Integrated cognitive systems comprise human cognitive, perception, and motor subsystems in coordinated action with interactive devices. Examples may be as simple as a human using a VCR or as complex as the behavior exhib- ited by Air Force pilots. This course will introduce students to the cognitive theory behind integrated cognitive systems, the techniques for collecting and analyzing data such as eye movements and action protocols, as well as the software tools available for the representation of interac- tive behavior. Prerequisite: Admission to doctoral program. Fall and spring terms annually. 4 credit hours

This course covers reasoning, decision making, and behav- ioral game theory, which are major domains in human higher order cognition. For each topic, we start from nor- mative theories, go through formal and mathematical models, and introduce empirical studies. The course emphasizes integrations of competing approaches within a domain, integration between reasoning and decision mak- ing, and integration between individual decision making and game-theoretic interactions. Each year, the course has a theme. The theme for this year is quantum cognition, which applies quantum theory in cognitive modeling. This course is designed as self-contained, and no pre-require- ments. A middle term presentation and a final term paper are required for each student. Graduate students only. Fall term annually. 4 credit hours

This course is about the connection between logic and artificial intelligence (AI). It may be partitioned into three general sections: 1) the straightforward application of first- order logic (FOL) in AI; 2) the broadening of FOL to enable a robot to reason in a commonsense way (nonmonotonic reasoning, induction, etc.) and to formalize a robot agent's belief and knowledge system (modal logics, etc.); and 3) using a logical approach to the Frame Problem and to building a planner. Spring term annually. 4 credit hours

An accelerated course covering important behavioral statistical concepts including probability, sampling distributions, hypothesis resting, ANOVA, and multiple regression. Course requires usage of statistical software package and is taught using the general linear model frame- work. Prerequisite: graduate status and one course in undergraduate statistics. Fall term annually. 4 credit hours

An in-depth study of quasi-experimental and experimen- tal design of behavioral research. Topics include test construction and development, factor analysis, meta-analysis, repeated measures, and MANOVA. Pre- requisite: COGS-6570 or permission of instructor. Spring term annually. 4 credit hours

An individually arranged independent study course under the supervision of a member of the Cognitive Science Department. The topic is selected by consultation between student and faculty member. Prerequisite: graduate status and permission of supervising faculty member. Fall and spring terms annually. 1 to 4 credits

An advanced course concerned with selected topics in cognitive science. Prerequisite: permission of instructor. Fall and spring terms annually. 1 to 4 credits

An overview of the structure of human language, focusing primarily on grammar. A formal analysis of (mostly English) grammatical constructions with special attention to patterns appearing in most or all languages. Implications for language learning in children and adults, the relationship between language and nonlinguistic cognition and the design of machines that understand language.

How do people navigate along a crowded sidewalk, reach for and pick up a glass of water, balance a tray of dishes, or type on a keyboard? How do highly skilled athletes, musicians, and dancers move with such efficiency and grace? What goes wrong when our ability to move becomes impaired and how can this ability be restored? Why is it so difficult to build machines to perform the same activities that most of us carry out effortlessly? This course is a graduate level introduction to the topic of perception and action. We will explore this topic from information processing, computational, dynamical systems, and ecological perspectives, review current empirical and computational research, and consider some applications, including training, rehabilitation, human-machine interaction, and robotics.

Methods of representing knowledge for automated inference and decision making are covered. Emphasis is placed on representations of time, space, causality, action, belief and uncertainty. Methods covered will include propositional, description, first-order, modal and nonmonotonic logics; Bayesian networks; probabilistic context-free grammars; scripts and frames. Emphasis is placed on the connections and differences between methods, their strengths and weaknesses and gaining experience in using them to effectively represent knowledge.

Active participation in a Master's-level project under the supervision of a faculty adviser, leading to a master's project report. Grades of IP are assigned until the master's project has been approved by the faculty adviser. If recommended by the adviser, the master's project may be accepted by the Office of Graduate Education to be archived in the Library. Grades will be listed as S. Fall and spring terms annually. 1 to 9 credits

Active participation in research, under the supervision of a faculty adviser, leading to a master's thesis. Grades of IP are assigned until the thesis has been approved by the faculty adviser and accepted by the Office of Graduate Education to be archived in a standard format in the Library. Grades will then be listed as S. Fall and spring terms annually 1 to 9 credits

Active participation in research, under the supervision of a faculty adviser, leading to a doctoral dissertation. Grades of IP are assigned until the dissertation has been publicly defended, approved by the doctoral committee, and accepted by the Office of Graduate Education to be archived in a standard format in the Library. Grades will then be listed as S. Fall and spring terms annually. Variable credit hours