Results for PSYCH

Graduate core course. The anatomy and physiology of the brain. Methods: electrical stimulation of the brain, neuroimaging, neuropsychology, psychophysics, single-cell neurophysiology, theory and computation. Neuronal pathways and mechanisms of attention, consciousness, emotion, language, memory, motor control, and vision. Prerequisite: For psychology graduate students, or consent of instructor.

Are you interested in strengthening your skills as a facilitator or section leader? Interested in opening up dialogue around identity within your community or among friends? This course will provide you with facilitation tools and practice, but an equal part of the heart of this class will come from your own reflection on the particular strengths and challenges you may bring to facilitation and how to craft a personal style that works best for you. This reflection process is ongoing, for the instructors as well as the students.

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.

This course provides an in-depth survey and understanding of modern computational approaches to design and analyses of neuroimaging data. The course is a mixture of lectures and projects geared to give the student an understanding of the possibilities as well as limitations of different computational approaches. Topics include: signal and noise in MRI; general linear modeling; fMRI-adaptation; multivoxel pattern analyses; decoding and encoding algorithms; modeling population receptive fields. Required: Psych 204A; Recommended: Cognitive Neuroscience, Stats

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. Open to Psychology PhD students only.

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

Required of and limited to first-year Ph.D. students in Psychology. Major issues in contemporary psychology with historical backgrounds.

Neural Network models of cognitive and developmental processes and the neural basis of these processes, including contemporary deep learning models. This is a small, hands-on, discussion intensive class in which students read, comment on, and discuss readings about fundamental mathematical and computational principles of neural networks and larn about classical and contemporary applications. Students also carry out exercises in the first four weeks, then undertake projects during the last six weeks of the quarter. Intended for students with 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. Instructor consent required.

Prerequisite: 207 or consent of instructor.

This seminar is the core graduate course on affective science. We consider definitional issues, such as differences between emotion and mood, as well as issues related to the function of affect, such as the role affect plays in daily life. We review autonomic, neural, genetic, and expressive aspects of affective responding. Later in the course we discuss the role of affect in cognitive processing, specifically how affective states direct attention and influence memory, as well as the role of affect in decision making. We will also discuss emotion regulation and the strategic control of emotion; the cultural shaping of emotional experience and regulation; disorders of emotion; and developmental trajectories of experience and control from early to very late life. Meetings are discussion based. Attendance and active participation are required. Prerequisite: 207 or consent of instructor.

Social psychology from the context of society and culture. The interdependence of psychological and sociocultural processes: how sociocultural factors shape psychological processes, and how psychological systems shape sociocultural systems. Theoretical developments to understand social issues, problems, and polity. Works of Baldwin, Mead, Asch, Lewin, Burner, and contemporary theory and empirical work on the interdependence of psychology and social context as constituted by gender, ethnicity, race, religion, and region of the country and the world.

Preference to graduate students. How cultural factors shape emotion and other feeling states. Empirical and ethnographic literature, theories, and research on culture and emotion. Applications to clinical, educational, and occupational settings. Research in psychology, anthropology, and sociology. May be repeated for credit.

How can we understand intelligent behavior as computation? This course introduces probabilistic programming as a tool for cognitive modeling. We will use probabilistic generative models to explain aspects of human and artificial cognition. Topics will be drawn from causal and social reasoning, concept learning, and hierarchical abstraction.

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.

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 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.

This seminar explores how emerging findings at the interface of neuroscience, psychology, and economics combine to inform our understanding of how the brain makes decisions. Topics include neural processes related to reward, punishment, probability, risk, time, reflection, and social interaction, as well as theoretical implications and practical applications. We will briefly touch on the possibility of extending individual brain and behavioral data down to physiological and up to aggregate levels of analysis.Because the course involves interdisciplinary material, it takes the format of a research seminar with background discussions, and is targeted at graduate students and advanced undergraduates who aim to conduct related research. Goals include: (1) building familiarity with relevant neuroscience, psychology, and economics concepts; (2) increasing awareness of key relevant literature; and (3) preparation to conduct and advance innovative interdisciplinary research.

Longer lives are generating new opportunities for products and services that support them. The Stanford Center on Longevity works closely with business leaders and entrepreneurs who are envisioning emerging longevity markets. The course overviews the broad demographic changes underway and related challenges that longer lives present. Within this context, students are required to think critically about new needs and opportunities in the longevity economy.

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.

This graduate seminar will explore social-cognitive perspectives on motivation and emotion. Meetings will be discussion based. Prerequisites: Psychology 207 and consent of instructor.

