Structural differences between REM and non-REM dream reports assessed by graph analysis
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/jspui/handle/123456789/29742 |
Resumo: | Dream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis. |
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Martin, Joshua MichaelAndriano, Danyal WainsteinMota, Natália BezerraRolim, Sérgio Arthuro MotaAraujo, John FonteneleRibeiro, Sidarta Tollendal Gomes2020-07-27T17:53:41Z2020-07-27T17:53:41Z2020-07-23MARTIN, Joshua M.; ANDRIANO, Danyal Wainstein; MOTA, Natalia B.; MOTA-ROLIM, Sergio A.; ARAÚJO, John Fontenele; SOLMS, Mark; RIBEIRO, Sidarta. Structural differences between REM and non-REM dream reports assessed by graph analysis. Plos One, [S.l.], v. 15, n. 7, p. e0228903, jul. 2020. http://dx.doi.org/10.1371/journal.pone.0228903. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228903. Acesso em: 27 jul. 2020.https://repositorio.ufrn.br/jspui/handle/123456789/2974210.1371/journal.pone.0228903Attribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessDreamsSleep, REMSleep stagesStructural differences between REM and non-REM dream reports assessed by graph analysisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleDream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis.engreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALStructuralDifferencesREM_Ribeiro_2020.pdfStructuralDifferencesREM_Ribeiro_2020.pdfStructuralDifferencesREM_Ribeiro_2020application/pdf1401168https://repositorio.ufrn.br/bitstream/123456789/29742/1/StructuralDifferencesREM_Ribeiro_2020.pdf13caeed31297ad9292772088747d97c8MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8914https://repositorio.ufrn.br/bitstream/123456789/29742/2/license_rdf4d2950bda3d176f570a9f8b328dfbbefMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/29742/3/license.txte9597aa2854d128fd968be5edc8a28d9MD53TEXTStructuralDifferencesREM_Ribeiro_2020.pdf.txtStructuralDifferencesREM_Ribeiro_2020.pdf.txtExtracted texttext/plain70558https://repositorio.ufrn.br/bitstream/123456789/29742/4/StructuralDifferencesREM_Ribeiro_2020.pdf.txtb2a8b47a51da13253e53ddee45227694MD54THUMBNAILStructuralDifferencesREM_Ribeiro_2020.pdf.jpgStructuralDifferencesREM_Ribeiro_2020.pdf.jpgGenerated Thumbnailimage/jpeg1767https://repositorio.ufrn.br/bitstream/123456789/29742/5/StructuralDifferencesREM_Ribeiro_2020.pdf.jpg444587e42395ba45e20efeb7ac26dd8bMD55123456789/297422020-08-02 04:54:39.351oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2020-08-02T07:54:39Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
title |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
spellingShingle |
Structural differences between REM and non-REM dream reports assessed by graph analysis Martin, Joshua Michael Dreams Sleep, REM Sleep stages |
title_short |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
title_full |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
title_fullStr |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
title_full_unstemmed |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
title_sort |
Structural differences between REM and non-REM dream reports assessed by graph analysis |
author |
Martin, Joshua Michael |
author_facet |
Martin, Joshua Michael Andriano, Danyal Wainstein Mota, Natália Bezerra Rolim, Sérgio Arthuro Mota Araujo, John Fontenele Ribeiro, Sidarta Tollendal Gomes |
author_role |
author |
author2 |
Andriano, Danyal Wainstein Mota, Natália Bezerra Rolim, Sérgio Arthuro Mota Araujo, John Fontenele Ribeiro, Sidarta Tollendal Gomes |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
Martin, Joshua Michael Andriano, Danyal Wainstein Mota, Natália Bezerra Rolim, Sérgio Arthuro Mota Araujo, John Fontenele Ribeiro, Sidarta Tollendal Gomes |
dc.subject.por.fl_str_mv |
Dreams Sleep, REM Sleep stages |
topic |
Dreams Sleep, REM Sleep stages |
description |
Dream reports collected after rapid eye movement sleep (REM) awakenings are, on average, longer, more vivid, bizarre, emotional and story-like compared to those collected after non-REM. However, a comparison of the word-to-word structural organization of dream reports is lacking, and traditional measures that distinguish REM and non-REM dreaming may be confounded by report length. This problem is amenable to the analysis of dream reports as non-semantic directed word graphs, which provide a structural assessment of oral reports, while controlling for individual differences in verbosity. Against this background, the present study had two main aims: Firstly, to investigate differences in graph structure between REM and non-REM dream reports, and secondly, to evaluate how non-semantic directed word graph analysis compares to the widely used measure of report length in dream analysis. To do this, we analyzed a set of 133 dream reports obtained from 20 participants in controlled laboratory awakenings from REM and N2 sleep. We found that: (1) graphs from REM sleep possess a larger connectedness compared to those from N2; (2) measures of graph structure can predict ratings of dream complexity, where increases in connectedness and decreases in randomness are observed in relation to increasing dream report complexity; and (3) measures of the Largest Connected Component of a graph can improve a model containing report length in predicting sleep stage and dream report complexity. These results indicate that dream reports sampled after REM awakening have on average a larger connectedness compared to those sampled after N2 (i.e. words recur with a longer range), a difference which appears to be related to underlying differences in dream complexity. Altogether, graph analysis represents a promising method for dream research, due to its automated nature and potential to complement report length in dream analysis. |
publishDate |
2020 |
dc.date.accessioned.fl_str_mv |
2020-07-27T17:53:41Z |
dc.date.available.fl_str_mv |
2020-07-27T17:53:41Z |
dc.date.issued.fl_str_mv |
2020-07-23 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
MARTIN, Joshua M.; ANDRIANO, Danyal Wainstein; MOTA, Natalia B.; MOTA-ROLIM, Sergio A.; ARAÚJO, John Fontenele; SOLMS, Mark; RIBEIRO, Sidarta. Structural differences between REM and non-REM dream reports assessed by graph analysis. Plos One, [S.l.], v. 15, n. 7, p. e0228903, jul. 2020. http://dx.doi.org/10.1371/journal.pone.0228903. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228903. Acesso em: 27 jul. 2020. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/jspui/handle/123456789/29742 |
dc.identifier.doi.none.fl_str_mv |
10.1371/journal.pone.0228903 |
identifier_str_mv |
MARTIN, Joshua M.; ANDRIANO, Danyal Wainstein; MOTA, Natalia B.; MOTA-ROLIM, Sergio A.; ARAÚJO, John Fontenele; SOLMS, Mark; RIBEIRO, Sidarta. Structural differences between REM and non-REM dream reports assessed by graph analysis. Plos One, [S.l.], v. 15, n. 7, p. e0228903, jul. 2020. http://dx.doi.org/10.1371/journal.pone.0228903. Disponível em: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228903. Acesso em: 27 jul. 2020. 10.1371/journal.pone.0228903 |
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https://repositorio.ufrn.br/jspui/handle/123456789/29742 |
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eng |
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eng |
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Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution 3.0 Brazil http://creativecommons.org/licenses/by/3.0/br/ |
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openAccess |
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