ORCID
- Williams: https://orcid.org/0000-0002-8288-0034
- Moss: https://orcid.org/0000-0003-2139-972X
Degree
Bachelor of Science (B.S.)
Major(s)
Psychology; Biochemistry
Document Type
Temporary Embargo for Patent/Proprietary Reasons then Campus Only Restricted Access
Abstract
This study explores individual differences in learning efficiency and memory retention ability using eye tracking approaches during a model-based adaptive fact-learning task. Specifically, we examined how participant-level gaze behavior, captured through fixation features and scanpath similarity, relates to the ACT-R derived Rate of Forgetting, a trait-like idiographic parameter. Machine learning models identified fixation entropy as the strongest predictor of Rate of Forgetting and the fixation duration on answer choices as the strongest predictor of model error. Cluster analyses using MultiMatch revealed distinct gaze strategies (‘Know’, ‘Cautious’, ‘Uncertain’) that evolved across learning and corresponded with changes in Rate of Forgetting. Mixed-effects modeling found that both fixation entropy and scanpath cluster type significantly predicted Rate of Forgetting, with entropy emerging as the most predictive marker. These findings suggest that trial-level distributions of gaze patterns may be captured with fixation entropy. Thus, the current study finds evidence that eye tracking can reveal behavioral patterns within trials on fact-learning tasks that are not captured by response metrics alone.
DOI
https://doi.org/10.54718/ZGUW7352
Date Defended
5-1-2025
Thesis Director
Jarrod Moss
Second Committee Member
Jonathan Whitlock
Third Committee Member
Holli Seitz
Recommended Citation
Williams, Alyssa R. and Moss, Jarrod, "Eye Tracking Features Predict Individual Differences in Forgetting Rates" (2025). Honors Theses. 174.
https://scholarsjunction.msstate.edu/honorstheses/174
