Publications

Abstract

In Dutch and German, the canonical order of subject, object(s) and finite verb is ‘verb-second’ (V2) in main but ‘verb-final’ (VF) in subordinate clauses. This occasionally leads to the production of noncanonical word orders. Familiar examples are causative and argumentative clauses introduced by a subordinating conjunction (Du. omdat, Ger. weil ‘because’): the omdat/weil-V2 phenomenon. Such clauses may also be introduced by coordinating conjunctions (Du. want, Ger. denn), which license V2 exclusively. However, want/denn-VF structures are unknown. We present the results of a corpus study on the incidence of omdat-V2 in spoken Dutch, and compare them to published data on weil-V2 in spoken German. Basic findings: omdat-V2 is much less frequent than weil-V2 (ratio almost 1:8); and the frequency relations between coordinating and subordinating conjunctions are opposite (want >> omdat; denn << weil). We propose that conjunction selection and V2/VF selection proceed partly independently, and sometimes miscommunicate—e.g. yielding omdat/weil paired with V2. Want/denn-VF pairs do not occur because want/denn clauses are planned as autonomous sentences, which take V2 by default. We sketch a simple feedforward neural network with two layers of nodes (representing conjunctions and word orders, respectively) that can simulate the observed data pattern through inhibition-based competition of the alternative choices within the node layers.

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.