Publications

Abstract

The study develops a neurocomputational architecture for grammatical processing in language production and language comprehension (grammatical encoding and decoding, respectively). It seeks to answer two questions. First, how is online syntactic structure formation of the complexity required by natural-language grammars possible in a fixed, preexisting neural network without the need for online creation of new connections or associations? Second, is it realistic to assume that the seemingly disparate instantiations of syntactic structure formation in grammatical encoding and grammatical decoding can run on the same neural infrastructure? This issue is prompted by accumulating experimental evidence for the hypothesis that the mechanisms for grammatical decoding overlap with those for grammatical encoding to a considerable extent, thus inviting the hypothesis of a single “grammatical coder.” The paper answers both questions by providing the blueprint for a syntactic structure formation mechanism that is entirely based on prewired circuitry (except for referential processing, which relies on the rapid learning capacity of the hippocampal complex), and can subserve decoding as well as encoding tasks. The model builds on the “Unification Space” model of syntactic parsing developed by Vosse & Kempen (2000, 2008, 2009). The design includes a neurocomputational mechanism for the treatment of an important class of grammatical movement phenomena.

Abstract

Behavioral syntactic priming effects during sentence comprehension are typically observed only if both the syntactic structure and lexical head are repeated. In contrast, during production syntactic priming occurs with structure repetition alone, but the effect is boosted by repetition of the lexical head. We used fMRI to investigate the neuronal correlates of syntactic priming and lexical boost effects during sentence production and comprehension. The critical measure was the magnitude of fMRI adaptation to repetition of sentences in active or passive voice, with or without verb repetition. In conditions with repeated verbs, we observed adaptation to structure repetition in the left IFG and MTG, for active and passive voice. However, in the absence of repeated verbs, adaptation occurred only for passive sentences. None of the fMRI adaptation effects yielded differential effects for production versus comprehension, suggesting that sentence comprehension and production are subserved by the same neuronal infrastructure for syntactic processing.

Abstract

A new cognitive architecture is proposed for the syntactic aspects of human sentence processing. The architecture, called Unification Space, is biologically inspired but not based on neural nets. Instead it relies on biosynthesis as a basic metaphor. We use simulated annealing as an optimization technique which searches for the best configuration of isolated syntactic segments or subtrees in the final parse tree. The gradually decaying activation of individual syntactic nodes determines the ‘global excitation level’ of the system. This parameter serves the function of ‘computational temperature’ in simulated annealing. We have built a computer implementation of the architecture which simulates well-known sentence understanding phenomena. We report successful simulations of the psycholinguistic effects of clause embedding, minimal attachment, right association and lexical ambiguity. In addition, we simulated impaired sentence understanding as observable in agrammatic patients. Since the Unification Space allows for contextual (semantic and pragmatic) influences on the syntactic tree formation process, it belongs to the class of interactive sentence processing models.