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Record W2600971645 · doi:10.1093/schbul/sbx021.064

45. Impact of Theory of Mind Abilities and Negative Symptoms in Schizophrenia During Real Social Interactions

2017· article· en· W2600971645 on OpenAlexaff
Amélie M. Achim, Carolane Parent, Marion Fossard

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsTheory of mindPsychologySchizophrenia (object-oriented programming)Social cognitionTask (project management)Set (abstract data type)CognitionSocial cognitive theoryNarrativeCognitive psychologyDevelopmental psychologyPsychiatryComputer science

Abstract

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Background: There is a recognized need to understand the determinants of social functioning in people with schizophrenia. While several factors such as cognitive or social cognitive deficits or negative symptoms are linked to social functioning, we know little about the impact of these symptoms during real social interactions. Methods: We developed new social collaboration tasks based on the social communication paradigm. Participants were asked to collaborate with another person to reorder sequences of images. For each trial of the task, participants were presented with a series of 6 images forming a story and were asked to tell the story to a confederate, who had the same 6 images in a random order. The confederate was asked to place her set of images in order based on the narrative produced by the participant. She was trained to provide feedback to signal eventual misunderstandings. A first study included 25 patients with schizophrenia and 22 healthy controls who presented 6 sequences of images from different movie scenes. A second study included 21 patients with schizophrenia and 22 healthy controls who presented 9 sequences of cartoon images. Patients also completed a theory of mind task and an evaluation of their symptoms with the PANSS (5-factor version). Naive research assistants then rated the audio recordings regarding how easy it was to place the card in order from the produced stories, how interesting each story was, and how expressive the voice was. Results: In both studies, patients were judged to make the collaborative task more difficult than healthy controls (Study 1: t(45) = 2.5, P = .015; Study 2 t(41) = 2.0, P = .05). Patients’ stories were perceived as less interesting (Study 1: t(45) = 3.0, P = .004; Study 2: t(41) = 3.1, P = .003) and their voices were perceived as less expressive (Study 1: t(45) = 4.0, P < .001; Study 2 t(41) = 3.1, P = .004). In patients, the perceived difficulty was correlated with theory of mind abilities in both studies (Study 1: r = .49, P = .01; Study 2: r = .63 P = .002). In contrast, the expressiveness and the interest of the story were strongly linked together (Study 1: r = .80, P < .001; Study 2: r = .90 P < .001) and both significantly correlated with patient’s negative symptoms as assessed with the PANSS (study 1: r = −.42, P = .04 for expressiveness and r = .44, P = .03 for interest; Study 2: r = −.62, P = .003 for expressiveness and r =−.60, P = .004 for interest). Conclusion: These results suggest that during the same social interaction, theory of mind deficits and negative symptoms can affect distinct aspects of the interaction. Theory of mind deficits seem to hinder the success of collaborative interactions whereas the lack of expressiveness (and more globally negative symptoms) seem to impact how interesting patients are perceived during social interactions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.392
Teacher spread0.355 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2017
Admission routes1
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