Neural Responses to Structural Incongruencies in Language and Statistical Learning Point to Similar Underlying Mechanisms

Neural Responses to Structural Incongruencies in Language and Statistical Learning Point to Similar Underlying Mechanisms Morten H. Christiansen (mhc27@cornell.edu) Department of Psychology, Cornell University, Ithaca, NY 14853 USA Christopher M. Conway (cmconway@indiana.edu) Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405 USA Luca Onnis (lo35@cornell.edu) Department of Psychology, Cornell University, Ithaca, NY 14853 USA Abstract Christiansen, 2006; Gomez & Gerken, 2000). Statistical learning involves the extraction of regularities and patterns distributed across a set of exemplars in time and/or space, typically without direct awareness of what has been learned. Though many researchers assume that statistical learning is important for language acquisition and processing (e.g., Gomez & Gerken, 2000), there is very little direct neural evidence supporting such a claim. There is some evidence from event-related potential (ERP) studies showing that structural incongruencies in non-language sequential stimuli elicit similar brain responses as those observed for syntactic violations in natural language: a positive shift in the brainwaves observed about 600 msec after the incongruency known as the P600 effect (Friederici, Steinhauer, & Pfeifer, 2002; Lelekov, Dominey, & Garcia-Larrea, 2000; Patel, Gibson, Ratner, Besson, & Holcomb, 1998). Although encouraging, the similarities are inferred across different subject populations and across different experimental paradigms. Thus, no firm conclusions can be made because there is no study that provides a direct within-subject comparison of the ERP responses to both natural language and statistical learning of sequential patterns. In this paper, we investigate the possibility that structural incongruencies in both natural language and other sequential stimuli will elicit the same electrophysiological response profile, a P600. We provide a within-subject comparison of the neural responses to both types of violations, allowing us to directly assess the hypothesis that statistical learning of sequential information is an important cognitive mechanism underlying language processing. Such a demonstration is important for both theoretical and practical reasons. Statistical learning has become a popular method for investigating natural language acquisition and processing, especially in infant populations (e.g., Gomez & Gerken, 2000). Thus, providing direct neural evidence linking statistical learning to natural language processing is necessary for validating the statistical learning approach to language. Moreover, our study is also of theoretical importance as it addresses issues relating to the modularity of language. Before describing our ERP study, we first We used event-related potentials (ERPs) to investigate the distribution of brain activity while adults performed (a) a natural language reading task and (b) a statistical learning task involving sequenced stimuli. The same positive ERP deflection, the P600 effect, typically linked to difficult or ungrammatical syntactic processing, was found for structural incongruencies in both natural language as well as statistical learning and had similar topographical distributions. These results suggest that general learning abilities related to the processing of complex, sequenced material may be implicated in language processing. We conclude that the same neural mechanisms are recruited for both syntactic processing of language stimuli and statistical learning of sequential patterns more generally. Keywords: Event-Related Potentials; Statistical Learning; Language Processing; P600 Introduction One of the central questions in cognitive science concerns the extent to which higher-order cognitive processes in humans are either subserved by separate, domain-specific brain mechanisms or whether the same neural substrate may support several cognitive functions in a domain-general fashion. The issue of modularity has played a particularly important role in the study of language, which has traditionally been regarded as being strongly modular (e.g., Friederici, 1995; Pinker, 1991). Given such modular characterization, the cognitive and neural machinery employed in acquiring and processing language is considered to be uniquely dedicated to language itself. Thus, on this account, little or no overlap in neural substrates would be expected between language and other higher-order cognitive processes. Here, we explore the alternative hypothesis that the neural underpinnings of language may be part of a broader family of neural mechanisms that the brain recruits when processing sequential information in general. One such type of learning process—employed to encode complex sequential patterns and also implicated in language processing—is implicit statistical learning 1 (Conway & “Implicit learning” and “statistical learning” have traditionally been studied separately; however, we consider these two terms to be touching on the same underlying learning mechanism, which we hereafter refer to simply as statistical learning.