Publications

Displaying 1 - 4 of 4
  • Fitz, H., & Chang, F. (2019). Language ERPs reflect learning through prediction error propagation. Cognitive Psychology, 111, 15-52. doi:10.1016/j.cogpsych.2019.03.002.

    Abstract

    Event-related potentials (ERPs) provide a window into how the brain is processing language. Here, we propose a theory that argues that ERPs such as the N400 and P600 arise as side effects of an error-based learning mechanism that explains linguistic adaptation and language learning. We instantiated this theory in a connectionist model that can simulate data from three studies on the N400 (amplitude modulation by expectancy, contextual constraint, and sentence position), five studies on the P600 (agreement, tense, word category, subcategorization and garden-path sentences), and a study on the semantic P600 in role reversal anomalies. Since ERPs are learning signals, this account explains adaptation of ERP amplitude to within-experiment frequency manipulations and the way ERP effects are shaped by word predictability in earlier sentences. Moreover, it predicts that ERPs can change over language development. The model provides an account of the sensitivity of ERPs to expectation mismatch, the relative timing of the N400 and P600, the semantic nature of the N400, the syntactic nature of the P600, and the fact that ERPs can change with experience. This approach suggests that comprehension ERPs are related to sentence production and language acquisition mechanisms
  • Zuidema, W., & Fitz, H. (2019). Key issues and future directions: Models of human language and speech processing. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 353-358). Cambridge, MA: MIT Press.
  • Bod, R., Fitz, H., & Zuidema, W. (2006). On the structural ambiguity in natural language that the neural architecture cannot deal with [Commentary]. Behavioral and Brain Sciences, 29, 71-72. doi:10.1017/S0140525X06239025.

    Abstract

    We argue that van der Velde's & de Kamps's model does not solve the binding problem but merely shifts the burden of constructing appropriate neural representations of sentence structure to unexplained preprocessing of the linguistic input. As a consequence, their model is not able to explain how various neural representations can be assigned to sentences that are structurally ambiguous.
  • Fitz, H. (2006). Church's thesis and physical computation. In A. Olszewski, J. Wolenski, & R. Janusz (Eds.), Church's Thesis after 70 years (pp. 175-219). Frankfurt a. M: Ontos Verlag.

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