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121 - 130 of 217 results for: PSYCH

PSYCH 203: MODELS OF LANGUAGE ACQUISITION

How do children learn to understand and produce their native language? Language acquisition is a core topic in cognitive science and has been a key test case for formal approaches. Topics include: learnability theory, grammatical approaches, connectionist models, and probabilistic models.
Terms: not given this year | Units: 3 | Grading: Letter (ABCD/NP)

PSYCH 204: Computation and cognition: the probabilistic approach

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: not given this year | Units: 3-4 | Grading: Letter or Credit/No Credit

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 | Grading: Letter or Credit/No Credit

PSYCH 204B: Computational Neuroimaging: Analysis Methods

Neuroimaging methods with focus on data analysis techniques. Basic MR physics and BOLD signals. Methods for neuroimaging data using real and simulated data sets. Topics include: linearity of the fmri signal; time versus space resolution tradeoffs; noise in neuroimaging; correlation analysis; visualization methods; cortical reconstruction, inflation, and flattening; reverse engineering; can cognitive states be predicted from brain activation? Prerequisite: consent of instructor.
Terms: Spr | Units: 1-3 | Grading: Letter or Credit/No Credit

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 | Grading: Letter or Credit/No Credit

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
Terms: Win | Units: 1-3 | Grading: Letter or Credit/No Credit

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: 2-3 | Grading: Satisfactory/No Credit

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.
Terms: not given this year | Units: 0-1 | Repeatable for credit | Grading: Satisfactory/No Credit

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.
Terms: not given this year | Units: 1-3 | Grading: Satisfactory/No Credit

PSYCH 209: Neural network and deep learning models for cognition and cognitive neuroscience

Models of cognitive and developmental processes and the brain basis of such processes, including perception, attention, memory, decision making, language processing, acting and thinking. Models considered will include neural network models including contemporary deep learning models, as well as other process models spanning a spectrum from abstract to neurally realistic. Relationships between such models and more abstract models of cognitive processes including probabilistic models will be explored. Students learn about classic models and carry out exercises in the first six weeks and will undertake projects and learn about recent developments during the last four weeks of the quarter. For advanced undergraduates and graduate students. Recommended: some familiarity with computer programming, differential equations, linear algebra, and/or probability theory, and courses in cognitive psychology and/or cognitive neuroscience.
Terms: not given this year | Units: 4 | Grading: Letter or Credit/No Credit
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