13 research outputs found

    Prolegomena to a neurocomputational architecture for human grammatical encoding and decoding

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    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

    A compositional neural architecture for language

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    Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception–action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de) compositional meaning. The model's architecture—a multidimensional coordinate system based on neurophysiological models of sensory processing—proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation

    Models linking production and comprehension

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    To what extent are linguistic representations shared between production and comprehension? What is the nature of the links between production and comprehension processes? In this chapter, we provide an introduction to those models that incorporate explicit assumptions about the degree of sharing and review some supporting evidence. We show that full sharing of representations is uncontroversial only for semantics and syntax, while there is mixed evidence over the degree of overlapping at the level of phonological and phonetic representations. We then propose a taxonomy of production‐comprehension links, ranging from long‐term to fast‐acting. To conclude, we advocate more explicit theorizing about the relationship between language production and language comprehension

    Action-based grammar

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    Verbal behavior without syntactic structures: beyond Skinner and Chomsky

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    What does it mean to know language? Since the Chomskian revolution, one popular answer to this question has been: to possess a generative grammar that exclusively licenses certain syntactic structures. Decades later, not even an approximation to such a grammar, for any language, has been formulated; the idea that grammar is universal and innately specified has proved barren; and attempts to show how it could be learned from experience invariably come up short. To move on from this impasse, we must rediscover the extent to which language is like any other human behavior: dynamic, social, multimodal, patterned, and purposive, its purpose being to promote desirable actions (or thoughts) in others and self. Recent psychological, computational, neurobiological, and evolutionary insights into the shaping and structure of behavior may then point us toward a new, viable account of language.Comment: Ms completed on February 4, 201

    Predicting and imagining language

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    To what extent is predicting language akin to imagining language? Recently, researchers have argued that covert simulation of the production system underlies both articulation imagery and predicting what somebody is about to say. Moreover, experimental evidence implicates potentially similar production-related mechanisms in prediction during language comprehension and in mental imagery tasks. We discuss evidence in favour of this proposal and argue that imagining others’ utterances can also implicate covert simulation. Finally, we briefly review evidence that speakers in joint language tasks cannot help but mentally represent (i.e., imagine) whether others are engaging in language production, and that they do so using mechanisms that are also implicated in preparing to speak

    Incrementality and flexibility in sentence production

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    Integrative (Synchronisations-)Mechanismen der (Neuro-)Kognition vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisationsparadigmas

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    Der Gegenstand der vorliegenden Arbeit besteht darin, aufbauend auf dem (Haupt-)Thema, der Darlegung und Untersuchung der Lösung des Bindungsproblems anhand von temporalen integrativen (Synchronisations-)Mechanismen im Rahmen der kognitiven (Neuro-)Architekturen im (Neo-)Konnektionismus mit Bezug auf die Wahrnehmungs- und Sprachkognition, vor allem mit Bezug auf die dabei auftretende KompositionalitĂ€ts- und SystematizitĂ€tsproblematik, die Konstruktion einer noch zu entwickelnden integrativen Theorie der (Neuro-)Kognition zu skizzie-ren, auf der Basis des ReprĂ€sentationsformats einer sog. „vektoriellen Form“, u.z. vor dem Hintergrund des (Neo-)Konnektionismus, der Theorie der nichtlinearen dynamischen Systeme, der Informationstheorie und des Selbstorganisations-Paradigmas
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