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Cognitive networks detect structural patterns and emotional complexity in suicide notes

Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find t...

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Autores principales: Stella, Massimo, Swanson, Trevor J., Li, Ying, Hills, Thomas T., Teixeira, Andreia S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773561/
https://www.ncbi.nlm.nih.gov/pubmed/36570999
http://dx.doi.org/10.3389/fpsyg.2022.917630
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author Stella, Massimo
Swanson, Trevor J.
Li, Ying
Hills, Thomas T.
Teixeira, Andreia S.
author_facet Stella, Massimo
Swanson, Trevor J.
Li, Ying
Hills, Thomas T.
Teixeira, Andreia S.
author_sort Stella, Massimo
collection PubMed
description Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations.
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spelling pubmed-97735612022-12-23 Cognitive networks detect structural patterns and emotional complexity in suicide notes Stella, Massimo Swanson, Trevor J. Li, Ying Hills, Thomas T. Teixeira, Andreia S. Front Psychol Psychology Communicating one's mindset means transmitting complex relationships between concepts and emotions. Using network science and word co-occurrences, we reconstruct conceptual associations as communicated in 139 genuine suicide notes, i.e., notes left by individuals who took their lives. We find that, despite their negative context, suicide notes are surprisingly positively valenced. Through emotional profiling, their ending statements are found to be markedly more emotional than their main body: The ending sentences in suicide notes elicit deeper fear/sadness but also stronger joy/trust and anticipation than the main body. Furthermore, by using data from the Emotional Recall Task, we model emotional transitions within these notes as co-occurrence networks and compare their structure against emotional recalls from mentally healthy individuals. Supported by psychological literature, we introduce emotional complexity as an affective analog of structural balance theory, measuring how elementary cycles (closed triads) of emotion co-occurrences mix positive, negative and neutral states in narratives and recollections. At the group level, authors of suicide narratives display a higher complexity than healthy individuals, i.e., lower levels of coherently valenced emotional states in triads. An entropy measure identified a similar tendency for suicide notes to shift more frequently between contrasting emotional states. Both the groups of authors of suicide notes and healthy individuals exhibit less complexity than random expectation. Our results demonstrate that suicide notes possess highly structured and contrastive narratives of emotions, more complex than expected by null models and healthy populations. Frontiers Media S.A. 2022-12-08 /pmc/articles/PMC9773561/ /pubmed/36570999 http://dx.doi.org/10.3389/fpsyg.2022.917630 Text en Copyright © 2022 Stella, Swanson, Li, Hills and Teixeira. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Stella, Massimo
Swanson, Trevor J.
Li, Ying
Hills, Thomas T.
Teixeira, Andreia S.
Cognitive networks detect structural patterns and emotional complexity in suicide notes
title Cognitive networks detect structural patterns and emotional complexity in suicide notes
title_full Cognitive networks detect structural patterns and emotional complexity in suicide notes
title_fullStr Cognitive networks detect structural patterns and emotional complexity in suicide notes
title_full_unstemmed Cognitive networks detect structural patterns and emotional complexity in suicide notes
title_short Cognitive networks detect structural patterns and emotional complexity in suicide notes
title_sort cognitive networks detect structural patterns and emotional complexity in suicide notes
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9773561/
https://www.ncbi.nlm.nih.gov/pubmed/36570999
http://dx.doi.org/10.3389/fpsyg.2022.917630
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