A Connectionist Model of Choice and Reaction Time in Absolute Identification

Abstract

A connectionist architecture is developed that can be used for modeling choice probabilities and reaction times in identification tasks. The architecture consists of a feedforward network and a decoding module, and learning is by mean-variance back-propagation, an extension of the standard back-propagation learning algorithm. We suggest that the new learning procedure leads to a better model of human learning in simple identification tasks than does standard back-propagation. Choice probabilities are modeled by the input-output relations of the network and reaction times are modeled by the time taken for the network, particularly the decoding module, to achieve a stable state. In this paper, the model is applied to the identification of unidimensional stimuli; applications to the identification of multidimensional stimuli—visual displays and words—is mentioned and presented in more detail in other papers. The strengths and weaknesses of this connectionist approach vis-a-vis other approaches are discussed