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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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AIC (Akaike information criterion) and unique explained variance analyses at channel Oz.(A), Mean explained variance across single subjects for Weibull (red), Fourier (blue) and skewness/kurtosis (green), respectively; shaded areas indicate S.E.M. (B), Mean AIC-value across single subjects computed from the residuals of each of the three regression models, as well as an additional model (black) consisting of Fourier and skewness/kurtosis values combined, showing that Weibull parameters provide the best fit to the data (low AIC-value); shaded areas indicate S.E.M. (C), Single subject AIC-values for the models displayed in B at the time-point of maximal explained variance for Weibull and Fourier statistics (113 ms); subjects are sorted based on independently determined SNR ratio (reported in Fig. S2). (D), Unique explained variance by each set of contrast statistics. (E), Absolute, non-parametric correlations (Spearman's ρ) with ERP amplitude for the individual image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), distribution Skewness (Sk) and Kurtosis (Ku). Absolute values are plotted for convenience; shaded areas indicate S.E.M. (F), Unique explained variance by each individual parameter. Results for A–E based on single-trial rather than single-image data were highly similar (Fig. S5).
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pcbi-1002726-g006: AIC (Akaike information criterion) and unique explained variance analyses at channel Oz.(A), Mean explained variance across single subjects for Weibull (red), Fourier (blue) and skewness/kurtosis (green), respectively; shaded areas indicate S.E.M. (B), Mean AIC-value across single subjects computed from the residuals of each of the three regression models, as well as an additional model (black) consisting of Fourier and skewness/kurtosis values combined, showing that Weibull parameters provide the best fit to the data (low AIC-value); shaded areas indicate S.E.M. (C), Single subject AIC-values for the models displayed in B at the time-point of maximal explained variance for Weibull and Fourier statistics (113 ms); subjects are sorted based on independently determined SNR ratio (reported in Fig. S2). (D), Unique explained variance by each set of contrast statistics. (E), Absolute, non-parametric correlations (Spearman's ρ) with ERP amplitude for the individual image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), distribution Skewness (Sk) and Kurtosis (Ku). Absolute values are plotted for convenience; shaded areas indicate S.E.M. (F), Unique explained variance by each individual parameter. Results for A–E based on single-trial rather than single-image data were highly similar (Fig. S5).

Mentions: In order to compare differences in explained variance for Weibull statistics compared to the other statistics (Fig. 6A), we used Akaike's information criterion (AIC) to evaluate the relative ‘goodness of fit’ of each of the three sets of contrast statistics. AIC is computed from the residuals of regression analyses (see Materials and Methods) and can be used for model selection given a set of candidate models of the same data, where the preferred model has minimum AIC-value. If we compare the mean AIC-value across individual subjects of Weibull, Fourier and skewness/kurtosis parameters over time, we find that the model fits start to diverge around 100 ms, with Weibull statistics leading to the lowest values (Fig. 6B). It thus appears that Weibull parameters provide a better fit to the data than the other two sets of statistics. This could be related to the fact that the Weibull parameters characterize the histogram of contrast responses at a selected spatial scale, and may thus contain information reflected in both Fourier power spectra and higher-order properties of the contrast distribution. Therefore, we also computed AIC-values for intercept, slope, skewness and kurtosis combined into one regressor (Fig. 6B, black line); the obtained values from this regression analysis are however still higher than those obtained from the Weibull parameters (significant differences between 117–140 ms, all t(19)<−2.8, all p<0.01). At the time-point of (mean) maximal explained variance (113 ms), the ordering of the different models in terms of AIC-values is consistent over subjects (Fig. 6C): in all subjects, Weibull parameters lead to the best model fit, although differences are minimal for low SNR subjects. Interestingly, for subjects with high SNR, the distance between AIC-values for the Weibull model compared to the other contrast statistics appears to increase. These findings suggest that Weibull statistics capture additional variance relative to the other contrast statistics parameters considered here.


