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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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Behavioral results and comparison with classification.(A), Accuracy of behavioral categorization (open circles: single subjects, filled circle: mean) and of classification based on Weibull parameters, Fourier parameters or skewness and kurtosis. (B), Behavioral confusion matrix, displaying mean categorization accuracy for specific comparisons of categories. For each pair of categories the percentage of correct answers is displayed as a grayscale value. (C), Comparison of mean behavioral confusion matrix with classification results based on the three sets of contrast statistics. (D), Inter-matrix correlations of the classification errors for each set of statistics with the mean behavioral confusion matrix (left, mean) as well as those of individual participants (right, single subjects). For the mean correlation, error bars indicate 95% confidence intervals obtained using a percentile bootstrap on values within the mean confusion matrix.
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pcbi-1002726-g008: Behavioral results and comparison with classification.(A), Accuracy of behavioral categorization (open circles: single subjects, filled circle: mean) and of classification based on Weibull parameters, Fourier parameters or skewness and kurtosis. (B), Behavioral confusion matrix, displaying mean categorization accuracy for specific comparisons of categories. For each pair of categories the percentage of correct answers is displayed as a grayscale value. (C), Comparison of mean behavioral confusion matrix with classification results based on the three sets of contrast statistics. (D), Inter-matrix correlations of the classification errors for each set of statistics with the mean behavioral confusion matrix (left, mean) as well as those of individual participants (right, single subjects). For the mean correlation, error bars indicate 95% confidence intervals obtained using a percentile bootstrap on values within the mean confusion matrix.

Mentions: Participants indicated for each possible combination of the 16 dead leaves categories whether these were the same or different category. Behavioral accuracy was high across all subjects (mean 93% correct, range 0.88–0.98), suggesting that subjects were well able to categorize these stimuli (Fig. 8A). To generate specific predictions about categorical similarity based on contrast statistics, we conducted classification analyses using the distance between images in each of the three similarity spaces, testing how often proximity in parameter values resulted in classification of an image to another category than its own (see Materials and Methods and Fig. 3D). Mean classification accuracy based on distance in contrast statistics was high for all three sets of contrast statistics, with highest accuracy for the Fourier parameters (99%), subsequently for the Weibull parameters (94%) and finally for skewness/kurtosis (93%). Despite these high accuracies, errors were made in both behavior and classification: to test whether these errors occurred for specific combinations of categories, we summarized the average number of errors for each specific combination of categories in confusion matrices.


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)

Behavioral results and comparison with classification.(A), Accuracy of behavioral categorization (open circles: single subjects, filled circle: mean) and of classification based on Weibull parameters, Fourier parameters or skewness and kurtosis. (B), Behavioral confusion matrix, displaying mean categorization accuracy for specific comparisons of categories. For each pair of categories the percentage of correct answers is displayed as a grayscale value. (C), Comparison of mean behavioral confusion matrix with classification results based on the three sets of contrast statistics. (D), Inter-matrix correlations of the classification errors for each set of statistics with the mean behavioral confusion matrix (left, mean) as well as those of individual participants (right, single subjects). For the mean correlation, error bars indicate 95% confidence intervals obtained using a percentile bootstrap on values within the mean confusion matrix.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002726-g008: Behavioral results and comparison with classification.(A), Accuracy of behavioral categorization (open circles: single subjects, filled circle: mean) and of classification based on Weibull parameters, Fourier parameters or skewness and kurtosis. (B), Behavioral confusion matrix, displaying mean categorization accuracy for specific comparisons of categories. For each pair of categories the percentage of correct answers is displayed as a grayscale value. (C), Comparison of mean behavioral confusion matrix with classification results based on the three sets of contrast statistics. (D), Inter-matrix correlations of the classification errors for each set of statistics with the mean behavioral confusion matrix (left, mean) as well as those of individual participants (right, single subjects). For the mean correlation, error bars indicate 95% confidence intervals obtained using a percentile bootstrap on values within the mean confusion matrix.
Mentions: Participants indicated for each possible combination of the 16 dead leaves categories whether these were the same or different category. Behavioral accuracy was high across all subjects (mean 93% correct, range 0.88–0.98), suggesting that subjects were well able to categorize these stimuli (Fig. 8A). To generate specific predictions about categorical similarity based on contrast statistics, we conducted classification analyses using the distance between images in each of the three similarity spaces, testing how often proximity in parameter values resulted in classification of an image to another category than its own (see Materials and Methods and Fig. 3D). Mean classification accuracy based on distance in contrast statistics was high for all three sets of contrast statistics, with highest accuracy for the Fourier parameters (99%), subsequently for the Weibull parameters (94%) and finally for skewness/kurtosis (93%). Despite these high accuracies, errors were made in both behavior and classification: to test whether these errors occurred for specific combinations of categories, we summarized the average number of errors for each specific combination of categories in confusion matrices.

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