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Researchers developed a computational model to track mind wandering (MW) during tasks. This method measures attention shifts trial-by-trial without interrupting participants, offering new insights into cognitive states.

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Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computational Modeling

Background:

  • Sustaining focused attention is crucial for goal achievement, but attention frequently lapses due to mind wandering (MW).
  • Traditional methods like thought probes are infrequent and disruptive, while other techniques assume fixed on-task/off-task time splits.
  • A need exists for continuous, non-intrusive methods to measure MW during cognitive tasks.

Purpose of the Study:

  • To develop and validate a computational framework for inferring mind wandering on a trial-by-trial basis.
  • To assess the feasibility of using computational modeling to track temporal variations in attentional states.
  • To provide a more granular understanding of MW dynamics without relying on disruptive thought probes.

Main Methods:

  • A random dot motion task with manipulated evidence and a repetitive response bias was employed.
  • A Hidden Markov Model with Generalized Linear Models (GLM-HMM) was fitted to participant performance data.
  • Occasional thought probes were used to validate the model's classification of internal states.

Main Results:

  • Off-task trials were characterized by higher accuracy and lower reaction times, indicating reduced stimulus processing and increased reliance on response bias.
  • The two-state GLM-HMM successfully predicted lower reaction times during off-task periods.
  • The model revealed an increase in mind wandering over the course of the task and correlated with self-reported focus levels.

Conclusions:

  • Computational modeling, specifically GLM-HMM, can effectively measure trial-by-trial variations in attentional states.
  • This approach allows for continuous assessment of mind wandering without interrupting cognitive tasks.
  • The findings open new avenues for research into the dynamics of attention and cognitive control.