alexa Distinctive features, categorical perception, and probability learning: Some applications of a neural model.
Engineering

Engineering

Journal of Information Technology & Software Engineering

Author(s): Anderson JA

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Reviews a previously proposed model for memory based on neurophysiological considerations. It is assumed that (a) nervous system activity is usefully represented as the set of simultaneous individual neuron activities in a group of neurons; (b) different memory traces make use of the same synapses; and (c) synapses associate two patterns of neural activity by incrementing synaptic connectivity proportionally to the product of pre- and postsynaptic activity, forming a matrix of synaptic connectivities. This model is extended by (a) introducing positive feedback of a set of neurons onto itself and (b) allowing the individual neurons to saturate. A hybrid model, partly analog and partly binary, arises. The system has certain characteristics reminiscent of analysis by distinctive features. The model is applied to "categorical perception," and probability learning is discussed. The model can predict overshooting, recency data, and probabilities occurring in systems with more than two events with reasonably good accuracy. (12 p ref) (PsycINFO Database Record (c) 2016 APA, all rights reserved)

This article was published in Psychological and referenced in Journal of Information Technology & Software Engineering

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