Fig. 5.

Illustration of the interaction of cue and outcome competition in dog-rabbit example 1. In this example, the weights learned with full error-driven learning (b) show that species-specific features (e.g., tail-wagging) are more relevant for species discrimination than shared features (i.e., size). When outcome competition is turned off during learning (c), the model does not discover that size is a feature dimension shared between the two species and cue competition leads to the same weights from all features (as in Fig. 3b). When cue competition is turned off during learning (d), weights correspond to the conditional probabilities of the label, here, “dog”, given a feature (small has a lower weight because in some cases it also precedes the label “rabbit”)