This pset-focused course will train you on how to use brain imaging data - primarily functional magnetic resonance imaging while participants watch movies - to read the minds of adults. In doing so, you will acquire expertise in writing Python code, using parallel computing and analyzing big data. Recommended: PSYCH 164, PSYCH 50, or CS 106A (or equivalent).

This course brings together several literatures on the psychological, neurological, behavioral and learning impact of scarcities, especially those of money (poverty) time and food. It will identify the known psychological hallmarks of these scarcities and explore their implications for psychological functioning, well-being and education--as well as, how they can be dealt with by individuals and in education.

Classic and contemporary psychological interventions; the role of psychological factors in social reforms for social problems involving healthcare, the workplace, education, intergroup, relations, and the law. Topics include theories of intervention, the role of laboratory research, evaluation, and social policy.

Is face processing innate? Are infants passive spectators of their world or active agents pursuing goals? Why don't we remember anything from our infancy? This seminar-based course will use a Developmental Cognitive Neuroscience lens - using methods like fMRI, EEG, and animal models - to consider these big questions in early Cognitive Development. Prerequisite: 207 or consent of instructor.

How do we design artificial systems that learn as we do early in life -- as "scientists in the crib" who explore and experiment with our surroundings? How do we make AI "curious" so that it explores without explicit external feedback? Topics draw from cognitive science (intuitive physics and psychology, developmental differences), computational theory (active learning, optimal experiment design), and AI practice (self-supervised learning, deep reinforcement learning). Students present readings and complete both an introductory computational project (e.g. train a neural network on a self-supervised task) and a deeper-dive project in either cognitive science (e.g. design a novel human subject experiment) or AI (e.g. implement and test a curiosity variant in an RL environment). Prerequisites: python familiarity and practical data science (e.g. sklearn or R).

n this graduate seminar we will consider how modern computational techniques and old ideas in psychometrics combine to enable new approaches to experimentation. We will cover topics such as item response theory, optimal experiment design, adaptive experiments, and Bayesian optional stopping. We will read fairly technical papers and ask students to implement some of the algorithms we are studying.

Survey of advances in the theory of neural networks, mainly (but not solely) focused on results of relevance to theoretical neuroscience.Synthesizing a variety of recent advances that potentially constitute the outlines of a theory for understanding when a given neural network architecture will work well on various classes of modern recognition and classification tasks, both from a representational expressivity and a learning efficiency point of view. Discussion of results in the neurally-plausible approximation of back propagation, theory of spiking neural networks, the relationship between network and task dimensionality, and network state coarse-graining. Exploration of estimation theory for various typical methods of mapping neural network models to neuroscience data, surveying and analyzing recent approaches from both sensory and motor areas in a variety of species. Prerequisites: calculus, linear algebra, and basic probability theory, or consent of instructor.

May be repeated for credit. Prerequisite: consent of instructors. Restricted to Developmental graduate students.

As Kurt Lewin says, "Research that produces nothing but books will not suffice." How can you address the problems you care about through psychological science? Topics will address: What is a wise intervention? When are you ready to implement one (what do you need to know first and how can you learn it)? How can you make your intervention impactful and scaleable? How can you assess impacts and key processes, especially over time? Where should you embed your intervention and what role do contexts play? Course will feature classic and contemporary readings, discussion, and student leadership, including a focus on students' ongoing research projects.

This is an advanced graduate seminar focused on ways the ways that key life domains must change to accommodate century-long lives.

This seminar will explore the impact and relative salience of racial/ethnic identity on select issues including: discrimination, social justice, mental health and academic performance. Theoretical perspectives on identity development will be reviewed, along with research on other social identity variables, such as social class, gender and regional identifications. New areas within this field such as the complexity of multiracial identity status and intersectional invisibility will also be discussed. Though the class will be rooted in psychology and psychological models of identity formation, no prior exposure to psychology is assumed and other disciplines-including cultural studies, feminist studies, and literature-will be incorporated into the course materials. Students will work with community partners to better understand the nuances of racial and ethnic identity development in different contexts. (Cardinal Course certified by the Haas Center)

Participant presentations. May be repeated for credit. Prerequisite: graduate standing in psychology or neuroscience program.

We will read a selection of recent papers from psychology, computer science, and other fields. We will aim to understand: How human-like are state of the art artificial intelligence systems? Where can AI be better informed by recent advances in cognitive science? Which ideas from modern AI inspire new approaches to human intelligence? Specific topics will be announced prior to the beginning of term. "Registration is limited to graduate students except by instructor consent. Please write to mcfrank@stanford.edu with a one-paragraph justification if you are an undergraduate interested in registering"

This seminar will discuss the state of the art in methods for the modeling and analysis of functional magnetic resonance imaging data. Potential topics include connectivity modeling, causal modeling, multivariate pattern analysis, encoding models, and classification analysis. The seminar will include hands-on analysis exercises in addition to lectures.

