Spatially Pooled Contrast Responses Predict Neural and Perceptual Similarity of Naturalistic Image Categories
Figure 6
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).