Graduation Year
2014
Document Type
Dissertation
Degree
Ph.D.
Degree Granting Department
Psychology
Major Professor
Kenneth Malmberg, Ph.D.
Committee Member
Amy Criss, Ph.D.
Committee Member
Emanuel Donchin, Ph.D.
Committee Member
Chad Dubé, Ph.D.
Committee Member
Sudeep Sarkar, Ph.D.
Committee Member
Toru Shimizu, Ph.D.
Keywords
measurement models, memory models, recognition memory, assimilation
Abstract
A sequential dependency occurs when the response on the current trial is correlated with responses made on prior trials. Sequential dependencies have been observed in a variety of both perception and memory tasks. Thus, sequential dependencies provide a platform for relating these two cognitive processes. However, there are many issues associated with measuring sequential dependencies and therefore it is necessary to develop measurement models that directly address them. Here, several measurement models of sequential dependencies for both binary and multi-interval response tasks are described. The efficacy of the models is verified by applying them to simulated data sets with known properties. Lastly, the models are then applied to real-world data sets which test the critical assumption that the underlying processes of sequential dependencies are modulated by attention. The models reveal increased vigilance during testing decreases the degree of sequential dependencies.
Scholar Commons Citation
Annis, Jeffrey Scott, "Bayesian Models of Sequential Dependencies in Binary and Multi-Interval Response Tasks" (2014). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/5173
