Evidence map›Paper›PMID 38438713›Full record

ReviewPsychonomic bulletin & review2024

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

Massimo Stella, Salvatore Citraro, Giulio Rossetti, Daniele Marinazzo, Yoed N Kenett, Michael S Vitevitch

Abstract readReview
In one paragraph

Review in Psychonomic bulletin & review, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. Review
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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

6 authors.

Massimo StellaCogNosco Lab, Department of Psychology and Cognitive Science, University of Trento, Trento, Italy. massimo.stella@inbox.com.ORCID http://orcid.org/0000-0003-1810-9699
Salvatore CitraroInstitute of Information Science and Technologies, National Research Council, Pisa, Italy.ORCID http://orcid.org/0000-0002-5021-4790
Giulio RossettiInstitute of Information Science and Technologies, National Research Council, Pisa, Italy.ORCID http://orcid.org/0000-0003-3373-1240
Daniele MarinazzoFaculty of Psychology and Educational Sciences, Department of Data Analysis, University of Ghent, Ghent, Belgium.ORCID http://orcid.org/0000-0002-9803-0122
Yoed N KenettFaculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel.ORCID http://orcid.org/0000-0003-3872-7689
Michael S VitevitchDepartment of Speech Language Hearing, University of Kansas, Lawrence, KS, USA.ORCID http://orcid.org/0000-0002-1209-0838

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Indexed as

Concept FormationPsycholinguisticsCognitionHumansModels, PsychologicalSemanticsCognitionCognitive data scienceCognitive modellingKnowledge modellingMultilayer networksMultiplex networks

Identifiers

PMID38438713
PMCPMC11543778

What OpenQuestion holds

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LicenceCC BY
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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.