121 - 130 of 162 results for: PSYCH

This course is a survey of methodological issues associated with multivariate analysis, the measurement of psychological constructs, and study of change processes. General areas to be covered include use of latent variable models (structural equation modeling), classical test theory, generalizability theory, principal component analysis, factor analysis, item response theory and how these models facilitate and/or constrain the study of change processes. Students will work through application/implementation of the models through hands-on analysis of simulated and empirical data, acquire experiences in the formulation of research questions and study designs that are appropriately tethered to the different theoretical perspectives invoked by the different models.

This class will teach you how to formulate, train, test, and compare your own custom statistical and mechanistic models for behavioral and neural data. The core of the class is the "universal procedure" of modern modeling that has emerged over the past 10 years, involving: (1) formulating your hypothesis space as a parameterized model, (2) optimizing model parameters to fit data with gradient methods, and (3) fairly evaluating the fitted model using cross-validation. The first part of the class will build understanding by recreating within this framework standard models you may already have encountered, such as regularized linear regression, GLMs, SVMs and logistic regression, linear mixed models, PCA and factor analysis, structural equation modeling, and simple neural networks. The second part of the class will focus on helping you workshop custom models for your own research problems. Prereqs: a working knowledge of Python programming, and Psych 251/253 (or similar courses). A few math tools will be used (derivatives and gradients, and some linear algebra), but we will help you get up to speed on these as part of the class.

Theory and research. Comparative and human research approaches map affective function to neuroanatomical and neurochemical substrates. Prerequisite: consent of instructor.

The goal of this practicum is to integrate methods from natural language processing with social scientific perspectives on race to build and/or analyze practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Projects may include large, complex datasets and involve community partnerships relevant to race and natural language processing. Graduate standing and instructor permission required. Students interested in participating should complete the online application for permission. Limited enrollment.

For students who are already or are planning to become involved in research on social construal, interventions and the role that they play in a variety of phenomena, notably the origin, escalation, and management of conflict and equality.

African American children and adolescents face a number of challenges (e.g., racism, discrimination, lack of access to resources, community violence) that can impact their mental health. Yet, they possess and utilize many strengths in the face of challenge and adversity. This seminar will explore the most salient historical, social, cultural, and ecological factors that influence the mental health and resilience of African American youth, with attention to contextual determinants that shape mental health. Applying an ecological systems approach, the course will focus on how families, schools, and communities are integral to youth's adjustment and well-being. By utilizing a culturally specific and context based lens in analyzing empirical, narrative, and visual content, students will better understand factors that can promote or inhibit the mental health and resilience of African American children and adolescents across development.

Metacognition - the ability to think about one's own thoughts - has been a topic of extensive research in the past few decades. The aim of this course is to provide an in-depth overview of the major theories and topics that have motivated empirical research, and push students to think about new directions for future work. Students will find, read, present, and discuss key theoretical and empirical papers to examine how metacognition has been defined and studied across domains (e.g., perception, memory, numerical cognition, reasoning about competence and effort, social decision-making) and disciplines (e.g., developmental, cognitive, and comparative science). Importantly, this course will also invite students to adopt a meta-stance toward research on metacognition itself: Students will be encouraged to critically examine the methods, identify gaps in the literature, and explore promising directions for future research. This course welcomes PhD students in Psychology and related disciplines whose work would benefit from a deeper understanding of the human capacity for self-reflection. Instructor permission required for Master's and Undergraduate students (background in developmental cognitive science required).

The primate visual system is a fertile ground for understanding the neuroscience underlying intelligent behavior. Indeed, much recent progress in machine learning rests upon fundamental concepts learned from the neurophysiology of vision and computational models developed from visual neuroscience. This course uses a combination of lectures, primary literature reading and computer tutorials to develop key concepts underlying computational approaches to intelligent behavior in visual neuroscience. Topics include optimal observer models, heuristics, Fourier analysis, LN models, normalization, signal detection, drift diffusion, efficient coding, Bayesian inference, visual search, metamers, texture models, population coding and recurrent dynamics. Students are expected to have familiarity with Python and linear algebra. Advanced undergraduates may enroll in this course with instructor consent (see pre-requisites in syllabus).

This course facilitates the application of the methods, theories, and findings of behavioral science to students own lives and improvement projects. It does so by combining behavioral science with a design thinking approach. You will learn to identify your potential, navigate to achieve it, and stay resilient during the journey. Students will design their own action plans, define goals and prototype strategies to test them, in an iterative feedback cycle. Our course thus blends two intellectual streams that seldom intersect: behavioral science and design thinking.