Publications

Abstract

This study investigated two questions. One is: To what degree is sentence processing beyond single words independent of the input modality (speech vs. reading)? The second question is: Which parts of the network recruited by both modalities is sensitive to syntactic complexity? These questions were investigated by having more than 200 participants read or listen to well-formed sentences or series of unconnected words. A largely left-hemisphere frontotemporoparietal network was found to be supramodal in nature, i.e., independent of input modality. In addition, the left inferior frontal gyrus (LIFG) and the left posterior middle temporal gyrus (LpMTG) were most clearly associated with left-branching complexity. The left anterior temporal lobe (LaTL) showed the greatest sensitivity to sentences that differed in right-branching complexity. Moreover, activity in LIFG and LpMTG increased from sentence onset to end, in parallel with an increase of the left-branching complexity. While LIFG, bilateral anterior temporal lobe, posterior MTG, and left inferior parietal lobe (LIPL) all contribute to the supramodal unification processes, the results suggest that these regions differ in their respective contributions to syntactic complexity related processing. The consequences of these findings for neurobiological models of language processing are discussed.

Abstract

The syntactic structure of main and subordinate clauses is determined to a considerable extent by verb biases. For example, some English and Dutch ditransitive verbs have a preference for the prepositional object dative, whereas others are typically used with the double object dative. In this study, we compare the effect of these biases on structure selection in (S)VO and (S)OV dative clauses in the Corpus of Spoken Dutch (CGN). This comparison allowed us to make inferences about the size of the advance planning scope during spontaneous speaking: If the verb is an obligatory component of clause-level advance planning scope, as is claimed by the hypothesis of hierarchical incrementality, then biases should exert their influence on structure choices, regardless of early (VO) or late (OV) position of the verb in the clause. Conversely, if planning proceeds in a piecemeal fashion, strictly guided by lexical availability, as claimed by linear incrementality, then the verb and its associated biases can only influence structure choices in VO sentences. We tested these predictions by analyzing structure choices in the CGN, using mixed logit models. Our results support a combination of linear and hierarchical incrementality, showing a significant influence of verb bias on structure choices in VO, and a weaker (but still significant) effect in OV clauses

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.