Deep networks generate good proxy for top-down representation.

A-C plot human sensitivity (y axis) for individual probe insertions (one small dot per insertion) separately for different scenes (pooled across participants), against values corresponding to probe insertion point on top-down/bottom-up maps (A/B) and the map generated by the CRF-RNN deep convolutional network [23] (C; abscissa values for this plot have been rescaled to range between 0 and 1). Dashed lines show 80%, 90%, and 95% (from thick to thin) confidence intervals for linear fit. Green symbols in A show average y value for individual abscissa values; symbol size scales with number of data points. D shows correlation values for scatter plots in A-C and those generated by other computer vision algorithms (Itti-Koch [3], GBVS [41], gPb-HS [5], nCuts [42], HVC [43]); open green symbol plots correlation for top-down map when consensus probe locations (indicated by solid green symbol in A) are excluded. Error bars in D show 95% confidence intervals. E plots rich/poor log-ratios to the conventions of Fig 2F where human sensitivity estimation for y axis is relabelled against rich/poor probe locations on the maps generated by CRF-RNN (red) and gPb-HS (blue) algorithms instead of top-down map (black). Values on the x axis are computed with respect to bottom-up map (same as Fig 2F). Icons show example segmentations from the two algorithms for the natural scene in Fig 1A; coloured overlay indicates segmented regions/boundaries, orange circle corresponds to red solid circle (top-down poor, bottom-up rich location) in Fig 1D. Data for this figure is available from S2 Data. HVC, hierarchical visual cues; GBVS, graph-based visual saliency.