Evidence map›Paper›PMID 32126068›Full record

ArticlePloS one2020

Pragmatics, Theory of Mind and executive functions in schizophrenia: Disentangling the puzzle using machine learning.

Alberto Parola, Rogerio Salvini, Ilaria Gabbatore, Livia Colle, Laura Berardinelli, Francesca M Bosco

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 2 pooled it
3.9field-weighted citation impact, top 5% of its field
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

22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.

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  7. Pragmatic comprehension and inferential ability: interplay with executive functions and Theory of Mind.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025
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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 at 3 institutions in 2 countries.

Alberto ParolaDepartment of Psychology, University of Turin, Turin, Italy.
Rogerio SalviniInstituto de Informática, Universidade Federal de Goiás, Goiânia, GO, Brasil.ORCID 0000-0001-8889-6654
Ilaria GabbatoreDepartment of Psychology, University of Turin, Turin, Italy.ORCID 0000-0002-9724-8066
Livia ColleDepartment of Psychology, University of Turin, Turin, Italy.
Laura BerardinelliDepartment of Mental Health, A.S.L. "Città di Torino", Turin, Italy.
Francesca M BoscoDepartment of Psychology, University of Turin, Turin, Italy.
University of Turin · ITNeuroscience Institute · ITUniversidade Federal de Goiás · BR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveSchizophrenia is associated with a severe impairment in the communicative-pragmatic domain. Recent research has tried to disentangle the relationship between communicative impairment and other domains usually impaired in schizophrenia, i.e. Theory of Mind (ToM) and cognitive functions. However, the results are inconclusive and this relationship is still unclear. Machine learning (ML) provides novel opportunities for studying complex relationships among phenomena and representing causality among multiple variables. The present research explored the potential of applying ML, specifically Bayesian network (BNs) analysis, to characterize the relationship between cognitive, ToM and pragmatic abilities in individuals with schizophrenia and healthy controls, and to identify the cognitive and pragmatic abilities that are most informative in discriminating between schizophrenia and controls.

methodsWe provided a comprehensive assessment of different aspects of pragmatic performance, i.e. linguistic, extralinguistic, paralinguistic, contextual and conversational, ToM and cognitive functions, i.e. Executive Functions (EF)-selective attention, planning, inhibition, cognitive flexibility, working memory and speed processing-and general intelligence, in a sample of 32 individuals with schizophrenia and 35 controls.

resultsThe results showed that the BNs classifier discriminated well between patients with schizophrenia and healthy controls. The network structure revealed that only pragmatic Linguistic ability directly influenced the classification of patients and controls, while diagnosis determined performance on ToM, Extralinguistic, Paralinguistic, Selective Attention, Planning, Inhibition and Cognitive Flexibility tasks. The model identified pragmatic, ToM and cognitive abilities as three distinct domains independent of one another.

conclusionTaken together, our results confirmed the importance of considering pragmatic linguistic impairment as a core dysfunction in schizophrenia, and demonstrated the potential of applying BNs in investigating the relationship between pragmatic ability and cognition.

Indexed as

Executive FunctionMachine LearningSchizophrenic PsychologyTheory of MindAdultBayes TheoremCase-Control StudiesCognitionCommunicationFemaleHumansLinguisticsMaleMemory, Short-TermMiddle AgedModels, Psychological

Identifiers

PMID32126068
PMCPMC7053733
OpenAlexW3009524027

What OpenQuestion holds

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LicenceCC BY
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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.