intervals obtained from a percentile bootstrap on the dissimilarity values.
Mentions: To demonstrate how these matrices can convey information about categorical properties of evoked responses, we selected the time-point at which maximal dissimilarities were found (Fig. 7A; 101 ms after stimulus-onset). In this subject-averaged RDM (Fig. 7B), we observe clustering by category: the matrix appears to consist of small blocks of 16×16 images that are minimally dissimilar amongst themselves (diagonal values), but that tend to differ from other categories (off-diagonal values). Moreover, differences between these blocks show that some categories are more dissimilar than others. Specifically, opaque categories (upper left quadrant) differ from one another and from transparent categories (lower left/upper right quadrant) whereas the transparent categories themselves tend to be minimally dissimilar (lower right quadrant).

Bottom Line: We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis).Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions.These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

View Article: PubMed Central - PubMed

Affiliation: Cognitive Neuroscience Group, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
The visual world is complex and continuously changing. Yet, our brain transforms patterns of light falling on our retina into a coherent percept within a few hundred milliseconds. Possibly, low-level neural responses already carry substantial information to facilitate rapid characterization of the visual input. Here, we computationally estimated low-level contrast responses to computer-generated naturalistic images, and tested whether spatial pooling of these responses could predict image similarity at the neural and behavioral level. Using EEG, we show that statistics derived from pooled responses explain a large amount of variance between single-image evoked potentials (ERPs) in individual subjects. Dissimilarity analysis on multi-electrode ERPs demonstrated that large differences between images in pooled response statistics are predictive of more dissimilar patterns of evoked activity, whereas images with little difference in statistics give rise to highly similar evoked activity patterns. In a separate behavioral experiment, images with large differences in statistics were judged as different categories, whereas images with little differences were confused. These findings suggest that statistics derived from low-level contrast responses can be extracted in early visual processing and can be relevant for rapid judgment of visual similarity. We compared our results with two other, well- known contrast statistics: Fourier power spectra and higher-order properties of contrast distributions (skewness and kurtosis). Interestingly, whereas these statistics allow for accurate image categorization, they do not predict ERP response patterns or behavioral categorization confusions. These converging computational, neural and behavioral results suggest that statistics of pooled contrast responses contain information that corresponds with perceived visual similarity in a rapid, low-level categorization task.

Show MeSH
Related in: MedlinePlus