Evidence map›Paper›PMID 33895819›Full record

ArticleCerebral cortex (New York, N.Y. : 1991)2021

Representational Pattern Similarity of Electrical Brain Activity Reveals Rapid and Specific Prediction during Language Comprehension.

Ryan J Hubbard, Kara D Federmeier

Abstract read
In one paragraph

Article in Cerebral cortex (New York, N.Y. : 1991), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Ryan J HubbardBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.
Kara D FedermeierBeckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA.

Funding

Hemispheric Differences in Memory and LanguageR01AG026308 · NIA · UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN · PI FEDERMEIER, KARA D · 2005 to 2022
$4.5M
Beckman InstituteNIA NIH HHS R01 AG026308
6 · The paper itself

Abstract

Predicting upcoming events is a critical function of the brain, and language provides a fertile testing ground for studying prediction, as comprehenders use context to predict features of upcoming words. Many aspects of the mechanisms of prediction remain elusive, partly due to a lack of methodological tools to probe prediction formation in the moment. To elucidate what features are neurally preactivated and when, we used representational similarity analysis on previously collected sentence reading data. We compared EEG activity patterns elicited by expected and unexpected sentence final words to patterns from the preceding words of the sentence, in both strongly and weakly constraining sentences. Pattern similarity with the final word was increased in an early time window following the presentation of the pre-final word, and this increase was modulated by both expectancy and constraint. This was not seen at earlier words, suggesting that predictions were precisely timed. Additionally, pre-final word activity-the predicted representation-had negative similarity with later final word activity, but only for strongly expected words. These findings shed light on the mechanisms of prediction in the brain: rapid preactivation occurs following certain cues, but the predicted features may receive reduced processing upon confirmation.

Indexed as

LanguageAdolescentAdultAnticipation, PsychologicalBrainComprehensionElectroencephalographyFemaleForecastingHumansMalePhotic StimulationYoung AdultcomprehensionEEGlanguagepredictionRSA

Identifiers

PMID33895819
PMCPMC8328210

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.