The last ten years has seen a watershed in the development of large-scale neural networks in artificial intelligence. At the same time, computational neuroscientists have discovered a surprisingly robust mapping between the internal components of these networks and real neural structures in the human brain. In this class we will discuss a panoply of examples of such "convergent man-machine evolution", including: feedforward models of sensory systems (vision, audition, somatosensation); recurrent neural networks for dynamics and motor control; integrated models of attention, memory, and navigation; transformer models of language areas; self-supervised models of learning; and deep RL models of decision and planning. We will also delve into the methods and metrics for comparing such models to real-world neural data, and address how unsolved open problems in AI (that you can work on!) will drive forward novel neural models. Some meaningful background in modern neural networks is highly advised (e.g. CS229, CS230, CS231n, CS234, CS236, CS 330), but formal preparation in cognitive science or neuroscience is not needed (we will provide this).

The course provides essential background about neurodiversity, the design thinking process and the Universal Design for Learning (UDL) framework to guide students in developing projects that maximize the potential of neurodiversity. Through case studies, field trips, guest speakers, and community engagement, students will explore approaches to maximizing inclusivity in realms such as education, employment, community and beyond. Students will use their knowledge to design and develop (or revising and enhance) processes, systems, experiences and/or products to maximize inclusivity and the potential of neurodiverse individuals. Based on student's interests and areas of focus, projects may include digital tool development such as app concept and design, redesign of standard processes such as job interviews/ candidate evaluations, design and development of physical products or spaces such as sensory-sensitive dorm rooms, "stim tools" and more. Students have the option to attend Monday classes or Wednesday classes for 2 units or attend both Monday and Wednesday classes for 4 units. This course is open to undergraduate and graduate students in all schools. Cardinal Course certified by the Haas Center.

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.

Interdisciplinary seminar focusing on understanding how computations in the brain enable rapid and efficient object perception. Covers topics from multiple perspectives drawing on recent research in Psychology, Neuroscience, and Computer Science. Emphasis on discussing recent empirical findings, methods and theoretical debates in the field.

Graduate laboratory class in experimental methods for psychology, with a focus on open science methods and best practices in behavioral research. Topics include experimental design, data collection, data management, data analysis, and the ethical conduct of research. The final project of the course is a replication experiment in which students collect new data following the procedures of a published paper. The course is designed for incoming graduate students in psychology, but is open to qualified students from other programs who have some working knowledge of the R statistical programming language. Requirement: Psych 10/Stats 60 or equivalent

This course offers an introduction to advanced topics in statistics with the focus of understanding data in the behavioral and social sciences. It is a practical course in which learning statistical concepts and building models in R go hand in hand. The course is organized into three parts: In the first part, we will learn how to visualize, wrangle, and simulate data in R. In the second part, we will cover topics in frequentist statistics (such as multiple regression, logistic regression, and mixed effects models) using the general linear model as an organizing framework. We will learn how to compare models using simulation methods such as bootstrapping and cross-validation. In the third part, we will focus on Bayesian data analysis as an alternative framework for answering statistical questions. Please view course website: https://psych252.github.io/. Open to graduate students only. Requirement: Psych 10/Stats 60 or equivalent

This course is a survey of methodological issues associated with the measurement of psychological constructs and processes of change. 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.

Selective overview of the scientific study of motivation. Our focus is on interesting, experimentally tractable ideas. Meetings will be discussion based.

In this practicum, students will examine how race works in a variety of institutional spaces by participating in community partnerships relevant to criminal justice, education, economic development, or health. Limited enrollment. Prerequisite for undergraduates: Psych 1, Psych 70, and one of the following: Psych 150, Psych 180, Psych 298, Psych 103, Psych 135, Psych 30N, or Psych 138.

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 psychological perspectives on race to build practical systems that address significant societal issues. Readings will be drawn broadly from across the social sciences and computer science. Students will work with large, complex datasets and participate in research involving community partnerships relevant to race and natural language processing. Prerequisite: CS224N, PSYCH290, or equivalent background in natural language processing. Students interested in participating should complete the online application for permission at https://web.stanford.edu/class/cs329r/. Limited enrollment.

