EPAM-like models of recognition and learning

Abstract

A description is provided of EPAM-III, a theory in the form of a computer program for simulating human verbal learning, along with a summary of the empirical evidence for its validity. Criticisms leveled against the theory in a recent paper by Barsalou and Bower are shown to derive largely from their misconception that EPAM-III employed a binary, rather than n-ary branching discrimination net. It is shown that Barsalou and Bower also failed to understand how the recursive structure of EPAM-III eliminates the need to duplicate test nodes that are used to recognize subobjects, and how the possibility of redundant recognition paths controls the sensitivity of EPAM to noticing order. EPAM is also compared briefly with other theories of human discrimination and discrimination learning, including PANDEMONIUM-like systems and dataflow nets.