PSYCH 204: Computation and cognition: the probabilistic approach (CS 428)
This course will introduce the probabilistic approach to cognitive science, in which learning and reasoning are understood as inference in complex probabilistic models. Examples will be drawn from areas including concept learning, causal reasoning, social cognition, and language understanding. Formal modeling ideas and techniques will be discussed in concert with relevant empirical phenomena.
Terms: Aut
| Units: 3
PSYCH 204A: Human Neuroimaging Methods
This course introduces the student to human neuroimaging using magnetic resonance scanners. The course is a mixture of lectures and hands-on software tutorials. The course begins by introducing basic MR principles. Then various MR measurement modalities are described, including several types of structural and functional imaging methods. Finally algorithms for analyzing and visualizing the various types of neuroimaging data are explained, including anatomical images, functional data, diffusion imaging (e.g., DTI) and magnetization transfer. Emphasis is on explaining software methods used for interpreting these types of data.
Terms: Win
| Units: 3
PSYCH 204B: Human Neuroimaging Methods
This course introduces the student to human neuroimaging using magnetic resonance scanners. The course is a mixture of lectures and hands-on software tutorials. The course begins by introducing basic MR principles. Then various MR measurement modalities are described, including several types of structural and functional imaging methods. Finally algorithms for analyzing and visualizing the various types of neuroimaging data are explained, including anatomical images, functional data, diffusion imaging (e.g., DTI) and magnetization transfer. Emphasis is on explaining software methods used for interpreting these types of data.nRequired:
Psych 204a; Recommended: Cognitive Neuroscience.
Terms: Spr
| Units: 3
Instructors:
Grill-Spector, K. (PI)
;
Bugatus, L. (TA)
PSYCH 205: Foundations of Cognition
Topics: attention, memory, language, similarity and analogy, categories and concepts, learning, reasoning, and decision making. Emphasis is on processes that underlie the capacity to think and how these are implemented in the brain and modeled computationally. The nature of mental representations, language and thought, modular versus general purpose design, learning versus nativism. Prerequisite: 207 or consent of instructor. nOpen to Psychology PhD students only.
Terms: Spr
| Units: 1-3
Instructors:
McClelland, J. (PI)
PSYCH 206: Cortical Plasticity: Perception and Memory
Seminar. Topics related to cortical plasticity in perceptual and memory systems including neural bases of implicity memory, recognition memory, visual priming, and perceptual learning. Emphasis is on recent research with an interdisciplinary scope, including theory, behavioral findings, neural mechanisms, and computational models. May be repeated for credit. Recommended: 30, 45
Last offered: Winter 2016
PSYCH 207: Professional Seminar for First-Year Ph.D. Graduate Students
Required of and limited to first-year Ph.D. students in Psychology. Major issues in contemporary psychology with historical backgrounds.
Terms: Aut
| Units: 3
Instructors:
Danoyan, T. (PI)
;
Gotlib, I. (PI)
PSYCH 207B: Professional Development Seminar in Psychology
For graduate students who wish to gain professional development skills to pursue an academic career. May be repeated for credit. Course is intended for second year Ph.D. student in Psychology but open to all years.
Last offered: Winter 2011
| Repeatable
1 times
(up to 1 units total)
PSYCH 208: Advanced Topics in Self-Defense
Seminar. Threat to the self and how people deal with them. Readings from social psychological areas including social comparison, self-affirmation, self-completion, self-discrepancy, shame and guilt, terror management, dimensions of self-worth, self-regulation, self-presentation, psychophysiology, and moral identity. Enrollment limited to 15.
Last offered: Winter 2008
PSYCH 209: Neural Network Models of Cognition: Principles and Applications
Neural Network models of cognitive and developmental processes and the neural basis of these processes, including contemporary deep learning models. Students learn about fundamental computational principles and classical as well as contemporary applications and carry out exercises in the first six weeks, then undertake projects during the last four weeks of the quarter. Recommended: computer programming ability, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognition, cognitive development or cognitive/systems neuroscience.
Terms: Win
| Units: 4
Instructors:
McClelland, J. (PI)
;
Hansen, S. (TA)
PSYCH 211: Developmental Psychology
Prerequisite: 207 or consent of instructor.
Terms: Win
| Units: 1-3
Instructors:
Dweck, C. (PI)
;
Markman, E. (PI)
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