CoChat
Sign Up
Cited by 11Neurobiology of Language and BilingualismReading and Literacy DevelopmentMemory Processes and Influences

Involvement of episodic memory in language comprehension: Naturalistic comprehension pushes unrelated words closer in semantic space for at least 12 h

Open access
Mak, Matthew H.C., Ball, Lewis V., O'Hagan, Alice, Walsh, Catherine R., Gaskell, M. Gareth·2025·Cognition
DOI 10.1016/j.cognition.2025.106086
Open this paper in CoChat — free account

Abstract

Recent experience with a word significantly influences its subsequent interpretation. For instance, encountering bank in a river-related context biases future interpretations toward 'side of a river' (vs. 'financial bank'). To explain this effect, the episodic context account posits that episodic memory helps bind word meanings in the language input, creating a temporary, context-specific representation that can bias subsequent lexical interpretation. This account predicts that even unrelated words would be linked together in episodic memory, potentially altering their interpretation. In Experiments 1-3, participants read unrelated word pairs (e.g., sword-microwave, privacy-export) embedded in meaningful sentences, then completed a speeded relatedness judgement task after delays of 5 min, 20 min, or 12 h (including sleep). Results showed that sentence exposure increased the likelihood of the unrelated pairs being judged as related-a robust effect observed across all delay intervals. Experiment 4 showed that this exposure effect was abolished when words in a target pair were read in separate sentences, suggesting that the exposure effect may be dependent on lexical co-occurrence. Experiment 5, also with a 12-h delay (including sleep), additionally used an innovative word arrangement task to assess word relatedness without presenting the target pairs simultaneously or successively. In line with relatedness judgement, sentence exposure pushed the unrelated words closer in semantic space. Overall, our findings suggest that a context-specific representation, supported by episodic memory, is generated during language comprehension, and in turn, these representations can influence lexical interpretation for at least 12 h and across different linguistic circumstances. We argue that these representations endow the mental lexicon with the efficiency to deal with word burstiness and the dynamic nature of language.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cognition.2025.106086

Cites

99 of 100 references verified1 citation errors · 0 not checkable

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • GenAI chatbots exhibit a positivity bias, engineered to keep outputs polite and respectful.

    “This pattern echoes with prior recognition that GenAI chatbots exhibit a positivity bias 9,41 , engineered to keep outputs polite and respectful.”

    From Style, sentiment, and quality of undergraduate writing in the AI era: A cross-sectional and longitudinal analysis of 4,820 authentic empirical reports (2025) · cites this paper as [41]

    The General discussion (p. 15) concerns episodic memory and judgments about whether words are related, not GenAI chatbots. Searches for chatbot positivity bias and polite or respectful AI outputs found no passage addressing either point in our copy. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. Mak, M. H. C., & Nation, K. (2024). A review of how word learning during reading is influenced by the linguistic context: From contextual constraint to contextual diversity . http://refhub.elsevier.com/S0010-0277(2500026-5/rf0240)

Cited by

Sorted by citations

Related

Authors
Mak, M. H. C., Ball, L. V., O'Hagan, A., Walsh, C. R., & Gaskell, M. G.
Affiliations
  • University of Warwick (GB)
  • University of York (GB)
  • University of California, Los Angeles (US)
ORCID
Venue
Cognition
Publisher
Elsevier BV
Published
2025-05
Volume
258
Pages
106086
ISSN
0010-0277 (ISSN-L)
DOI
10.1016/j.cognition.2025.106086
OpenAlex
W4407775405
Cited by
9
References
94
Citations by year
2026: 7 · 2025: 2
FWCI
5.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Cognition (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
  • Warwick Research Archive Portal (University of Warwick) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
UK Research and Innovation Economic and Social Research Council (ES/T008571/1)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
  • Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Memory Processes and Influences
Keywords
episodic memory, language comprehension, semantic relatedness, lexical interpretation, semantic space, word co-occurrence, memory delay, sleep, mental lexicon
MeSH terms
Adult, Female, Humans, Language, Male, Psycholinguistics, Reading, Semantics, Time Factors, Comprehension, Young Adult, Memory, Episodic

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