This research project will develop methods for analyzing eye-tracking data that will provide a more accurate view of the underlying cognitive processes of human behavior. Eye movements tend to track with attention, and eye-tracking is a flexible tool for tracking eye movements. Despite the ubiquity and utility of eye-tracking methodology, current data analytic practices are limited. The methods to be developed are general and may be used to advance knowledge in other fields where comparable data are collected. The investigators will apply the new methods in interdisciplinary collaborative efforts with researchers from psycholinguistics, education, and computer science. Graduate and undergraduate students will be trained in these analytic techniques, and the results of this project will be disseminated to both specialized and multidisciplinary communities through journal articles, conferences, workshops, and a webinar. All findings, data, code, and software from this project will be made publicly available.
This research project will develop statistical models for intensive categorical time series data with the complex temporal, spatial, and dependence structures exhibited by eye-tracking data. The investigators will develop a general class of models called dynamic generalized linear mixed effect models or dynamic GLMM. These models will capture both temporal effects (serial dependence and trend effects) and spatial effects (spatial dependence and distance effects) while accounting for multiple forms of variation across data modes including trials, items, persons, item groups (or clusters), and person groups. The investigators will present a dynamic GLMM specification for binary data, which will indicate whether the participant is or is not fixating on a critical interest area at each moment in time. The investigators also will present a dynamic GLMM specification for ordered-category or partially ordered category data using an IRTree approach. This approach allows the researcher to examine cognitive processes which drive the choice among multiple stimuli for the eye to fixate on. To improve data analytic practices, methods using the dynamic GLMM will be developed following the sequence of the data analytic procedures: data description, model specification, model estimation, model selection, and model evaluation.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
