Publications
Displaying 1 - 6 of 6
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Hagoort, P., & Beckmann, C. F. (2019). Key issues and future directions: The neural architecture for language. In P. Hagoort (
Ed. ), Human language: From genes and brains to behavior (pp. 527-532). Cambridge, MA: MIT Press. -
Hagoort, P. (2019). Introduction. In P. Hagoort (
Ed. ), Human language: From genes and brains to behavior (pp. 1-6). Cambridge, MA: MIT Press. -
Heilbron, M., Ehinger, B., Hagoort, P., & De Lange, F. P. (2019). Tracking naturalistic linguistic predictions with deep neural language models. In Proceedings of the 2019 Conference on Cognitive Computational Neuroscience (pp. 424-427). doi:10.32470/CCN.2019.1096-0.
Abstract
Prediction in language has traditionally been studied using
simple designs in which neural responses to expected
and unexpected words are compared in a categorical
fashion. However, these designs have been contested
as being ‘prediction encouraging’, potentially exaggerating
the importance of prediction in language understanding.
A few recent studies have begun to address
these worries by using model-based approaches to probe
the effects of linguistic predictability in naturalistic stimuli
(e.g. continuous narrative). However, these studies
so far only looked at very local forms of prediction, using
models that take no more than the prior two words into
account when computing a word’s predictability. Here,
we extend this approach using a state-of-the-art neural
language model that can take roughly 500 times longer
linguistic contexts into account. Predictability estimates
fromthe neural network offer amuch better fit to EEG data
from subjects listening to naturalistic narrative than simpler
models, and reveal strong surprise responses akin to
the P200 and N400. These results show that predictability
effects in language are not a side-effect of simple designs,
and demonstrate the practical use of recent advances
in AI for the cognitive neuroscience of language. -
Sjerps, M. J., & Chang, E. F. (2019). The cortical processing of speech sounds in the temporal lobe. In P. Hagoort (
Ed. ), Human language: From genes and brain to behavior (pp. 361-379). Cambridge, MA: MIT Press. -
Van Berkum, J. J. A., & Nieuwland, M. S. (2019). A cognitive neuroscience perspective on language comprehension in context. In P. Hagoort (
Ed. ), Human language: From genes and brain to behavior (pp. 429-442). Cambridge, MA: MIT Press. -
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.
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