The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Thinking ahead: The role and roots of prediction in language comprehension
Open accessAbstract
Reviewed are studies using event‐related potentials to examine when and how sentence context information is used during language comprehension. Results suggest that, when it can, the brain uses context to predict features of likely upcoming items. However, although prediction seems important for comprehension, it also appears susceptible to age‐related deterioration and can be associated with processing costs. The brain may address this trade‐off by employing multiple processing strategies, distributed across the two cerebral hemispheres. In particular, left hemisphere language processing seems to be oriented toward prediction and the use of top‐down cues, whereas right hemisphere comprehension is more bottom‐up, biased toward the veridical maintenance of information. Such asymmetries may arise, in turn, because language comprehension mechanisms are integrated with language production mechanisms only in the left hemisphere (the PARLO framework).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Distributed representations, simple recurrent networks, and grammatical structure
Elman, J. L. (1991) · Machine Learning · doi:10.1007/bf00114844 · Cited by 1,354
Contextual effects on word perception and eye movements during reading
Ehrlich, S. F., & Rayner, K. (1981) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(81)90220-6 · Cited by 796
Cited by
Kutas, M., & Federmeier, K. D. (2011) · Annual Review of Psychology · doi:10.1146/annurev.psych.093008.131123 · Cited by 4,377
Self-citationAn integrated theory of language production and comprehension
Pickering, M. J., & Garrod, S. (2013) · Behavioral and Brain Sciences · doi:10.1017/s0140525x12001495 · Cited by 1,569
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
Auditory processing in autism spectrum disorder: A review
O’Connor, K. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2011.11.008 · Cited by 466
Four central questions about prediction in language processing
Huettig, F. (2015) · Brain Research · doi:10.1016/j.brainres.2015.02.014 · Cited by 405
Joint Action, Interactive Alignment, and Dialog
Garrod, S., & Pickering, M. J. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01020.x · Cited by 402
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
The linguistic processes underlying the P600
Gouvea, A. C., Phillips, C., Kazanina, N., & Poeppel, D. (2010) · Language and Cognitive Processes · doi:10.1080/01690960902965951 · Cited by 344
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
Menenti (2011) · doi:10.1177/0956797611418347 · Cited by 238
The violation-of-expectation paradigm: A conceptual overview.
Margoni, F., Surian, L., & Baillargeon, R. (2024) · Psychological Review · doi:10.1037/rev0000450 · Cited by 127
Alpha and theta band dynamics related to sentential constraint and word expectancy
Rommers, J., Dickson, D. S., Norton, J. J. S., Wlotko, E. W., & Federmeier, K. D. (2017) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2016.1183799 · Cited by 109
Self-citationThe role of language processing in language acquisition
Phillips, C., & Ehrenhofer, L. (2015) · Linguistic Approaches to Bilingualism · doi:10.1075/lab.5.4.01phi · Cited by 123
Training versus engagement as paths to cognitive enrichment with aging.
Stine-Morrow, E. A. L., Payne, B. R., Roberts, B. W., Kramer, A. F., Morrow, D. G., Payne, L., Hill, P. L., Jackson, J. J., Gao, X., Noh, S. R., et al. (2014) · Psychology and Aging · doi:10.1037/a0038244 · Cited by 109
“Such findings have been explained in terms of neural models suggesting that predictive processing (i.e., production) is integrally related to comprehension mechanisms (Federmeier, 2007).”
BackgroundConversation Electrified: ERP Correlates of Speech Act Recognition in Underspecified Utterances
Gisladottir, R. S., Chwilla, D. J., & Levinson, S. C. (2015) · PLOS ONE · doi:10.1371/journal.pone.0120068 · Cited by 108
Cognitive control ability mediates prediction costs in monolinguals and bilinguals
Zirnstein, M., van Hell, J. G., & Kroll, J. F. (2018) · Cognition · doi:10.1016/j.cognition.2018.03.001 · Cited by 84
Predictive coding in auditory perception: challenges and unresolved questions
Denham, S. L., & Winkler, I. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.13802 · Cited by 76
Hu, J., Small, H., Kean, H., Takahashi, A., Zekelman, L., Kleinman, D., Ryan, E., Nieto-Castañón, A., Ferreira, V., & Fedorenko, E. (2023) · Cerebral Cortex · doi:10.1093/cercor/bhac350 · Cited by 73
A hierarchy of linguistic predictions during natural language comprehension
(2020) · doi:10.1101/2020.12.03.410399 · Cited by 63
Gisladottir, R. S., Bögels, S., & Levinson, S. C. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00034 · Cited by 63
Dividing attention influences contextual facilitation and revision during language comprehension
Hubbard, R. J., & Federmeier, K. D. (2021) · Brain Research · doi:10.1016/j.brainres.2021.147466 · Cited by 35
Self-citationPredictive sentence comprehension during story-listening in autism spectrum disorder
Brennan, J. R., Lajiness-O'Neill, R., Bowyer, S., Kovelman, I., & Hale, J. T. (2019) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2018.1560483 · Cited by 19
“LazImpa”: Lazy and impatient neural agents learn to communicate efficiently
Rita, M., Chaabouni, R., & Dupoux, E. (2020) · Proceedings of the 24th Conference on Computational Natural Language Learning · doi:10.18653/v1/2020.conll-1.26 · Cited by 17
Metadata
- Authors
- Federmeier, K. D.
- Venue
- Psychophysiology
- Publisher
- Wiley
- Published
- 2007-05-22
- Volume
- 44
- Issue
- 4
- Pages
- 491-505
- ISSN
- 0048-5772 (ISSN-L)
- 1469-8986
- OpenAlex
- W2096575147
- Cited by
- 801
- References
- 2
- FWCI
- 10.36
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.