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

The goal of this course is to examine social psychological perspectives on race, crime, and punishment in the United States. Readings will be drawn not only from psychology, but also from sociology, criminology, economics, and legal studies. We will consider the manner in which social psychological variables may operate at various points in the crimina; justice system- from policing, to sentencing, to imprisonment, to re-entry. Conducted as a seminar. Students interested in participating should attend the first session and complete online application for permission at https://goo.gl/forms/CAut7RKX6MewBIuG3.

This undergraduate and graduate seminar will examine ancient Greek philosophical and contemporary psychological literatures relevant to emotion. Questions to be investigated include: What is the nature of emotions? What is the appropriate place in our lives for emotions? How should we manage our emotions? Do the emotions threaten the integrity of the agent? Meetings will be discussion oriented. Prerequisite: consent of instructor.

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.

The course will combine lectures, in-depth discussions of primary literature and computer tutorials to develop key concepts in the neuroscience of vision and how these concepts have been built on to understand the neural basis of higher cognition. 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. Familiarity with Python (or willingness to self-learn) at the level of being able to plot data and write equations from papers to simulate key aspects of vision models will be needed for computer tutorials. 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.

The course is intended as an exploration of the major ideas, theories, and findings of social psychology and their applied status. Special attention will be given to historical issues, classic experiments, and seminal theories, and their implications for topics relevant to education. Contemporary research will also be discussed. Advanced undergraduates and graduate students from other disciplines are welcome, but priority for enrollment will be given to graduate students. In order to foster a vibrant, discussion-based class, enrollment will be capped at 20 students. Interested students should enroll in the class through simple enroll or axess. There will be an application process on the first day of class if there is overwhelming interest.

Memory is not a unitary faculty, but consists of multiple forms of learning and remembering. The cognitive and neural architectures of memory, focusing on the application of functional brain imaging (primarily fMRI and ERP). Psych 45 and Psych 169 required if undergraduate student.

An essential aspect of the brain is its complex pattern of connectivity between neurons across different areas. This course will provide a comprehensive overview of the networks of the brain, analyzed from a range of standpoints from the microscopic to the macroscopic, with a particular focus on the organization of the human brain. Specific topics include brain anatomy, connectomics, structural and functional neuroimaging, graph theory and network science, dynamic models, and causal inference. The course will comprise a combination of lectures, paper discussions, and hands-on analysis exercises. The first session each week will be composed of lecture and background, and the second session will be focused on discussion and hands-on analyses, with students assigned to lead the discussion sessions. Prerequisites: Basic knowledge of neuroscience (equivalent to Psych 50A). A moderate level of programming experience will be required for hands-on exercises and problem sets. Primary exercises will be in Python.

Traditional psychological experiments have been performed at small scale: with relatively few participants, reporting on relatively restricted sets of conditions, designed to adjudicate a small number of situation-specific hypotheses. However a confluence of important developments -- from the replication crisis, to the advent of online experimental platforms, to the flowering of modern artificial intelligence -- has made it increasingly evident that psychological science can (and probably should) be done at a larger scale, and in a more systematic fashion, than ever before. In this seminar, we will discuss the meaning and purpose of scale, as it pertains not only to experiment size, but also ecological realness and validity of stimuli and conditions, and the richness of measurement instruments. We will survey recent works of "psychology at scale" from a wide variety of domains, including perception, memory, decision making, language, and social interaction. We will also discuss and develop design principles and best practices for modern psychological experiments ? principles that enable both learning from, and contributing back to, psychology-adjacent areas such as artificial intelligence, neuroscience, and statistical science.

(Graduate students register for 268.) The scientific study of emotion regulation. Topics: historical antecedents, conceptual foundations, autonomic and neural bases, individual differences, developmental and cultural aspects, implications for psychological and physical health. Focus is on experimentally tractable ideas.

May be repeated for credit. Prerequisite: graduate standing in Psychology.

A graduate seminar on writing and communication of psychological research, both for our colleagues and audiences outside the field.

Western medicine's definition of health as the absence of sickness, disease, or pathology; Native American cultures' definition of health as the beauty of physical, spiritual, emotional, and social things, and sickness as something out of balance. Topics include: historical trauma; spirituality and healing; cultural identity; values and acculturation; and individual, school, and community-based interventions. Prerequisite: experience working with American Indian communities.

Many wise interventions offer people new beliefs but have not addressed the contexts in which those beliefs will be situated and implemented. This can limit the interventions¿ effectiveness. In this course, we will explore how combining mindset change with consideration of, or change to, the setting can enhance the authenticity of the intervention and the duration of its effects. Topics will include contextual boundary conditions on direct-to-student treatments, the nature of contextual ¿affordances,¿ and the creation of contextual affordances.

