TY - JOUR
AU - MARLEY, A. A. J.
AB -  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-à-vis other approaches are discussed 
TI - A Connectionist Model of Choice and Reaction Time in Absolute Identification
JF - Connection Science
DO - 10.1080/09540099108946595
DA - 1991-01-01
UR - https://www.deepdyve.com/lp/taylor-francis/a-connectionist-model-of-choice-and-reaction-time-in-absolute-WyhbPhSha0
SP - 401
EP - 433
VL - 3
IS - 4
DP - DeepDyve
ER -