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Fig. 10.11. Effect of training patterns on face preferences. Results are shown for nine matched face detection simulations (bypassing V1), each with a different set of input patterns. The top row displays examples of these patterns, drawn on the retina. The second row shows the LGN response to the retinal input, which forms the input to the FSA in these simulations. The third row plots a sample FSA receptive field after self-organization, visualized by subtracting the OFF weights from the ON; other neurons learned similar RFs. In each case the HLISSOM network learns FSA RFs similar to the LGN representation. These RFs are not patchy, because they no longer represent the patchy V1 activities. The two numerical rows quantify the face selectivity of each network. The row labeled "Sc" specifies the selectivity for facelike schematics (from Figure 10.6b-d) relative to non-facelike schematics (from Figure 10.6e-i). The row labeled "Im" lists the selectivity for the six face images from Figure 10.13 relative to the six comparable object images in the same figure. The different training patterns gave rise to different selectivities. Pattern (g) leads to equal responses for both facelike and non-facelike schematics (selectivity of 0.5), and (h) and (i) have a greater overall response to the non-facelike schematics (selectivity lower than 0.5). Thus, not all training patterns can explain preferences for schematic faces even if they match some parts of the face. Similarly, the single-dot pattern (g) has a selectivity below 0.5 for real faces, indicating a stronger response for the objects than for real faces. The other training patterns all have the same size as real faces, or match at least two parts of the face, and thus have selectivities larger than 0.5 for real faces. Overall, the shape of the training pattern is clearly important for face selectivity, both for schematics and real faces, but it need not be controlled very tightly to result in face-selective responses.