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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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Regression analysis of EEG data: single subject results.Explained variance of ERP amplitude at channel Oz over time, for each individual subject (colored thin lines) and mean across subjects (black thick line), using as regressors either (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and slope and (C), skewness and kurtosis; single-trial results of these analyses can be found in Fig. S4. Insets display scalp plots of r2 values for all electrodes at the time of maximal explained variance averaged over subjects (113 ms for Weibull/Fourier, 254 ms for skewness/kurtosis. (D), Grand average ERP amplitude (averaged over all subjects and all images) for an early and a late time-point of peak explained variance displayed in A–C.
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pcbi-1002726-g005: Regression analysis of EEG data: single subject results.Explained variance of ERP amplitude at channel Oz over time, for each individual subject (colored thin lines) and mean across subjects (black thick line), using as regressors either (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and slope and (C), skewness and kurtosis; single-trial results of these analyses can be found in Fig. S4. Insets display scalp plots of r2 values for all electrodes at the time of maximal explained variance averaged over subjects (113 ms for Weibull/Fourier, 254 ms for skewness/kurtosis. (D), Grand average ERP amplitude (averaged over all subjects and all images) for an early and a late time-point of peak explained variance displayed in A–C.

Mentions: Regression of single-image ERP amplitude (per subject, electrode and time-point) on contrast statistics showed that Weibull statistics explain a substantial amount of variance between individual images. Highest values were found at occipital channel Oz, where explained variance for all subjects reached a maximum between 100 and 210 ms after stimulus onset; maximal values ranged between r2 = 0.12–0.80 (Fig. 5A) and were highly significant (all p<0.0001, FDR-corrected). For Fourier parameters (Fig. 5B), somewhat lower values were found (max r2 between 0.08–0.59, 100–210 ms; all p<0.0001). For skewness and kurtosis (Fig. 5C), explained variance was much lower and did not reach a consistent maximum during a specific time frame (max r2 between 0.02–0.23 at 78–421 ms; maximal values were significant for 11 out of 19 subjects).


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)

Regression analysis of EEG data: single subject results.Explained variance of ERP amplitude at channel Oz over time, for each individual subject (colored thin lines) and mean across subjects (black thick line), using as regressors either (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and slope and (C), skewness and kurtosis; single-trial results of these analyses can be found in Fig. S4. Insets display scalp plots of r2 values for all electrodes at the time of maximal explained variance averaged over subjects (113 ms for Weibull/Fourier, 254 ms for skewness/kurtosis. (D), Grand average ERP amplitude (averaged over all subjects and all images) for an early and a late time-point of peak explained variance displayed in A–C.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002726-g005: Regression analysis of EEG data: single subject results.Explained variance of ERP amplitude at channel Oz over time, for each individual subject (colored thin lines) and mean across subjects (black thick line), using as regressors either (A), Weibull parameters beta and gamma, (B), Fourier parameters intercept and slope and (C), skewness and kurtosis; single-trial results of these analyses can be found in Fig. S4. Insets display scalp plots of r2 values for all electrodes at the time of maximal explained variance averaged over subjects (113 ms for Weibull/Fourier, 254 ms for skewness/kurtosis. (D), Grand average ERP amplitude (averaged over all subjects and all images) for an early and a late time-point of peak explained variance displayed in A–C.
Mentions: Regression of single-image ERP amplitude (per subject, electrode and time-point) on contrast statistics showed that Weibull statistics explain a substantial amount of variance between individual images. Highest values were found at occipital channel Oz, where explained variance for all subjects reached a maximum between 100 and 210 ms after stimulus onset; maximal values ranged between r2 = 0.12–0.80 (Fig. 5A) and were highly significant (all p<0.0001, FDR-corrected). For Fourier parameters (Fig. 5B), somewhat lower values were found (max r2 between 0.08–0.59, 100–210 ms; all p<0.0001). For skewness and kurtosis (Fig. 5C), explained variance was much lower and did not reach a consistent maximum during a specific time frame (max r2 between 0.02–0.23 at 78–421 ms; maximal values were significant for 11 out of 19 subjects).

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