Publications

Displaying 1 - 4 of 4
  • Fitz, H., Uhlmann, M., Van den Broek, D., Duarte, R., Hagoort, P., & Petersson, K. M. (2020). Neuronal spike-rate adaptation supports working memory in language processing. Proceedings of the National Academy of Sciences of the United States of America, 117(34), 20881-20889. doi:10.1073/pnas.2000222117.

    Abstract

    Language processing involves the ability to store and integrate pieces of
    information in working memory over short periods of time. According to
    the dominant view, information is maintained through sustained, elevated
    neural activity. Other work has argued that short-term synaptic facilitation
    can serve as a substrate of memory. Here, we propose an account where
    memory is supported by intrinsic plasticity that downregulates neuronal
    firing rates. Single neuron responses are dependent on experience and we
    show through simulations that these adaptive changes in excitability pro-
    vide memory on timescales ranging from milliseconds to seconds. On this
    account, spiking activity writes information into coupled dynamic variables
    that control adaptation and move at slower timescales than the membrane
    potential. From these variables, information is continuously read back into
    the active membrane state for processing. This neuronal memory mech-
    anism does not rely on persistent activity, excitatory feedback, or synap-
    tic plasticity for storage. Instead, information is maintained in adaptive
    conductances that reduce firing rates and can be accessed directly with-
    out cued retrieval. Memory span is systematically related to both the time
    constant of adaptation and baseline levels of neuronal excitability. Inter-
    ference effects within memory arise when adaptation is long-lasting. We
    demonstrate that this mechanism is sensitive to context and serial order
    which makes it suitable for temporal integration in sequence processing
    within the language domain. We also show that it enables the binding of
    linguistic features over time within dynamic memory registers. This work
    provides a step towards a computational neurobiology of language.
  • Chang, F., Bauman, M., Pappert, S., & Fitz, H. (2015). Do lemmas speak German?: A verb position effect in German structural priming. Cognitive Science, 39(5), 1113-1130. doi:10.1111/cogs.12184.

    Abstract

    Lexicalized theories of syntax often assume that verb-structure regularities are mediated by lemmas, which abstract over variation in verb tense and aspect. German syntax seems to challenge this assumption, because verb position depends on tense and aspect. To examine how German speakers link these elements, a structural priming study was performed which varied syntactic structure, verb position (encoded by tense and aspect), and verb overlap. Abstract structural priming was found, both within and across verb position, but priming was larger when the verb position was the same between prime and target. Priming was boosted by verb overlap, but there was no interaction with verb position. The results can be explained by a lemma model where tense and aspect are linked to structural choices in German. Since the architecture of this lemma model is not consistent with results from English, a connectionist model was developed which could explain the cross-linguistic variation in the production system. Together, these findings support the view that language learning plays an important role in determining the nature of structural priming in different languages
  • Brouwer, H., Fitz, H., & Hoeks, J. C. (2010). Modeling the noun phrase versus sentence coordination ambiguity in Dutch: Evidence from Surprisal Theory. In Proceedings of the 2010 Workshop on Cognitive Modeling and Computational Linguistics, ACL 2010 (pp. 72-80). Association for Computational Linguistics.

    Abstract

    This paper investigates whether surprisal theory can account for differential processing difficulty in the NP-/S-coordination ambiguity in Dutch. Surprisal is estimated using a Probabilistic Context-Free Grammar (PCFG), which is induced from an automatically annotated corpus. We find that our lexicalized surprisal model can account for the reading time data from a classic experiment on this ambiguity by Frazier (1987). We argue that syntactic and lexical probabilities, as specified in a PCFG, are sufficient to account for what is commonly referred to as an NP-coordination preference.
  • Fitz, H. (2010). Statistical learning of complex questions. In S. Ohlsson, & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 2692-2698). Austin, TX: Cognitive Science Society.

    Abstract

    The problem of auxiliary fronting in complex polar questions occupies a prominent position within the nature versus nurture controversy in language acquisition. We employ a model of statistical learning which uses sequential and semantic information to produce utterances from a bag of words. This linear learner is capable of generating grammatical questions without exposure to these structures in its training environment. We also demonstrate that the model performs superior to n-gram learners on this task. Implications for nativist theories of language acquisition are discussed.

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