Eyetracking assessments of cognitive health in aging and neurodegeneration.
Description: Eyetracking - monitoring the movements of the eyes via cameras and infrared light - is a tool that has been used to investigate cognitive health since the 1960s. Lab-based eye movement-based tasks have been useful for revealing cognitive differences between younger and older adults, and for distinguishing between clinical populations, such as individuals with mild cognitive impairment from their healthy aged counterparts. As a result, eyetracking is poised to become a valuable tool to identify those adults who may either have, or be at risk for, neurodegeneration. However, in order for such screening tools to be effective, eyetracking-based tasks must move from out of the lab and into community settings, or even into the home. In the proposed research, we will record eye movements simultaneously with standard (lab-based) eyetrackers and an online, webcam-based, eyetracker to: 1) detail the comprehensive changes in visual exploration that occur in younger and older adults who vary in their cognitive health, and in individuals with amnestic mild cognitive impairment, 2) determine the correspondence between in-lab and online eyetracking data, 3) identify the impact of demographic factors on eyetracking metrics of cognitive health, and 4) make all data, tasks, and associated documentation open access for the scientific community. This research will reveal whether online eyetracking, when compared to in-lab eyetracking, may be a valid method to assess cognition in older adults. Our ultimate goal for this research program is to provide the foundation for the development of eyetracking-based assessment tools that may be used to support clinical decisions regarding neurodegeneration, and to track the efficacy of pharmacological and non-pharmacological interventions.
Amount: CA$348,100
Funding type: research
Funder scheme: Project Grant
Start date: 2024-04-01
End date: 2027-03-31
Investigator: Jennifer Ryan
Institution: Baycrest Academy for Research and Education (Toronto, ON)
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Funded outputs count: 0
Funder award ID: 191925_1
OpenAlex ID: G6508479265