The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
The ERP response to the amount of information conveyed by words in sentences
Open accessAbstract
Reading times on words in a sentence depend on the amount of information the words convey, which can be estimated by probabilistic language models. We investigate whether event-related potentials (ERPs), too, are predicted by information measures. Three types of language models estimated four different information measures on each word of a sample of English sentences. Six different ERP deflections were extracted from the EEG signal of participants reading the same sentences. A comparison between the information measures and ERPs revealed a reliable correlation between N400 amplitude and word surprisal. Language models that make no use of syntactic structure fitted the data better than did a phrase-structure grammar, which did not account for unique variance in N400 amplitude. These findings suggest that different information measures quantify cognitively different processes and that readers do not make use of a sentence's hierarchical structure for generating expectations about the upcoming word.
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Random effects structure for confirmatory hypothesis testing: Keep it maximal
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.001 · Cited by 10,328
Towards a neural basis of auditory sentence processing
Friederici, A. D. (2002) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(00)01839-8 · Cited by 2,231
Prediction during natural language comprehension
Willems, R. M., Frank, S. L., Nijhof, A. D., Hagoort, P., & van den Bosch, A. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv075 · Cited by 288
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What do we mean by prediction in language comprehension?
Kuperberg, G. R., & Jaeger, T. F. (2016) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2015.1102299 · Cited by 1,087
Predicting while comprehending language: A theory and review.
Pickering, M. J., & Gambi, C. (2018) · Psychological Bulletin · doi:10.1037/bul0000158 · Cited by 518
The neural architecture of language: Integrative modeling converges on predictive processing
Schrimpf, M., Blank, I. A., Tuckute, G., Kauf, C., Hosseini, E. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105646118 · Cited by 517
Shared computational principles for language processing in humans and deep language models
Goldstein, A., Zada, Z., Buchnik, E., Schain, M., Price, A., Aubrey, B., Nastase, S. A., Feder, A., Emanuel, D., Cohen, A., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01026-4 · Cited by 447
A hierarchy of linguistic predictions during natural language comprehension
Heilbron, M., Armeni, K., Schoffelen, J.-M., Hagoort, P., & de Lange, F. P. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201968119 · Cited by 343
Metadata
- Authors
- Frank, S. L., Otten, L. J., Galli, G., & Vigliocco, G.
- Venue
- Brain and Language
- Publisher
- Elsevier BV
- Published
- 2015-01
- Volume
- 140
- Pages
- 1-11
- ISSN
- 0093-934X (ISSN-L)
- OpenAlex
- W2119728020
- Cited by
- 381
- References
- 3
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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