Using individual traits to enhance predictions of mind wandering

Mind wandering refers to when attention shifts away from the task at hand towards unrelated thoughts, which can
interfere with learning. Researchers have identified various signs of mind wandering, such as changes in eye movements,
heart rate, and brain activity. These signs can then be utilized to train computer models that predict when mind wandering is
likely to occur. However, these models often face difficulties when applied to different tasks and different individuals.
Furthermore, some indicators, like brain activity, are challenging to measure in everyday situations. Accordingly, this project
investigates whether accounting for stable individual differences can improve the accuracy and practicality of these models.
This project, conducted in collaboration with Exo Insights, a company that uses virtual reality for training, could provide
innovative methods for monitoring attention.

Faculty Supervisor:

Daniel Smilek

Student:

Partner:

Exo Insights Corp

Discipline:

Sociology

Sector:

Professional, scientific and technical services

University:

University of Waterloo

Program: