My research focuses on the application of multimodal imaging and machine learning techniques to the study of visual processing and sustained attention. This includes the moment-to-moment fluctuations in attention to a task that are often known as “mind-wandering” and their influence on memory encoding. A major aim is to develop a covert, objective, and continuous metric of sustained attention based on eye tracking, pupillometry, and neural imaging. This metric would add a much-needed objective source of truth labels to a field that currently relies on infrequent self-reports, enabling a clearer view of the neural processes taking place and characterizing an important source of trial-to-trial variability in task-based studies.
Studies retain close ties to ecologically valid scenarios (e.g., free-viewing video and reading) so that findings may be more immediately translatable to real-world diagnosis, treatment, and educational settings. Eventual applications could identify attentional lapses in real time, aiding the diagnosis and treatment of attention deficit disorder, helping educators to improve their teaching, or informing car manufacturers on the attentional implications of their designs.
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