In order to better characterize the link between a specific network state and behavior, we must have an objective and robust behavioral tool which reliably predicts individual differences in the observed network state. Moreover, this behavioral measure must be flexible and sensitive, so that it can be used to assess small changes in networks over short time periods. The second point in particular often does not hold for many of the behavioral tools that we currently have, which consist mostly of questionnaires and neuropsychological tests. To this end, we are developing novel methods of assessing behavior, with a focus on getting more out of objective measures such as eye tracking.
Using Hollywood movie clips which elicit very consistent eye movements across participants, we examined eyemovement (a)typicality across individuals. For each participant, we caluclate the distance of their eye movements from the average scan path of everyone else watching the same movie. This gives us an individual distance measure of eye movement typicality, which is objective, sufficiently variable within the population to assess individual differences, and stable.