Evidence map›Paper›PMID 36458499›Full record

ArticleQuarterly journal of experimental psychology (2006)2023

Contextual dynamics in lexical encoding across the ageing spectrum: A simulation study.

Brendan T Johns, Vanessa Taler, Michael N Jones

Abstract read
In one paragraph

Article in Quarterly journal of experimental psychology (2006), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

3 authors.

Brendan T JohnsDepartment of Psychology, McGill University, Montreal, Quebec, Canada.ORCID 0000-0001-6152-4914
Vanessa TalerUniversity of Ottawa, Ottawa, Ontario, Canada.
Michael N JonesIndiana University Bloomington, Bloomington, IN, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The field of psycholinguistics has recently questioned the primacy of word frequency (WF) in influencing word recognition and production, instead focusing on the importance of a word's contextual diversity (CD). WF is operationalised by counting the number of occurrences of a word in a corpus, while a word's CD is a count of the number of contexts that a word occurs in, with repetitions within a context being ignored. Numerous studies have converged on the conclusion that CD is a better predictor of word recognition latency and accuracy than frequency. These findings support a cognitive mechanism based on the principle of likely need over the principle of repetition in lexical organisation. In the current study, we trained the semantic distinctiveness model on communication patterns in social media platforms consisting of over 55-billion-word tokens and examined the ability of theoretically distinct models to explain word recognition latency and accuracy data from over 1 million participants from the Mandera et al. English Crowdsourding Project norms, consisting of approximately 59,000 words across six age bands ranging from ages 10 to 60 years. There was a clear quantitative trend across the age bands, where there is a shift from a social environment-based attention mechanism in the "younger" models, to a clear dominance for a discourse-based attention mechanism as models "aged." This pattern suggests that there is a dynamical interaction between the cognitive mechanisms of lexical organisation and environmental information that emerges across ageing.

Indexed as

PsycholinguisticsSemanticsAdolescentAdultAgingChildCommunicationComputer SimulationHumansMiddle AgedYoung Adultbig datacognitive modellingcontextual diversityLexical organisationlexical semantics

Identifiers

PMID36458499
PMCPMC10466941

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Registered trials

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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.