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)

AIC (Akaike information criterion) and unique explained variance analyses at channel Oz.(A), Mean explained variance across single subjects for Weibull (red), Fourier (blue) and skewness/kurtosis (green), respectively; shaded areas indicate S.E.M. (B), Mean AIC-value across single subjects computed from the residuals of each of the three regression models, as well as an additional model (black) consisting of Fourier and skewness/kurtosis values combined, showing that Weibull parameters provide the best fit to the data (low AIC-value); shaded areas indicate S.E.M. (C), Single subject AIC-values for the models displayed in B at the time-point of maximal explained variance for Weibull and Fourier statistics (113 ms); subjects are sorted based on independently determined SNR ratio (reported in Fig. S2). (D), Unique explained variance by each set of contrast statistics. (E), Absolute, non-parametric correlations (Spearman's ρ) with ERP amplitude for the individual image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), distribution Skewness (Sk) and Kurtosis (Ku). Absolute values are plotted for convenience; shaded areas indicate S.E.M. (F), Unique explained variance by each individual parameter. Results for A–E based on single-trial rather than single-image data were highly similar (Fig. S5).
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pcbi-1002726-g006: AIC (Akaike information criterion) and unique explained variance analyses at channel Oz.(A), Mean explained variance across single subjects for Weibull (red), Fourier (blue) and skewness/kurtosis (green), respectively; shaded areas indicate S.E.M. (B), Mean AIC-value across single subjects computed from the residuals of each of the three regression models, as well as an additional model (black) consisting of Fourier and skewness/kurtosis values combined, showing that Weibull parameters provide the best fit to the data (low AIC-value); shaded areas indicate S.E.M. (C), Single subject AIC-values for the models displayed in B at the time-point of maximal explained variance for Weibull and Fourier statistics (113 ms); subjects are sorted based on independently determined SNR ratio (reported in Fig. S2). (D), Unique explained variance by each set of contrast statistics. (E), Absolute, non-parametric correlations (Spearman's ρ) with ERP amplitude for the individual image parameters: Beta (B), Gamma (G), Fourier Intercept (Ic), Fourier Slope (S), distribution Skewness (Sk) and Kurtosis (Ku). Absolute values are plotted for convenience; shaded areas indicate S.E.M. (F), Unique explained variance by each individual parameter. Results for A–E based on single-trial rather than single-image data were highly similar (Fig. S5).
Mentions: In order to compare differences in explained variance for Weibull statistics compared to the other statistics (Fig. 6A), we used Akaike's information criterion (AIC) to evaluate the relative ‘goodness of fit’ of each of the three sets of contrast statistics. AIC is computed from the residuals of regression analyses (see Materials and Methods) and can be used for model selection given a set of candidate models of the same data, where the preferred model has minimum AIC-value. If we compare the mean AIC-value across individual subjects of Weibull, Fourier and skewness/kurtosis parameters over time, we find that the model fits start to diverge around 100 ms, with Weibull statistics leading to the lowest values (Fig. 6B). It thus appears that Weibull parameters provide a better fit to the data than the other two sets of statistics. This could be related to the fact that the Weibull parameters characterize the histogram of contrast responses at a selected spatial scale, and may thus contain information reflected in both Fourier power spectra and higher-order properties of the contrast distribution. Therefore, we also computed AIC-values for intercept, slope, skewness and kurtosis combined into one regressor (Fig. 6B, black line); the obtained values from this regression analysis are however still higher than those obtained from the Weibull parameters (significant differences between 117–140 ms, all t(19)<−2.8, all p<0.01). At the time-point of (mean) maximal explained variance (113 ms), the ordering of the different models in terms of AIC-values is consistent over subjects (Fig. 6C): in all subjects, Weibull parameters lead to the best model fit, although differences are minimal for low SNR subjects. Interestingly, for subjects with high SNR, the distance between AIC-values for the Weibull model compared to the other contrast statistics appears to increase. These findings suggest that Weibull statistics capture additional variance relative to the other contrast statistics parameters considered here.

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