Applying an exemplar model to the artificial-grammar task: Inferring grammaticality from similarity

Abstract

We present three artificial-grammar experiments. The first used position constraints, and the second used sequential constraints. The third varied both the amount of training and the degree of sequential constraint. Increasing both the amount of training and the redundancy of the grammar benefited participants' ability to infer grammatical status; nevertheless, they were unable to describe the grammar. We applied a multitrace model of memory to the task. The model used a global measure of similarity to assess the grammatical status of the probe and captured performance both in our experiments and in three classic studies from the literature. The model shows that retrieval is sensitive to structure in memory, even when individual exemplars are encoded sparsely. The work ties an understanding of performance in the artificial-grammar task to the principles used to understand performance in episodic-memory tasks.

Keywords:

• Artificial grammar
• Global memory model
• Implicit learning
• Episodic memory

The research was supported by grants from the Natural Sciences and Engineering Research Council of Canada to both the first and the second authors. We are deeply indebted to Beth Johns and Joaquin Vaquero for a critical reading and subsequent honing of the manuscript.