Reasoning in Multiparty Dialogue Involving Patients with Schizophrenia

Breitholtz, Ellen; Cooper, Robin; Howes, Christine; Lavelle, Mary

doi:10.1007/978-3-030-71434-5_3

Cited by 3 publications

(3 citation statements)

References 30 publications

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“…We hypothesize that the differences seen in the turn-taking behaviours may be because the PSz lacks awareness of the normative turn-taking expectations, or because they are actively avoiding cues directed towards them: our analysis here, based on an automatic detection of silences between turns, does not distinguish between gaps which occur at a TRP and those that do not, which may be a factor here (though note that our broad observations about differences between the groups hold despite this lack of nuance). Future work will address these questions using both human annotation and recently developed automatic methods to detect TRPs [ 53 ], and will also consider the content and type of turns [ 38 , 54 ], since our qualitative analysis suggests that adjacency pairs may be used in the PSz dialogues to make turn-taking cues more explicit.…”

Section: Discussionmentioning

confidence: 99%

Quirky conversations: how people with a diagnosis of schizophrenia do dialogue differently

Howes

Lavelle

2023

Phil. Trans. R. Soc. B

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People with a diagnosis of schizophrenia (PSz) have difficulty engaging in social interaction, but little research has focused on dialogues involving PSz interacting with partners who are unaware of their diagnosis. Using quantitative and qualitative methods on a unique corpus of triadic dialogues of PSz first social encounters, we show that turn-taking is disrupted in dialogues involving a PSz. Specifically, there are on average longer gaps between turns in groups which contain a PSz compared to those which do not, particularly when the speaker switch occurs from one control (C) participant to the other. Furthermore, the expected link between gesture and repair is not present in dialogues with a PSz, particularly for C participants interacting with a PSz. As well as offering some insights into how the presence of a PSz affects an interaction, our results also demonstrate the flexibility of our mechanisms for interaction. This article is part of a discussion meeting issue ‘Face2face: advancing the science of social interaction’.

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Section: Discussionmentioning

confidence: 99%

Quirky conversations: how people with a diagnosis of schizophrenia do dialogue differently

Howes

Lavelle

2023

Phil. Trans. R. Soc. B

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“…Part of this negotiation concerns word meaning (Myrendal 2019;Breitholtz and Cooper 2021). The group must agree that a term matches their political conceptualization.…”

Section: Risementioning

confidence: 99%

The utility of (political) dogwhistles – a life cycle perspective

Sayeed,

Breitholtz,

Cooper

et al. 2024

JLP

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The term dogwhistle refers to an expression conveying a message to a subset of an audience which is not perceived by the rest of the group, in addition to a primary meaning directed at the group at large. We follow up on previous work in linguistics and political communication on defining dogwhistles, taking into account how they likely function in real-life political contexts. We consider the utility of dogwhistles in terms of their sensitivity and their specificity, which allows us to consider dogwhistles in terms of an idealized “life cycle”, whose phases we describe in terms of a multi-dimensional utility tradeoff, described in terms of dogwhistle users, the benefit they expect to receive from dogwhistling, and the deniability of controversial dogwhistle meanings. We propose an approach for the longitudinal study of dogwhistles, and describe the first stages of an experiment to characterize dogwhistles in terms of their lexical properties.

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“…Recent reviews of acoustic patterns in schizophrenia speech found that patients exhibited reduced speech speed, pitch changes, and increased pause duration [21], [22], indicating the possibility of identifying acoustic markers in schizophrenia. Furthermore, some researchers [23], [24] found that non-verbal dialogue cues (such as mutual silence, average response time, and the number of natural turns) can be used to reliably distinguish patients from normal controls or to assess negative symptoms decently.…”

Section: B Automatic Assessment Using Non-verbal Speechmentioning

confidence: 99%

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques

Huang

Lin

Liu

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for tracking the clinical patients' conditions and early detection of clinical high-risks. Detecting such symptoms require a trained clinician's expertise, which is prohibitive due to cost and the high patient-to-clinician ratio. In this paper, we propose a machine learning method using Transformer-based model to help automate the assessment of the severity of the thought disorder of schizophrenia. The proposed model uses both textual and acoustic speech between occupational therapists or psychiatric nurses and schizophrenia patients to predict the level of their thought disorder. Experimental results show that the proposed model has the ability to closely predict the results of assessments for Schizophrenia patients base on the extracted semantic, syntactic and acoustic features. Thus, we believe our model can be a helpful tool to doctors when they are assessing schizophrenia patients.

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Reasoning in Multiparty Dialogue Involving Patients with Schizophrenia

Cited by 3 publications

References 30 publications

Quirky conversations: how people with a diagnosis of schizophrenia do dialogue differently

Quirky conversations: how people with a diagnosis of schizophrenia do dialogue differently

The utility of (political) dogwhistles – a life cycle perspective

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques

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