The eliminative connectionist is concerned to provide an account of cognition that eschews symbols, and operates at the subsymbolic level. For instance, the concept of “dog” could be captured in a distributed representation as a number of input features (e.g. four-footed, furry, barks etc) and would then exist in the net in the form of the weighted links between its neuron like units.
Published in Chapter:
Artificial Neural Networks and Cognitive Modelling
Amanda J.C. Sharkey (University of Sheffield, UK)
Copyright: © 2009
|Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch025
Abstract
In their heyday, artificial neural networks promised a radically new approach to cognitive modelling. The connectionist approach spawned a number of influential, and controversial, cognitive models. In this article, we consider the main characteristics of the approach, look at the factors leading to its enthusiastic adoption, and discuss the extent to which it differs from earlier computational models. Connectionist cognitive models have made a significant impact on the study of mind. However connectionism is no longer in its prime. Possible reasons for the diminution in its popularity will be identified, together with an attempt to identify its likely future. The rise of connectionist models dates from the publication in 1986 by Rumelhart and McClelland, of an edited work containing a collection of connectionist models of cognition, each trained by exposure to samples of the required tasks. These volumes set the agenda for connectionist cognitive modellers and offered a methodology that subsequently became the standard. Connectionist cognitive models have since been produced in domains including memory retrieval and category formation, and (in language) phoneme recognition, word recognition, speech perception, acquired dyslexia, language acquisition, and (in vision) edge detection, object and shape recognition. More than twenty years later the impact of this work is still apparent.