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Language of Perseverative Thoughts Predicts Emotion Regulation Strategy Choice.

Yiqin Zhu1, Kevin Trent1, Beatris Garcia1

  • 1Department of Psychological and Brain Sciences, Washington University in St. Louis, St Louis, MO USA.

Affective Science
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

Perseverative thought dimensions influence emotion regulation strategy choice. Negative valence during thought predicts distraction, while self-focus predicts avoidance or suppression.

Keywords:
Emotion regulation choiceExperience samplingLIWCNatural language processingPerseverative thought

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Area of Science:

  • Psychology
  • Cognitive Science
  • Computational Linguistics

Background:

  • Emotion regulation (ER) strategy selection is influenced by contextual factors.
  • Perseverative thought (PT) presents an ongoing internal context impacting emotional responses.
  • Understanding PT's influence on ER is crucial for mental health research.

Purpose of the Study:

  • To investigate how dimensions of perseverative thought (PT) predict subsequent emotion regulation (ER) strategy choice.
  • To examine the relationship between specific PT characteristics (e.g., valence, self-focus) and the selection of engagement or disengagement ER strategies.
  • To explore the utility of computational linguistics in analyzing PT's influence on ER.

Main Methods:

  • A 10-day daily diary study with 413 college students.
  • Inclusion of a perseverative thought (PT) induction writing task each night.
  • Utilizing Linguistic Inquiry Word Count (LIWC) for PT dimension analysis and multilevel logistic regressions for ER strategy prediction.

Main Results:

  • Higher negative valence in PT predicted increased use of distraction.
  • Increased self-focus during PT was associated with greater experiential avoidance and expressive suppression.
  • Higher situation certainty, social words, and past focus in PT predicted less problem-solving strategy use.

Conclusions:

  • Dimensions of perseverative thought (PT) significantly shape emotion regulation (ER) strategy selection.
  • Specific PT characteristics are linked to distinct ER strategies, offering insights into emotional processing.
  • Computational linguistic analysis of written PT provides a valuable dimensional approach to studying ER contexts.