Abstract
Task-unrelated thought (TUT), commonly referred to as mind wandering, is a mental state where a person’s attention moves away from the task-at-hand. This state is extremely common, yet not much is known about how to measure it, especially during dyadic interactions. We thus built a model to detect when a person experiences TUTs while talking to another person through a computer-mediated conversation, using their keystroke patterns. The best model was able to differentiate between task-unrelated thoughts and task-related thoughts with a kappa of 0.363, using features extracted from a 15 second window. We also present a feature analysis to provide additional insights into how various typing behaviors can be linked to our ongoing mental states.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 617-641 |
| Number of pages | 25 |
| Journal | User Modeling and User-Adapted Interaction |
| Volume | 33 |
| Issue number | 3 |
| DOIs | |
| State | Published - Jul 2023 |
Bibliographical note
Publisher Copyright:© 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Keywords
- Affective computing
- Keystrokes
- Machine learning
- Mind wandering
- Task-unrelated thought
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