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| Research article summary (published 29 Jun 2003): |
Stochastic cascade processes as a model of multi-stage concurrent information processing.
Full Abstract
McClelland's (Psychol. Rev. 86 (1979) 287) influential cascade model of reaction time (RT) is often seen as an important explicitly formulated alternative to strictly serial models because some of its basic notions fit in with known characteristics of neural activation processes. A disadvantage is that it is an essentially deterministic model to which noise is added by across-trials parameter variability that is unrelated to the concept of cascaded activation propagation per se. We propose and analyze a general stochastic cascade model based on neural counting processes; within this framework we present a new process-oriented interpretation of McClelland's cascade activation equation. We then theoretically explain and numerically explore conditions under which the stochastic cascade model predicts additive vs. interactive mean RT effects, and compare them to known properties of McClelland's model. Some of these properties remain valid even within a stochastic neural counting formulation, while others reflect assumptions which are essentially unrelated to the concept of cascaded activation.
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Author information
Author/s: Schwarz, Wolf (W);
Affiliation: University of Nijmegen, NICI, Postbus 9104, 6500 HE, Nijmegen, The Netherlands. schwarz@nici.kun.nl
Journal and publication information
Publication Type: Journal Article
Journal: Acta psychologica (Acta Psychol (Amst)), published in Netherlands. (Language: eng)
Reference: 2003-Jul; vol 113 (issue 3) : pp 231-61
Dates: Created 2003/07/01; Completed 2003/09/09; Revised 2004/11/17;
PMID: 12834998, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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