Implicit Acquisition of Grammars With Crossed and Nested Non-Adjacent Dependencies: Investigating the Push-Down Stack Model
Version of Record online: 27 MAR 2012
Copyright © 2012 Cognitive Science Society, Inc.
Volume 36, Issue 6, pages 1078–1101, August 2012
How to Cite
Uddén, J., Ingvar, M., Hagoort, P. and Petersson, K. M. (2012), Implicit Acquisition of Grammars With Crossed and Nested Non-Adjacent Dependencies: Investigating the Push-Down Stack Model. Cognitive Science, 36: 1078–1101. doi: 10.1111/j.1551-6709.2012.01235.x
- Issue online: 27 JUL 2012
- Version of Record online: 27 MAR 2012
- Received 17 August 2010; received in revised form 22 August 2011; accepted 24 August 2011
- Artificial grammar learning;
- Non-adjacent dependencies;
- Implicit learning
A recent hypothesis in empirical brain research on language is that the fundamental difference between animal and human communication systems is captured by the distinction between finite-state and more complex phrase-structure grammars, such as context-free and context-sensitive grammars. However, the relevance of this distinction for the study of language as a neurobiological system has been questioned and it has been suggested that a more relevant and partly analogous distinction is that between non-adjacent and adjacent dependencies. Online memory resources are central to the processing of non-adjacent dependencies as information has to be maintained across intervening material. One proposal is that an external memory device in the form of a limited push-down stack is used to process non-adjacent dependencies. We tested this hypothesis in an artificial grammar learning paradigm where subjects acquired non-adjacent dependencies implicitly. Generally, we found no qualitative differences between the acquisition of non-adjacent dependencies and adjacent dependencies. This suggests that although the acquisition of non-adjacent dependencies requires more exposure to the acquisition material, it utilizes the same mechanisms used for acquiring adjacent dependencies. We challenge the push-down stack model further by testing its processing predictions for nested and crossed multiple non-adjacent dependencies. The push-down stack model is partly supported by the results, and we suggest that stack-like properties are some among many natural properties characterizing the underlying neurophysiological mechanisms that implement the online memory resources used in language and structured sequence processing.