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31 - 40 of 51 results for: PSYCH

PSYCH 221: Image Systems Engineering

This course is an introduction to digital imaging technologies. We focus on the principles of key elements of digital systems components; we show how to use simulation to predict how these components will work together in a complete image system simulation. The early lectures introduce the software environment and describe options for the course project. The following topics are covered and software tools are introduced:n- Basic principles of optics (Snell's Law, diffraction, adaptive optics).n- Image sensor and pixel designsn- Color science, metrics, and calibrationn- Human spatial resolutionn- Image processing principlesn- Display technologiesnA special theme of this course is that it explains how imaging technologies accommodate the requirements of the human visual system. The course also explains how image systems simulations can be useful in neuroscience and industrial vision applications.nThe course consists of lectures, software tutorials, and a course project. Tutorials and projects include extensive software simulations of the imaging pipeline. Some background in mathematics (linear algebra) and programming (Matlab) is valuable.nPre-requisite: EE 261 or equivalent. Or permission of instructor required.
Terms: Aut | Units: 1-3 | Grading: Letter or Credit/No Credit

PSYCH 226: Models and Mechanisms of Memory

Current topics in memory as explored through computational models addressing experimental findings and physiological and behavioral investigations. Topics include: episodic and statistical learning; impact of prior knowledge on new learning; and the role of MTL structures in learning and memory. May be repeated for credit.
Terms: Aut | Units: 1-3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 231: Questionnaire Design for Surveys and Laboratory Experiments: Social and Cognitive Perspectives (COMM 339, POLISCI 421K)

The social and psychological processes involved in asking and answering questions via questionnaires for the social sciences; optimizing questionnaire design; open versus closed questions; rating versus ranking; rating scale length and point labeling; acquiescence response bias; don't-know response options; response choice order effects; question order effects; social desirability response bias; attitude and behavior recall; and introspective accounts of the causes of thoughts and actions.
Terms: Aut | Units: 4 | Grading: Letter (ABCD/NP)
Instructors: Krosnick, J. (PI)

PSYCH 234: Special Topics in Depression

In this course we will discuss current issues in the study of major depression, including the epidemiology and phenomenology of depression and other affective disorders, psychological and biological theories of depression, gender differences in depression, cognitive and social functioning of depressed persons, findings from neuroimaging studies of depression, depression in children, risk factors for depression, issues involving suicide, and implications of the NIMH RDoC initiative for the study of depression and other psychiatric diagnostic categories. Prerequisite: graduate standing in Psychology or consent of instructor.
Terms: Aut | Units: 3 | Repeatable for credit | Grading: Letter or Credit/No Credit

PSYCH 246: Cognitive and Neuroscience Friday Seminar

Participant presentations. May be repeated for credit. Prerequisite: graduate standing in psychology or neuroscience program.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Wagner, A. (PI)

PSYCH 249: Large-Scale Neural Network Modeling for Neuroscience (CS 375)

Introduction to designing, building, and training neural networks for modeling brain and behavioral data, including: deep convolutional neural network models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics, memory and attention; integration of variational and generative methods for cognitive modeling; and methods and metrics for comparing such models to real-world neural data. Attention will be given both to established methods as well as cutting-edge techniques. Students will learn conceptual bases for deep neural network models, and will also implement learn to implement and train large-scale models in Tensorflow using GPUs. Requirements: Fluency in Unix shell and Python programming, familiarity with differential equations, linear algebra, and probability theory, and one or more courses in cognitive or systems neuroscience.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Yamins, D. (PI)

PSYCH 249L: Workshop on Incremental Language Processing (LINGUIST 249L)

Language is processed incrementally over time. This has consequences for language comprehension, production, acquisition, and change, all of which occur at different timescales. What is the role of time in language? The class will be based around visiting lectures by major researchers in this area, along with meetings to prepare for their visits by discussing key readings. May be repeated for credit.
Terms: Aut, Win, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Degen, J. (PI)

PSYCH 251: Lab in Experimental Methods

Laboratory class in experimental methods for psychology, with a focus on technical/computer-based methods. Programming experience helpful although not required. Topics include data collection on the web, data management and data analysis.
Terms: Aut | Units: 4 | Grading: Letter or Credit/No Credit

PSYCH 255: Seminar on Motivation

Selective overview of the scientific study of motivation. Our focus is on interesting, experimentally tractable ideas. Meetings will be discussion based.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Gross, J. (PI)

PSYCH 257: Individually Supervised Practicum

Satisfies INS requirements for curricular practical training. Relevant experience for graduate students as part of their program of study. May be repeated for credit. Prerequisites: graduate standing in Psychology, consent of adviser.nn (Staff)
Terms: Aut, Win, Spr, Sum | Units: 3-5 | Repeatable for credit | Grading: Letter (ABCD/NP)
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