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Bayesian Models of Sequential Dependencies in Binary and Multi-Interval Response Tasks

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.

Identiferoai:union.ndltd.org:USF/oai:scholarcommons.usf.edu:etd-6369
Date09 July 2014
CreatorsAnnis, Jeffrey Scott
PublisherScholar Commons
Source SetsUniversity of South Flordia
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceGraduate Theses and Dissertations
Rightsdefault

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