ArticleBehavior research methods2022
Exploring self-generated thoughts in a resting state with natural language processing.
Article in Behavior research methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dynamics of the stream of thought: insights from an accumulation-to-threshold model.Neuroscience of consciousness · 2026Article
- Mapping content and dynamics in the stream of consciousness through latent brain state analysis.iScience · 2025Article
- Happier individuals generate more spontaneous thoughts about friends and value relationships over money.Communications psychology · 2025Article
- Neural dynamics of spontaneous memory recall and future thinking in the continuous flow of thoughts.Nature communications · 2025Article
- Examining Brain Function Changes in HIV-Infected Patients With Asymptomatic Neurocognitive Impairment: A Longitudinal Study.Brain and behavior · 2025Article
- Excluding spontaneous thought periods enhances functional connectivity test-retest reliability and machine learning performance in fMRI.Frontiers in neuroscience · 2025Article
- Speaking Well and Feeling Good: Age-Related Differences in the Affective Language of Resting State Thought.Affective science · 2024Article
- Creative minds at rest: Creative individuals are more associative and engaged with their idle thoughts.Creativity research journal · 2024Article
- Recent advances in the neuroscience of spontaneous and off-task thought: implications for mental health.Nature. Mental health · 2023Article
- A dorsomedial prefrontal cortex-based dynamic functional connectivity model of rumination.Nature communications · 2023Article
- Naturalistic computational psychiatry: How to get there?Journal of psychiatry & neuroscience : JPNArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The present study seeks to examine individuals' stream of thought in real time. Specifically, we asked participants to speak their thoughts freely out loud during a typical resting-state condition. We first examined the feasibility and reliability of the method and found that the oral reporting method did not significantly change the frequency or content characteristics of self-generated thoughts; moreover, its test-retest reliability was high. Based on methodological feasibility, we combined natural language processing (NLP) with the Bidirectional Encoder Representation from Transformers (BERT) model to directly quantify thought content. We analyzed the divergence of self-generated thought content and expressions of sadness and empirically verified the validity and behavioral significance of the metrics calculated by BERT. Furthermore, we found that reflection and brooding could be differentiated by detecting the divergence of self-generated thought content and expressions of sadness, thus deepening our understanding of rumination and depression and providing a way to distinguish adaptive from maladaptive rumination. Finally, this study provides a new framework to examine self-generated thoughts in a resting state with NLP, extending research on the continuous content of instant self-generated thoughts with applicability to resting-state functional brain imaging.
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Registered trials
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