• Tor Wager, Department of Psychology (Muenzinger D261D)
  Office Hours: Th 9:00-11:00
• Michael Mozer, Department of Computer Science (Engineering Center, Office Tower 741)
  Office Hours: Tu, Th 12:45-13:45

This course serves as an introduction to the interdisciplinary field of Cognitive Science for graduate students considering research and careers in the field. Cognitive Science is concerned with understanding the nature of cognition in humans, animals, and machines. Cognition refers to the mental processes that compose our mind: thought, reasoning, decision making, language, learning, and perception. The style of work in cognitive science is interdisciplinary, drawing upon ideas from psychology, philosophy, artificial intelligence, neuroscience, linguistics, and education. A guiding theme of work in cognitive science is the the idea that the mind can be understood as a computational system, sometimes referred to as the computational metaphor of the mind.

The course will be organized into three major sections: (1) computational methods, including cognitive architectures, production systems, neural networks, deep networks, Bayesian networks, and machine learning tools (support-vector machines, nonlinear regression, decision trees, etc.); (2) empirical methods, including experimental design, behavioral experimentation (with reaction time, errors, and choices as the dependent measures), electrophysiological studies involving EEG and event-related potentials, and neuroimaging with fMRI and PET; (3) interdisciplinary readings from the current literature.

The course is open to all graduate students with some background in at least one of the allied disciplines of Cognitive Science.

Students are expected to attend all lectures and participate in class discussions. We will assign small projects at the end of the computational methods and empirical methods sections of the course. During the readings portion of the course, students will be expected to write regular commentaries on articles in preparation for class discussions. Course grade will be roughly based on the two projects (30%), commentaries (50%), and class participation (20%). Class participation may include optional presentation of material in the latter portion of the class.

Over the course of the semester, we'll ask you to write a total of TEN commentaries on assigned or optional readings. The commentary consists of no more than one page of comments, questions, or critiques. The commentary must be submitted online to our google group, and can include one or more of the following:

• [required] a summary of what you think the main or most interesting ideas are behind the reading(s).
• questions about the material for further discussion, either clarification questions or points of disagreement with the authors (``I don't see how such and such will work as the author claims…'').
• comments on how the assigned reading relates to other course readings, or, if you feel ambitious and want to track down some related work in the field, how the assigned reading compares to this other work.
• a critique of the work: What are the flaws in the ideas presented? What are the limitations? Do the authors place their work in the appropriate theoretical perspective? Do the authors overstate their results? In what direction might the work be extended?

These commentaries are intended to promote careful thought about a paper. For most of the commentaries, you may pick articles in our readings that interest you most. The articles must be research results, and not tutorial articles (e.g., about fMRI methods). You should hand the commentary in within one week of the class in which the article or related material was discussed. You will be sharing your commentary with the class, and since not everyone will read every article, your commentary may serve as a summary for other students. It should thus be written in an accessible manner. For several assigned readings, we may require that all students write a commentary, and we may require that the commentary be done by the class in which we discuss the article. This procedure will allow us to have class discussions in which everyone is well informed in advance of the class session.

The commentaries should be mailed to the class Google Group, IssuesMethods2013@googlegroups.com. In the subject line, please use “YOUR=LAST-NAME commentary on PAPER-AUTHOR-NAMES”. Feel free to respond to others' commentaries in your own, or in separate emails. You should have an invitation to join the group.

Readings

• Abbott: theoretical neuroscience
• synesthesia (Ramachandran)
• dynamic systems models of emotion -
• educational data mining
• game theory
• neuroeconomics
• computational social neuroscience
• meyer - ML and fMRI
• luke chang - RL and social behavior
• Executive function and cognitive workload (M. Banich? Miyake?)
• IQ and “general intelligence” (N. Friedman?)
• Belief formation (M. Frank)

• memory, IQ improvements
• cognitive workloads
• consciousness (and the brain)
• executive function
• belief formation
• intuition, heuristics
• language development
• language processing