Dreaming during the Covid-19 pandemic: Computational assessment of dream reports reveals mental suffering related to fear of contagion

  1. Natália Bezerra Mota
  2. Janaina Weissheimer
  3. Marina Ribeiro
  4. Mizziara de Paiva
  5. Juliana Avilla-Souza
  6. Gabriela Simabucuru
  7. Monica Frias Chaves
  8. Lucas Cecchi
  9. Jaime Cirne
  10. Guillermo Cecchi
  11. Cilene Rodrigues
  12. Mauro Copelli
  13. Sidarta Ribeiro

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Abstract

The current global threat brought on by the Covid-19 pandemic has led to widespread social isolation, posing new challenges in dealing with metal suffering related to social distancing, and in quickly learning new social habits intended to prevent contagion. Neuroscience and psychology agree that dreaming helps people to cope with negative emotions and to learn from experience, but can dreaming effectively reveal mental suffering and changes in social behavior? To address this question, we applied natural language processing tools to study 239 dream reports by 67 individuals, made either before the Covid-19 outbreak or during the months of March and April, 2020, when lockdown was imposed in Brazil following the WHO’s declaration of the pandemic. Pandemic dreams showed a higher proportion of anger and sadness words, and higher average semantic similarities to the terms “contamination” and “cleanness”. These features seem to be associated with mental suffering linked to social isolation, as they explained 40% of the variance in the PANSS negative subscale related to socialization (p = 0.0088). These results corroborate the hypothesis that pandemic dreams reflect mental suffering, fear of contagion, and important changes in daily habits that directly impact socialization.

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  1. Version published to 10.1371/journal.pone.0242903
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.20107078: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board StatementConsent: All the participants agreed to participate and signed informed consent.
    IRB: The study was approved by the UFRN Research Ethics Committee (permit # 3.613.186).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Semantic similarity using customized software to perform Fast text method in Python: In order to investigate how the dream content must reflect pandemic related concerns or change of daily habits, we estimated the semantic similarity of dream reports to the probe words (“contamination”, “cleanness”, “sickness”, “health”, “death”, “life”).
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study has several limitations such as a small sample size, without representative diversity related to gender balance, age, or educational level. Although this might impede generalizing the obtained result to the global population, it does suggest a relatively simple and safe manner to track mental suffering during periods of forced social isolation. Larger studies in diverse samples should light our understanding of dreams as a reflector of inner suffering and fears. It might as well help us identify early signs of mental issues, being thus part of the prevention of severe mental impact. In conclusion, our results support the theory that dreams reflect daily changes during this critical period of our lives, associated with mental suffering. Observing and reporting dreams, thus, may help individuals to overcome this challenge, flagging feelings such as anxiety and sadness.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on page 20. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.05.19.20107078v2 on medRxiv
  4. Version published to 10.1101/2020.05.19.20107078v1 on medRxiv