**A**, The left panel shows the structural accuracy of reconstructions using several different methods (subject TN). In each case structural reconstruction accuracy (y-axis) is quantified using a similarity metric that ranges from 0.0 to 1.0. From left to right the bars give the structural similarity between the target image and reconstruction (mean **±** s.e.m, image reconstruction data set) for the structural model with a flat prior; the structural model with a sparse Gabor prior; the structural model with a natural image prior; and the *hybrid method* consisting of the structural model, the semantic model, and the natural image prior. The red line indicates chance performance. Reconstructions produced using the sparse Gabor or natural image prior are significantly more accurate than chance (p < 0.01, *t*-test; for this subject only, the reconstructions produced using a flat prior are also significant at this level). Reconstruction with the structural model and the natural image prior is significantly more accurate than reconstruction with a sparse Gabor prior (p < 0.01, *t*-test). These results indicate that prior information is important for obtaining structurally accurate image reconstructions. The structural accuracy of the structural model with natural image prior and the hybrid method are not significantly different (p > 0.3, *t*-test), so structural accuracy is not affected by the addition of the semantic model. The right panel shows semantic accuracy of reconstructions obtained using the structural model with natural image prior (blue) and the hybrid method (black). In each case semantic reconstruction accuracy (y-axis) is quantified in terms of the probability that a reconstruction will belong to the same semantic category as the target image (errorbars indicate bootstrapped estimate of s.d.). The number of semantic categories in the classification tree varies from 2 broadly defined categories to the 23 specific categories shown in (x-axis). The red curve indicates chance performance. The semantic accuracy of the reconstructions obtained using the structural model and natural image prior are rarely significantly greater than chance (p > 0.3, binomial test). However, the semantic accuracy of the hybrid method is significantly greater than chance regardless of the number of semantic categories (p < 10^{-5}, binomial test). **B**, Data for subject KK, format same as in A. Prior information is important for obtaining structurally accurate image reconstructions (p-values of structural accuracy comparisons same as in A). The semantic accuracy of the hybrid method is significantly greater than chance (p < .002, binomial test). **C**, Data for subject SN, format same as in A. Prior information is important for obtaining structurally accurate image reconstructions (p-values of structural accuracy comparisons same as in A). The semantic accuracy of the hybrid method is significantly greater than chance (p < 10^{-5}, binomial test).

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