Unifying syntactic theory and sentence processing difficulty through a connectionist minimalist parser

Prolegomena to a neurocomputational architecture for human grammatical encoding and decoding

This 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 and Kempen (Cognition 75:105-143, 2000; Cognitive Neurodynamics 3:331-346, 2009a). The design includes a neurocomputational mechanism for the treatment of an important class of grammatical movement phenomena.

A modular latching chain

Many cognitive tasks involve transitions between distinct mental processes, which may range from discrete states to complex strategies. The ability of cortical networks to combine discrete jumps with continuous glides along ever changing trajectories, dubbed latching dynamics, may be essential for the emergence of the unique cognitive capacities of modern humans. Novel trajectories have to be followed in the multidimensional space of cortical activity for novel behaviours to be produced; yet, not everything changes: several lines of evidence point at recurring patterns in the sequence of activation of cortical areas in a variety of behaviours. To extend a mathematical model of latching dynamics beyond the simple unstructured auto-associative Potts network previously analysed, we introduce delayed structured connectivity and hetero-associative connection weights, and we explore their effects on the dynamics. A modular model in the small-world regime is considered, with modules arranged on a ring. The synaptic weights include a standard auto-associative component, stabilizing distinct patterns of activity, and a hetero-associative component, favoring transitions from one pattern, expressed in one module, to the next, in the next module. We then study, through simulations, how structural parameters, like those regulating rewiring probability, noise and feedback connections, determine sequential association dynamics.

Computationelle Neurolinguistik

Computational neurolinguistics integrates methods from computational (psycho-)linguistics and computational neuroscience in order to model neural correlates of linguistic behavior. We illustrate these techniques using an example of the language processing of German negative polarity items (NPI) in the event-related brain potential (ERP) paradigm. To that aim, we first describe the syntactic and semantic licensing conditions of NPIs by means of slightly modified minimalist grammars. In a second step we use dynamic cognitive modeling (DCM) to map the state descriptions of a minimalist parser onto activation patterns of a neural network. Thirdly, the network’s synaptic weights are trained with the correct parse of NPI constructions. Using these weights we calculate neural harmony measures for correct and for ungrammatical NPI constructions. In a final step we correlate the harmonies of the dynamical model with experimentally obtained ERP amplitudes by means of a simple statistical model.

A dynamical systems perspective on the relationship between symbolic and non-symbolic computation

It has been claimed that connectionist (artificial neural network) models of language processing, which do not appear to employ "rules", are doing something different in kind from classical symbol processing models, which treat "rules" as atoms (e.g., McClelland and Patterson in Trends Cogn Sci 6(11):465-472, 2002). This claim is hard to assess in the absence of careful, formal comparisons between the two approaches. This paper formally investigates the symbol-processing properties of simple dynamical systems called affine dynamical automata, which are close relatives of several recurrent connectionist models of language processing (e.g., Elman in Cogn Sci 14:179-211, 1990). In line with related work (Moore in Theor Comput Sci 201:99-136, 1998; Siegelmann in Neural networks and analog computation: beyond the Turing limit. Birkhäuser, Boston, 1999), the analysis shows that affine dynamical automata exhibit a range of symbol processing behaviors, some of which can be mirrored by various Turing machine devices, and others of which cannot be. On the assumption that the Turing machine framework is a good way to formalize the "computation" part of our understanding of classical symbol processing, this finding supports the view that there is a fundamental "incompatibility" between connectionist and classical models (see Fodor and Pylyshyn 1988; Smolensky in Behav Brain Sci 11(1):1-74, 1988; beim Graben in Mind Matter 2(2):29--51,2004b). Given the empirical successes of connectionist models, the more general, super-Turing framework is a preferable vantage point from which to consider cognitive phenomena. This vantage may give us insight into ill-formed as well as well-formed language behavior and shed light on important structural properties of learning processes.