Objective Schizophrenia 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. Methods We 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. Results The 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. Conclusion Taken 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.

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

Parola, Alberto;Gabbatore, Ilaria
;
Colle, Livia;Bosco, Francesca M.
2020-01-01

Abstract

Objective Schizophrenia 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. Methods We 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. Results The 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. Conclusion Taken 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.
2020
Inglese
Esperti anonimi
15
3
e0229603-01
e0229603-17
17
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0229603
Schizophrenia, Machine learning, Bayesian network, Pragmatics, Communication, Cognitive functioning, Theory of mind
BRASILE
   Project: MOVES
   H2020
1 – prodotto con file in versione Open Access (allegherò il file al passo 6 - Carica)
262
6
Parola, Alberto; Salvini, Rogerio; Gabbatore, Ilaria; Colle, Livia; Berardinelli, Laura; Bosco, Francesca M.
info:eu-repo/semantics/article
open
03-CONTRIBUTO IN RIVISTA::03A-Articolo su Rivista
File in questo prodotto:
File Dimensione Formato  
ParolaSalvini et al., 2020_PlosOne_SchizoML.pdf

Accesso aperto

Tipo di file: PDF EDITORIALE
Dimensione 716.8 kB
Formato Adobe PDF
716.8 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1734245
Citazioni
  • ???jsp.display-item.citation.pmc??? 12
  • Scopus 31
  • ???jsp.display-item.citation.isi??? 27
social impact