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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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Stimuli set out against their respective contrast statistics.Each data-point reflects parameter values for a single image, color-coded by category. Individual images are displayed against their (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and (increasing negative) slope and (C), distribution properties skewness and kurtosis. In all cases, clustering by category based on parameter values is evident. (D), Non-parametric correlations between the six image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), Skewness (Sk) and Kurtosis (Ku).
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pcbi-1002726-g004: Stimuli set out against their respective contrast statistics.Each data-point reflects parameter values for a single image, color-coded by category. Individual images are displayed against their (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and (increasing negative) slope and (C), distribution properties skewness and kurtosis. In all cases, clustering by category based on parameter values is evident. (D), Non-parametric correlations between the six image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), Skewness (Sk) and Kurtosis (Ku).

Mentions: If we set out all 256 dead leaves images against the three sets of image statistics (Weibull parameters, Fourier parameters and skewness/kurtosis), stimuli cluster by category in all cases, with Fourier parameters leading to the most separable clusters (Fig. 4A–C). There were considerable correlations between the various parameters (Fig. 4D; individual correlations plots in Fig. S2). Skewness and kurtosis correlated highly (ρ = 0.91, p<0.0001), but other significant correlations are observed as well, for example between Fourier slope and the Weibull beta parameter (ρ = 0.57, p<0.0001) and also between the two Weibull parameters (ρ = 0.48, p<0.001). A correlation of similar magnitude was also observed [17] for natural scenes, supporting the notion that the dead leaves stimuli used here have similar low-level structure as natural stimuli.


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)

Stimuli set out against their respective contrast statistics.Each data-point reflects parameter values for a single image, color-coded by category. Individual images are displayed against their (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and (increasing negative) slope and (C), distribution properties skewness and kurtosis. In all cases, clustering by category based on parameter values is evident. (D), Non-parametric correlations between the six image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), Skewness (Sk) and Kurtosis (Ku).
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002726-g004: Stimuli set out against their respective contrast statistics.Each data-point reflects parameter values for a single image, color-coded by category. Individual images are displayed against their (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and (increasing negative) slope and (C), distribution properties skewness and kurtosis. In all cases, clustering by category based on parameter values is evident. (D), Non-parametric correlations between the six image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), Skewness (Sk) and Kurtosis (Ku).
Mentions: If we set out all 256 dead leaves images against the three sets of image statistics (Weibull parameters, Fourier parameters and skewness/kurtosis), stimuli cluster by category in all cases, with Fourier parameters leading to the most separable clusters (Fig. 4A–C). There were considerable correlations between the various parameters (Fig. 4D; individual correlations plots in Fig. S2). Skewness and kurtosis correlated highly (ρ = 0.91, p<0.0001), but other significant correlations are observed as well, for example between Fourier slope and the Weibull beta parameter (ρ = 0.57, p<0.0001) and also between the two Weibull parameters (ρ = 0.48, p<0.001). A correlation of similar magnitude was also observed [17] for natural scenes, supporting the notion that the dead leaves stimuli used here have similar low-level structure as natural stimuli.

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