Gaze-Based Detection of Thoughts across Naturalistic Tasks Using a PSO-Optimized Random Forest Algorithm.

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks.

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

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.