Academic journal article Psychonomic Bulletin & Review

Is State-Trace Analysis an Appropriate Tool for Assessing the Number of Cognitive Systems?

Academic journal article Psychonomic Bulletin & Review

Is State-Trace Analysis an Appropriate Tool for Assessing the Number of Cognitive Systems?

Article excerpt

Published online: 14 January 2014

© Psychonomic Society, Inc. 2014

Abstract There is now much evidence that humans have multiple memory systems, and evidence is also building that other cognitive processes are mediated by multiple systems. Even so, several recent articles have questioned the existence of multiple cognitive systems, and a number of these have based their arguments on results from state-trace analysis. State-trace analysis was not developed for this purpose but, rather, to identify data sets that are consistent with variation in a single parameter. All previous applications have assumed that state-trace plots in which the data fall on separate curves rule out any model in which only a single parameter varies across the two tasks under study. Unfortunately, this assumption is incorrect. Models in which only one parameter varies can generate any type of state-trace plot, as can models in which two or more parameters vary. In addition, it is straight-forward to show that both single-system and multiple-systems models can generate state-trace plots that are considered in the literature to be consistent with either one or multiple cognitive systems. Thus, without additional information, there is no empirical state-trace plot that supports any inferences about the number of underlying parameters or systems.

Keywords Category learning · Memory systems · Model evaluation · State trace analysis

Introduction

The theory that humans have multiple memory systems be- came widely accepted within the field of cognitive neurosci- ence during the 1980s and 1990s (Eichenbaum & Cohen, 2001; Schacter, Wagner, & Buckner, 2000; Squire, 2004). Researchers in many other fields are now also debating wheth- er multiple systems might mediate what previously was thought to be a unitary cognitive process. Included in this list are category learning (Ashby, Alfonso-Reese, Turken, & Waldron, 1998; Erickson & Kruschke, 1998), recognition memory (e.g., Yonelinas, 2002), and logical reasoning (Sloman, 1996). Although the evidence favoring multiple systems continues to grow in each of these areas, several recent articles have questioned the existence of multiple cog- nitive systems (e.g., Newell, Dunn, & Kalish, 2011;Nosofsky, Stanton, & Zaki, 2005; Stanton & Nosofsky, 2007). In a number of these, arguments have been based on results from state-trace analysis (Dunn, 2008; Dunn, Newell, & Kalish, 2012; Newell & Dunn, 2008; Newell, Dunn, & Kalish, 2010).

State-trace analysis (Bamber, 1979; Dunn & Kirsner, 1988) is a method for determining the number of cognitive processes or systems that are used to generate data from two separate tasks or experimental conditions. The idea is to plot perfor- mance on the two tasks against one another and examine the resulting scatterplot. On the basis of the type of scatterplot that emerges, inferences are then made about the number of un- derlying processes or systems. State-trace analysis has been proposed as a more powerful alternative to dissociation logic. For example, Newell and Dunn (2008) went so far as to argue that state-trace analysis "overcomes all of the flaws of disso- ciation logic" (p. 285).

Suppose the same participants complete two tasks, T1 and T2.LetP(T1)andP(T2) denote their performance on tasks T1 and T2, respectively. A state-trace analysis begins by plotting values of P(T1)andP(T2) against each other. In this article, I will assume that values of P(T2) are plotted on the ordinate and values of P(T1) are on the abscissa. Current applications of state-trace analysis distinguish among four different types of plots. In a type 1 plot, the data all fall on a single strictly monotonic curve-that is, a curve that is either strictly increasing or strictly decreasing (e.g., as in Fig. 1a below). In a type 2 plot, the data all fall on a single nonmonotonic curve in which P(T2) is a function of P(T1)-that is, in which each value of P(T1) co-occurs with only a single value of P(T2) (e. …

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