Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach

Abstract An impairment in pragmatic communication is a core feature of schizophrenia, often associated with difficulties in social interactions. The pragmatic deficits regard various pragmatic phenomena, e.g., direct and indirect communicative acts, deceit, irony, and include not only the use of lan...

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Autores principales:	Alberto Parola, Ilaria Gabbatore, Laura Berardinelli, Rogerio Salvini, Francesca M. Bosco
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Psychiatry RC435-571
Acceso en línea:	https://doaj.org/article/32ff0f01af31405f8404b3bd016a278d
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spelling	oai:doaj.org-article:32ff0f01af31405f8404b3bd016a278d2021-12-02T15:00:50ZMultimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach10.1038/s41537-021-00153-42334-265Xhttps://doaj.org/article/32ff0f01af31405f8404b3bd016a278d2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41537-021-00153-4https://doaj.org/toc/2334-265XAbstract An impairment in pragmatic communication is a core feature of schizophrenia, often associated with difficulties in social interactions. The pragmatic deficits regard various pragmatic phenomena, e.g., direct and indirect communicative acts, deceit, irony, and include not only the use of language but also other expressive means such as non-verbal/extralinguistic modalities, e.g., gestures and body movements, and paralinguistic cues, e.g., prosody and tone of voice. The present paper focuses on the identification of those pragmatic features, i.e., communicative phenomena and expressive modalities, that more reliably discriminate between individuals with schizophrenia and healthy controls. We performed a multimodal assessment of communicative-pragmatic ability, and applied a machine learning approach, specifically a Decision Tree model, with the aim of identifying the pragmatic features that best separate the data into the two groups, i.e., individuals with schizophrenia and healthy controls, and represent their configuration. The results indicated good overall performance of the Decision Tree model, with mean Accuracy of 82%, Sensitivity of 76%, and Precision of 91%. Linguistic irony emerged as the most relevant pragmatic phenomenon in distinguishing between the two groups, followed by violation of the Gricean maxims, and then extralinguistic deceitful and sincere communicative acts. The results are discussed in light of the pragmatic theoretical literature, and their clinical relevance in terms of content and design of both assessment and rehabilitative training.Alberto ParolaIlaria GabbatoreLaura BerardinelliRogerio SalviniFrancesca M. BoscoNature PortfolioarticlePsychiatryRC435-571ENnpj Schizophrenia, Vol 7, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Psychiatry RC435-571
spellingShingle	Psychiatry RC435-571 Alberto Parola Ilaria Gabbatore Laura Berardinelli Rogerio Salvini Francesca M. Bosco Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
description	Abstract An impairment in pragmatic communication is a core feature of schizophrenia, often associated with difficulties in social interactions. The pragmatic deficits regard various pragmatic phenomena, e.g., direct and indirect communicative acts, deceit, irony, and include not only the use of language but also other expressive means such as non-verbal/extralinguistic modalities, e.g., gestures and body movements, and paralinguistic cues, e.g., prosody and tone of voice. The present paper focuses on the identification of those pragmatic features, i.e., communicative phenomena and expressive modalities, that more reliably discriminate between individuals with schizophrenia and healthy controls. We performed a multimodal assessment of communicative-pragmatic ability, and applied a machine learning approach, specifically a Decision Tree model, with the aim of identifying the pragmatic features that best separate the data into the two groups, i.e., individuals with schizophrenia and healthy controls, and represent their configuration. The results indicated good overall performance of the Decision Tree model, with mean Accuracy of 82%, Sensitivity of 76%, and Precision of 91%. Linguistic irony emerged as the most relevant pragmatic phenomenon in distinguishing between the two groups, followed by violation of the Gricean maxims, and then extralinguistic deceitful and sincere communicative acts. The results are discussed in light of the pragmatic theoretical literature, and their clinical relevance in terms of content and design of both assessment and rehabilitative training.
format	article
author	Alberto Parola Ilaria Gabbatore Laura Berardinelli Rogerio Salvini Francesca M. Bosco
author_facet	Alberto Parola Ilaria Gabbatore Laura Berardinelli Rogerio Salvini Francesca M. Bosco
author_sort	Alberto Parola
title	Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
title_short	Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
title_full	Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
title_fullStr	Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
title_full_unstemmed	Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
title_sort	multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/32ff0f01af31405f8404b3bd016a278d
work_keys_str_mv	AT albertoparola multimodalassessmentofcommunicativepragmaticfeaturesinschizophreniaamachinelearningapproach AT ilariagabbatore multimodalassessmentofcommunicativepragmaticfeaturesinschizophreniaamachinelearningapproach AT lauraberardinelli multimodalassessmentofcommunicativepragmaticfeaturesinschizophreniaamachinelearningapproach AT rogeriosalvini multimodalassessmentofcommunicativepragmaticfeaturesinschizophreniaamachinelearningapproach AT francescambosco multimodalassessmentofcommunicativepragmaticfeaturesinschizophreniaamachinelearningapproach
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Multimodal assessment of communicative-pragmatic features in schizophrenia: a machine learning approach

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