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State‐Trace Analysis: Dissociable Processes in a Connectionist Network?
Author(s) -
Yeates Fayme,
Wills Andy J.,
Jones Fergal W.,
McLaren Ian P. L.
Publication year - 2015
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1111/cogs.12185
Subject(s) - connectionism , trace (psycholinguistics) , computer science , dissociation (chemistry) , function (biology) , artificial intelligence , artificial neural network , linguistics , philosophy , chemistry , evolutionary biology , biology
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.