Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: J.K. Aggarwal. Vita. Includes bibliographical references.

Thesis (M.S.)--Brigham Young University. Dept. of Statistics, 2006. / Includes bibliographical references (p. 67-68).

Thesis (Ph.D.)--McMaster University, 2004. / Advisor: James P. Reilly. Includes bibliographical references ( leaves 204-213). Also available online.

Thesis (Ph. D.)--University of Maryland, College Park, 2005. / "Bayesian scoring is used to evaluate and compare results from actual data collected for the Baltimore Metropolitan Area with the set of predominant conceptual frameworks linking travel behavior and land use obtained from the literature"--Abstract. Includes bibliographical references (p. 167-176) and abstract.

Thesis (Ph.D.)--University of Missouri-Columbia, 2006. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 1, 2007) Vita. Includes bibliographical references.

Thesis (Ph. D.)--Ohio State University, 2007. / Title from first page of PDF file. Includes bibliographical references (p. 160-170).

by Gordon M. Kaufman and S. James Press. / Also appeared in the University of Chicago series as Report 7322, Center for Mathematical Studies in Business and Economics, Department of Economics and Graduate School of Business, April 1973. / Bibliography: leaf 33.

J.L. Marroquin. / "May, 1985." / Bibliography: p. 16. / "Advanced Research Projects Agency of the Department of Defense under Office of Naval Research Contract N00014-80-C-0505" "The author was supported by the Army Research Office under contract ARO-DAAG29-84-K-0005."

Thesis (Ph.D.)--Baylor University, 2009. / Includes bibliographical references (p. 175-178).

Attentional set-shifting tasks, consisting of multiple stages of discrimination learning, have been widely used in animals and humans to investigate behavioural flexibility. However, there are several learning criteria (e.g., 6-correct-choice-in-a-row, or 10-out- of-12-correct) by which a subject might be judged to have learned a discrimination. Furthermore, the currently frequentist approach does not provide a detailed analysis of individual performance. In this PhD study, a large set of archival data of rats performing a 7-stage intra-dimensional/extra-dimensional (ID/ED) attentional set- shifting task was analysed, using a novel Bayesian analytical approach, to estimate each rat's learning processes over its trials within the task. The analysis showed that the Bayesian learning criterion may be an appropriate alternative to the frequentist n- correct-in-a-row criterion for studying performance. The individual analysis of rats' behaviour using the Bayesian model also suggested that the rats responded according to a number of irrelevant spatial and perceptual information sources before the correct stimulus-reward association was established. The efficacy of the Bayesian analysis of individual subjects' behaviour and the appropriateness of the Bayesian learning criterion were also supported by the analysis of simulated data in which the behavioural choices in the task were generated by known rules. Additionally, the efficacy was also supported by analysis of human behaviour during an analogous human 7-stage attentional set-shifting task, where participants' detailed learning processes were collected based on their trial-by-trial oral report. Further, an extended Bayesian approach, which considers the effects of feedback (correct vs incorrect) after each response in the task, can even help infer whether individual human participants have formed an attentional set, which is crucial when applying the set-shifting task to an evaluation of cognitive flexibility. Overall, this study demonstrates that the Bayesian approach can yield additional information not available to the conventional frequentist approach. Future work could include refining the rat Bayesian model and the development of an adaptive trial design.