We sketch four applications of Marr's levels-of-analysis methodology to the relations between logic and experimental data in the cognitive neuroscience of language and reasoning. The first part of the paper illustrates the explanatory power of computational level theories based on logic. We show that a Bayesian treatment of the suppression task in reasoning with conditionals is ruled out by EEG data, supporting instead an analysis based on defeasible logic. Further, we describe how results from an EEG study on temporal prepositions (...) can be reanalyzed using formal semantics, addressing a potential confound. The second part of the article demonstrates the predictive power of logical theories drawing on EEG data on processing progressive constructions and on behavioral data on conditional reasoning in people with autism. Logical theories can constrain processing hypotheses all the way down to neurophysiology, and conversely neuroscience data can guide the selection of alternative computational level models of cognition. (shrink)
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. (shrink)
Compositionality remains effective as an explanation of cases in which processing complexity increases due to syntactic factors only. It falls short of accounting for situations in which complexity arises from interactions with the sentence or discourse context, perceptual cues, and stored knowledge. The idea of compositionality as a methodological principle is appealing, but imputing the complexity to one component of the grammar or another, instead of enriching the notion of composition, is not always an innocuous move, leading to fully equivalent (...) theories. Compositionality sets an upper bound on the degree of informational encapsulation that can be posited by modular or component-based theories of language: simple composition ties in with a strongly modular take on meaning assembly, which is seen as sealed off from information streams other than the lexicon and the syntax. (shrink)
The suitability of the artificial grammar learning paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences and recorded the EEG before and after exposure (...) to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere-exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test. (shrink)
Many authors have recently highlighted the importance of prediction for language comprehension. Pickering & Garrod (P&G) are the first to propose a central role for prediction in language production. This is an intriguing idea, but it is not clear what it means for speakers to predict their own utterances, and how prediction during production can be empirically distinguished from production proper.
' a welcome guide for researchers to the merging fields of neuroscience, linguistics and psycholinguistics.' BRAIN'... an important and captivating book, one that has been long awaited by all researchers interested in language and the brain.' Trends in Cognitive Sciences, 1999. The Neurocognition of Language brings together experts on human language and the brain to present the first critical overview of the cognitive neuroscience of language, one of the fastest-moving and most exciting areas today. In-depth discussion of the representations and (...) structures of language, as well as of the cognitive architectures which underlie speaking, listening, and reading, will provide a basis for future brain imaging research. In addition, the existing brain imaging literature on word and sentence processing is critically reviewed, as well as contributions from brain lesion data. Finally, the book discusses the prospects and problems of brain imaging techniques for the study of language, presents some of the most recent and promising analytic procedures for relating brain imaging data to the higher cognitive functions, and contains a review of the neuroanatomical structure of Broca's language area. Uniquely interdisciplinary, this book will provide researchers and students in cognitive neuroscience with state-of-the-art reviews of the major language functions, while being of equal interest to researchers in linguistics and language who want to learn about the neural bases of language. It will be an essential purchase for anyone requiring an overview of our current understanding of the relation between language and the brain. (shrink)