Opposition logic and neural network models in artificial grammar learning

Abstract

Following neural network simulations of the two experiments of Higham, Vokey, and Pritchard (2000), Tunney and Shanks (2003) argued that the opposition logic advocated by Higham et al. (2000) was incapable of distinguishing between single and multiple influences on performance of artificial grammar learning and more generally. We show that their simulations do not support their conclusions. We also provide different neural network simulations that do simulate the essential results of Higham et al. (2000).

Introduction

Tunney and Shanks (2003) responded to our experiments in which we used opposition logic to separate controlled from automatic influences in artificial grammar learning (Higham, Vokey, & Pritchard, 2000). They used a simple neural network simulation that they claimed produced similar results without the need of separate processes or influences. There is a somewhat dismissive tone in Tunney and Shanks (2003) that seems to imply that a major contention that we made has been disproven. We are not sure what claim we have made that has been disproven by their simulation, but we do think that the value of the experiments reported by Higham et al. (2000) is being underestimated in their discussion.

Tunney and Shanks (2003) argued for a single memory system, which we also have consistently argued for over at least the last 10 years (see, e.g., the “episodes and abstractions” section of Higham et al., 2000). This claim is not a matter of contention between us, and, indeed, is rather a growing consensus in the literature (e.g., Banks, 2000; Palmeri & Flanery, 2002; Ratcliff, Van Zandt, & McKoon, 1995). What appears to be at contention is whether the opposition procedure provides evidence that is usefully interpreted as a distinction between controlled and automatic influences. Manipulations intended to vary the degree of participants’ control, such as deadlines (e.g., Higham et al., 2000; Toth, 1996), divided attention (e.g., Debner & Jacoby, 1994; Jacoby, 1991), or delayed testing (e.g., Higham et al., 2000; Jacoby, Kelley, Brown, & Jasechko, 1989a), produce separable effects when investigated with the opposition procedure. Whether this separation of effects is called a difference in influences or processes or changes of parameters within a single system, the opposition procedure has produced clear and stable patterns of results in research on memory, word perception, and artificial grammar learning. Over a wide range of experimental variations, the effects of these variables are computationally separable into controlled and automatic components, both of which may be manipulated without influencing the other: deadlines and divided attention selectively affect the controlled component (e.g., Toth, 1996; Yonelinas & Jacoby, 1994), whereas variations in such things as habit strength and proactive interference do so for the automatic component (Hay & Jacoby, 1996; Jacoby, Debner, & Hay, 2001). Furthermore, this pattern of influence is clear and simple using opposition logic, as opposed to its traditional alternatives, which was the central contrast of our paper.

In what follows, we briefly review the critical results of Higham et al. (2000), and Tunney and Shanks’ neural network simulation of them. We demonstrate that their model failed to reproduce the central pattern of dissociations from Higham et al. (2000). We present a different neural network simulation that provides a better fit with the results, and, finally, discuss the implications of such models for opposition logic.