Inferring mind wandering from perceptual decision making.

People need to sustain focused attention to achieve goals. Yet, attention often lapses, as minds wander towards task-unrelated thoughts. The conventional way to study such shifts in attention is through thought probes that explicitly ask if thoughts are task-related. However, probes are rare and int...

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Publicado en:Communications Psychology Vol. 4; no. 1; pp. 1 - 15
Autores principales: Zhang, Cathy, Kool, Wouter
Formato: equations & formulas pictorial research tables/charts Journal Article
Publicado: Springer Nature 2/20/2026
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2/20/2026
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      pub: Springer Nature
      place: New York, New York
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        atl: Inferring mind wandering from perceptual decision making.
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          Zhang, Cathy
          Kool, Wouter
        affil: https://ror.org/01yc7t268 Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
      sug:
        subj:
          Wandering Behavior
          Thinking
          Decision Making
          Perception
          Task Performance and Analysis
          Hidden Markov Models
          Attention
          Human
          Reaction Time
          Self Report
          Conceptual Framework
          Male
          Female
          Descriptive Statistics
          Data Analysis Software
          Adolescence
          Young Adult
          Cognition
          Funding Source
          Adolescent: 13-18 years
          Male
          Female
      ab: People need to sustain focused attention to achieve goals. Yet, attention often lapses, as minds wander towards task-unrelated thoughts. The conventional way to study such shifts in attention is through thought probes that explicitly ask if thoughts are task-related. However, probes are rare and interrupt behavior. Other methods to measure mind wandering assume a 50/50 split in time spent on-task vs off-task. We address these issues with a framework to infer mind wandering (MW) using computational modeling. We use a random dot motion task with varying evidence, but with a strong bias inducing a repetitive response requirement. Occasional thought probes were used for validation. When participants (N = 93) reported being off-task, accuracy was higher and reaction time (RT) was lower, suggesting less stimulus processing and more reliance on bias. To classify internal states for individual trials from performance, we fit a Hidden Markov Model with Generalized Linear Models (GLM-HMM) for each state to responses. A two-state GLM-HMM predicted lower RTs on off-task trials, revealed an increase in mind wandering across the task, and aligned with self-reported focus. This shows that temporal variation in attentional states can be measured on a trial-to-trial basis without thought probes, paving the way for future MW research. Mind wandering is typically measured using rare, disruptive thought probes. This study shows that trial-by-trial fluctuations in attentional states can be inferred from behavior alone using a biased perceptual task and computational modeling.
      pubtype: Academic Journal
      doctype:
        equations & formulas
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        research
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      ougenre: Article
    language: English
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