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Automatically detecting task-unrelated thoughts during conversations using keystroke analysis.

Vishal Kuvar1,2, Nathaniel Blanchard3, Alexander Colby2

  • 1Minneapolis, USA University of Minnesota Twin Cities.

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|August 29, 2022
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Summary
This summary is machine-generated.

Researchers developed a model to detect mind wandering during conversations using typing patterns. This study offers insights into how keystroke dynamics can reveal task-unrelated thoughts in real-time interactions.

Keywords:
Affective computingKeystrokesMachine learningMind wanderingTask-unrelated thought

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Psychology

Background:

  • Task-unrelated thought (TUT), or mind wandering, is common but difficult to measure, especially during social interactions.
  • Understanding mind wandering in dyadic settings is crucial for fields like psychology and HCI.
  • Existing methods for detecting TUTs are often intrusive or not suitable for real-time analysis.

Purpose of the Study:

  • To develop and validate a computational model for detecting task-unrelated thoughts (TUTs) during computer-mediated dyadic conversations.
  • To explore the relationship between typing patterns and ongoing mental states.
  • To provide a non-intrusive method for assessing mind wandering in interactive settings.

Main Methods:

  • A machine learning model was constructed to analyze keystroke patterns during computer-mediated conversations.
  • Features were extracted from typing data within a 15-second window to predict mental states.
  • The model's performance was evaluated using kappa statistic to differentiate between task-related and task-unrelated thoughts.

Main Results:

  • The best-performing model achieved a kappa of 0.363 in distinguishing between task-unrelated thoughts and task-related thoughts.
  • Feature analysis revealed specific typing behaviors associated with different mental states.
  • The findings suggest that keystroke dynamics can serve as a reliable indicator of mind wandering.

Conclusions:

  • Keystroke patterns can be effectively utilized to detect task-unrelated thoughts during dyadic interactions.
  • The developed model offers a novel, non-intrusive approach to studying mind wandering in real-world communication.
  • Further research can build upon these findings to enhance understanding of cognitive states in computer-mediated communication.