ABSTRACT
Attention allocation predictions for the models were derived as follows. ALCOVE and RASHNL have explicit attention weight parameters, which are reported. This chapter aims to define implicit measures of a dimension’s attentional salience, by summing the absolute values of weights from all input nodes associated with a given dimension to the hidden node layer in the hippocampal net component of the Cortico-Hippocampai Model (CHM). ALCOVE, RASHNL, and the CHM gave as much or more attention weight to Dimension 4 as to the more diagnostic dimensions. ALCOVE, RASHNL, and SUSTAIN all learn to allocate more attention to Dimensions 3 and 4, that together define the classification in terms of a simple XOR relationship. In contrast, the CHM pays more attention to the individually, but merely probabilistically, diagnostic Dimensions 1 and 2.
