Publications

Displaying 1 - 6 of 6
  • Brouwer, H., Fitz, H., & Hoeks, J. (2012). Getting real about semantic illusions: Rethinking the functional role of the P600 in language comprehension. Brain Research, 1446, 127-143. doi:10.1016/j.brainres.2012.01.055.

    Abstract

    In traditional theories of language comprehension, syntactic and semantic processing are inextricably linked. This assumption has been challenged by the ‘Semantic Illusion Effect’ found in studies using Event Related brain Potentials. Semantically anomalous sentences did not produce the expected increase in N400 amplitude but rather one in P600 amplitude. To explain these findings, complex models have been devised in which an independent semantic processing stream can arrive at a sentence interpretation that may differ from the interpretation prescribed by the syntactic structure of the sentence. We review five such multi-stream models and argue that they do not account for the full range of relevant results because they assume that the amplitude of the N400 indexes some form of semantic integration. Based on recent evidence we argue that N400 amplitude might reflect the retrieval of lexical information from memory. On this view, the absence of an N400-effect in Semantic Illusion sentences can be explained in terms of priming. Furthermore, we suggest that semantic integration, which has previously been linked to the N400 component, might be reflected in the P600 instead. When combined, these functional interpretations result in a single-stream account of language processing that can explain all of the Semantic Illusion data.
  • Chang, F., Janciauskas, M., & Fitz, H. (2012). Language adaptation and learning: Getting explicit about implicit learning. Language and Linguistics Compass, 6, 259-278. doi:10.1002/lnc3.337.

    Abstract

    Linguistic adaptation is a phenomenon where language representations change in response to linguistic input. Adaptation can occur on multiple linguistic levels such as phonology (tuning of phonotactic constraints), words (repetition priming), and syntax (structural priming). The persistent nature of these adaptations suggests that they may be a form of implicit learning and connectionist models have been developed which instantiate this hypothesis. Research on implicit learning, however, has also produced evidence that explicit chunk knowledge is involved in the performance of these tasks. In this review, we examine how these interacting implicit and explicit processes may change our understanding of language learning and processing.
  • Fitz, H., Chang, F., & Christansen, M. H. (2011). A connectionist account of the acquisition and processing of relative clauses. In E. Kidd (Ed.), The acquisition of relative clauses. Processing, typology and function (pp. 39-60). Amsterdam: Benjamins.

    Abstract

    Relative clause processing depends on the grammatical role of the head noun in the subordinate clause. This has traditionally been explained in terms of cognitive limitations. We suggest that structure-related processing differences arise from differences in experience with these structures. We present a connectionist model which learns to produce utterances with relative clauses from exposure to message-sentence pairs. The model shows how various factors such as frequent subsequences, structural variations, and meaning conspire to create differences in the processing of these structures. The predictions of this learning-based account have been confirmed in behavioral studies with adults. This work shows that structural regularities that govern relative clause processing can be explained within a usage-based approach to recursion.
  • Fitz, H. (2011). A liquid-state model of variability effects in learning nonadjacent dependencies. In L. Carlson, C. Hölscher, & T. Shipley (Eds.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society (pp. 897-902). Austin, TX: Cognitive Science Society.

    Abstract

    Language acquisition involves learning nonadjacent dependencies that can obtain between words in a sentence. Several artificial grammar learning studies have shown that the ability of adults and children to detect dependencies between A and B in frames AXB is influenced by the amount of variation in the X element. This paper presents a model of statistical learning which displays similar behavior on this task and generalizes in a human-like way. The model was also used to predict human behavior for increased distance and more variation in dependencies. We compare our model-based approach with the standard invariance account of the variability effect.
  • 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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