David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Behavioral and Brain Sciences 23 (4):443-467 (2000)
Over the last decade, fully distributed models have become dominant in connectionist psychological modelling, whereas the virtues of localist models have been underestimated. This target article illustrates some of the benefits of localist modelling. Localist models are characterized by the presence of localist representations rather than the absence of distributed representations. A generalized localist model is proposed that exhibits many of the properties of fully distributed models. It can be applied to a number of problems that are difficult for fully distributed models, and its applicability can be extended through comparisons with a number of classic mathematical models of behaviour. There are reasons why localist models have been underused, though these often misconstrue the localist position. In particular, many conclusions about connectionist representation, based on neuroscientific observation, can be called into question. There are still some problems inherent in the application of fully distributed systems and some inadequacies in proposed solutions to these problems. In the domain of psychological modelling, localist modelling is to be preferred. Key Words: choice; competition; connectionist modelling; consolidation; distributed; localist; neural networks; reaction-time.
|Keywords||choice competition connectionist modelling consolidation distributed localist neural networks reaction-time|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
James L. McClelland (2010). Emergence in Cognitive Science. Topics in Cognitive Science 2 (4):751-770.
Saskia van Dantzig, Antonino Raffone & Bernhard Hommel (2011). Acquiring Contextualized Concepts: A Connectionist Approach. Cognitive Science 35 (6):1162-1189.
Matthew M. Walsh & Kevin A. Gluck (2015). Mechanisms for Robust Cognition. Cognitive Science 39 (6):1131-1171.
Nicola J. Pitchford, Walter Jb van Heuven, Andrew N. Kelly, Taoli Zhang & Timothy Ledgeway (2012). Vision, Development, and Bilingualism Are Fundamental in the Quest for a Universal Model of Visual Word Recognition and Reading. Behavioral and Brain Sciences 1 (1):38-39.
Similar books and articles
Gail A. Carpenter (2000). Combining Distributed and Localist Computations in Real-Time Neural Networks. Behavioral and Brain Sciences 23 (4):473-474.
Stephen Grossberg (2000). Localist but Distributed Representations. Behavioral and Brain Sciences 23 (4):478-479.
C. Philip Beaman (2000). Neurons Amongst the Symbols? Behavioral and Brain Sciences 23 (4):468-470.
Jeffrey S. Bowers (2000). Further Arguments in Support of Localist Coding in Connectionist Networks. Behavioral and Brain Sciences 23 (4):471-471.
Simon Farrell & Stephan Lewandowsky (2000). The Case Against Distributed Representations: Lack of Evidence. Behavioral and Brain Sciences 23 (4):476-477.
Norman D. Cook (2000). Localist Representations and Theoretical Clarity. Behavioral and Brain Sciences 23 (4):474-475.
Robert M. French & Elizabeth Thomas (2000). Why Localist Connectionist Models Are Inadequate for Categorization. Behavioral and Brain Sciences 23 (4):477-477.
Colin Martindale (2000). Localist Representations Are a Desirable Emergent Property of Neurologically Plausible Neural Networks. Behavioral and Brain Sciences 23 (4):485-486.
Craig Leth-Steensen (2000). Localist Network Modelling in Psychology: Ho-Hum or Hm-M-M? Behavioral and Brain Sciences 23 (4):484-485.
Mike Page (2000). Sticking to the Manifesto. Behavioral and Brain Sciences 23 (4):496-505.
Added to index2009-01-28
Total downloads17 ( #147,710 of 1,700,283 )
Recent downloads (6 months)4 ( #161,079 of 1,700,283 )
How can I increase my downloads?