State-trace analysis: dissociable processes in a connectionist network?
McLaren, Ian P.L.
Wiley for Cognitive Science Society
Copyright © 2014 Cognitive Science Society, Inc
Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state-trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, performance measured by either two dependent variables, or two conditions of the same dependent measure. The resulting analysis is considered to provide evidence that either (a) a single process underlies performance (one function is produced) or (b) there is evidence for more than one process (more than one function is produced). This article reports simulations using the simple recurrent network (SRN; Elman, 1990) in which changes to the learning rate produced state-trace plots with multiple functions. We also report simulations using a single-layer error-correcting network that generate plots with a single function. We argue that the presence of different functions on a state-trace plot does not necessarily support a dual-system account, at least as typically defined (e.g. two separate autonomous systems competing to control responding); it can also indicate variation in a single parameter within theories generally considered to be single-system accounts.
This is the peer reviewed version of the following article: Yeates, F., Wills, A. J., Jones, F. W. and McLaren, I. P. L. (2015), State-Trace Analysis: Dissociable Processes in a Connectionist Network?. Cognitive Science, 39: 1047–1061, which has been published in final form at 10.1111/cogs.12185. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving: http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms
Vol. 39 (5), pp. 1047 - 1061
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