91 - 100 of 162 results for: PSYCH

How can we understand natural language use in computational terms? This course surveys probabilistic models for natural language semantics and pragmatics. It begins with an introduction to the Rational Speech Acts framework for modeling pragmatics as social reasoning. It then explores a variety of phenomena in language meaning and usage. Probabilistic programming will be used as a precise and practical way to express models.

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. The 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.

What are the neural representations and computations that give humans the amazing and unique ability to use language? This seminar course provides an overview of the field of cognitive neuroscience of language, examining the landmark empirical discoveries that have shaped our understanding of language comprehension and production in the brain. Lectures cover all aspects of language processing, from early sensory perception, to recognizing individual words, to processing complex to higher level semantic and syntactic structures. We will discuss findings from different neuroimaging methods, as well as insight from individuals with language processing disorders.

This course covers research and theory on the origins and function of social norms. Topics include the estimation of public opinion, the function of norms as ideals and standards of judgment, and the impact of norms on collective and individual behavior and norm intervention. In addition to acquainting students with the various forms and functions of social norms the course will provide students with experience in identifying and formulating tractable research questions. Priority for enrollment will be given to PhD students but advanced undergraduates may request permission for enrollment from the instructor.

The human visual system has more than two dozen topographic maps of the visual field. This course will explain principles of topographic maps in the visual system, mapping of visual areas using retinotopy, as well as modeling spatial and temporal computations in the visual system using population receptive fields. The class will combine reading and discussing papers that discovered these maps and computational principles with a lab component in which the students will analyze fMRI datasets that are used to map visual cortex. The course should be open for advanced undergrads and graduate students with prior experience in perception, cognitive neuroscience, or neuroimaging.

This course will cover both classic findings and the latest research progress on the intersection of cognitive science, neuroscience, and artificial intelligence: How does the study of minds and machines inform and guide each other? What are the assumptions, representations, or learning mechanisms that are shared (across multiple disciplines, and what are different? How can we build a synergistic partnership between cognitive psychology, neuroscience, and artificial intelligence? We will focus on object perception and social cognition (human capacities, especially in infancy and early childhood) and the ways in which these capacities are formalized and reverse-engineered (computer vision, reinforcement learning). Through paper reading and review, discussion, and the final project, students will learn the common foundations shared behind neuroscience, cognitive science, and AI research and leverage them to develop their own research project in these areas. Recommended prerequisites: PSYCH 1, PSYCH 24/ SYMSYS 1/ CS 24, CS 221, CS 231N

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.

Adaptation to speaker variability in language use has receivednincreasing attention in recent years from linguists and psychologistsnalike, who have recognized that, though long ignored, it poses a problemnfor static theories of language. The course will present a broad surveynof recent work in this area across levels of linguistic representation,nincluding phonetic, lexical, syntactic, prosodic, and semanto-pragmaticnadaptation. We will discuss the cognitive underpinnings of adaptationnand its relation to priming and learning, compare adaptation in varyingndomains, and consider the implications for theories of language andncommunication. The course will be organized primarily around discussionnof assigned readings. Students will develop a research proposal relevantnto issues in adaptation. May be repeated for credit. Prerequisite: LINGUIST 145 or background in any subfield of linguistics

The field of early childhood development is undergoing a profound transformation in the 21st century. Traditional models of child development, while foundational, are increasingly challenged by the complex realities of our rapidly changing world. This course, "The New Ecology of Early Childhood," is designed to equip graduate students with a comprehensive understanding of an emerging paradigm being formulated at the Stanford Center on Early Childhood (SCEC) that reconceptualizes early childhood development within the context of contemporary global challenges and opportunities. The course is built upon the premise that the ecological systems in which children develop are no longer as clearly delineated as once conceived. Forces that were previously considered distant or indirect now exert immediate and powerful influences on children's developmental trajectories. And the prevalence of some direct influences (e.g., time spent early care and education environments, residing in extended family households) is increasing. These shifts necessitate a radical rethinking of how we understand, study, and support early childhood development. By offering a comprehensive exploration of this emerging paradigm in early childhood development, this course prepares students to navigate the complex realities of supporting all children thriving in the 21st century. It challenges students to think critically, engage with cutting-edge research, and develop innovative solutions to pressing challenges. Through this course, students will not only gain a deeper understanding of the new ecology of early childhood but also develop the skills and perspectives necessary to become effective practitioners, researchers, and advocates in this rapidly evolving field.