Psychological research has the potential to create novel interventions that promote the public good. This workshop will expose students to psychologically 'wise' intervention research and to support their efforts to conduct such interventions, especially in the context of education, broadly conceived, as well as other areas. The first part of the class will address classic interventions and important topics in intervention research, including effective delivery mechanisms, sensitive behavioral outcomes, the role of theory and psychological process, and considerations of the role of time and of mechanisms that can sustain treatment effects over time. In the second part of the class, students will present and receive feedback on their own ongoing and/or future intervention research. Prerequisite: Graduate standing in Psychology or Education, or consent of instructor.

Intermediate-level research undertaken with members of departmental faculty. Prerequisite: consent of instructor.nn (Staff)

Intermediate-level research undertaken with Prof. Wandell for EE students. Prerequisite: consent of instructor.

What is a mindset and how does it work? This seminar will explore existing and ongoing research, ranging from research on mindsets about talent and personality to mindsets about disease, stress and aging. We will focus on key questions related to the advancement of mindset science and intervention, including: how is ¿mindset¿ defined and how is it distinct from related constructs? What are the mechanisms through which mindsets exert effects on physiology and behavior? How do mindsets speak to the issues of the interplay of nature and nurture, body and mind, cognitions and emotion? How do mindsets function at the group, organization or cultural level? How are mindsets effectively changed? What are the limits of mindsets? This seminar is offered to graduate students and others with the instructors¿ permission.

Climate scientists are largely in agreement that humanity faces a serious crisis, yet responses from the population at large and key decision makers have been mixed. How can we explain this disconnect and the diversity of reactions? In this discussion-based seminar we will read papers with a goal of understanding the psychological factors behind this situation. Topics may include: public opinions about climate; how people evaluate expert testimony; factors affecting the information people depend on; how people value future generations in decision making; and, linguistic issues in framing the debate.

Everything we learn - be it a historical fact, the meaning of a new word, or a skill like reading, math, programming or playing the piano - depends on brain plasticity. The human brain's incredible capacity for learning is served by a variety of learning mechanisms that all result in changes in brain structure and function over different time scales. The goal of this course is to (a) provide an overview of different learning systems in the brain, (b) introduce methodologies and experiments that have led to new discoveries linking human brain plasticity and learning, (3) design an experiment, collect neuroimaging data, and measure the neurobiological underpinnings of learning in your own brain with MRI. The first section of the course will involve a series of lectures and discussions on the foundations of plasticity and learning with particular attention to experimental methods used in human neuroimaging studies. The second part of the course will involve workshops on designing and implementing experiments in MATLAB/Psychtoolbox or Python/PsychoPy. During this part of the course students will design, present and implement their own experiments as group projects. Finally, students will learn how to collect and analyze MRI data by being participants in their own fMRI experiments or analyzing publicly available datasets. Requirements: This class is designed for students who are interested in gaining hands-on experience with measuring the neurobiological underpinnings of learning. Student projects will involve designing experiments, collecting and analyzing data. So some experience with MATLAB/Python or an equivalent programming language is required. Some background in neuroscience (at least 1 course) is also required as we will assume basic knowledge.

At its core, social psychology is concerned with educational problems because it addresses the problem of how to change hearts and minds in lasting ways. This course explores the major ideas, theories, and findings of social psychology, their educational implications, and the insights they shed into how and when people change. There will be a focus on educational issues. Intersections with other disciplines, in particular social development and biology, will be addressed. Historical tensions and traditions, as well as classic studies and theories, will be covered. Graduate students from other disciplines, and advanced undergraduates, are welcome (class size permitting).

Enrollment limited to teaching assistants in selected Psychology courses. May be repeated for credit.

Pedagogical training focused on teaching introductory psychology: creating engaging and inclusive lesson plans and activities, providing helpful feedback to students, responding to student feedback, and supporting student learning in 1:1 and small group interactions. Second quarter focuses on designing and iterating section activities, giving and receiving peer feedback on teaching, and reflecting on teaching practices. Limited to current graduate PSYCH 1 Teaching Fellows. May be repeated for credit.

Undergraduate and graduate students will work in teams to design, build, test, and distribute online toolkits that help practitioners solve real-world problems by applying social science. Graduate students can build toolkits for their own research. Students will learn how to assess the needs of practitioner audiences; write text, design graphics, and program activities for these audiences; prepare, deliver, and produce a TED-style online video; design surveys in Qualtrics; and build and user-test the toolkit. Readings and class discussions will include modules on design thinking, storytelling, science writing, information design, and impact evaluation. For an example of a toolkit in progress, please visit spacereface.org. Permission of instructor required.

For students interested in research in political science, psychology, or communication. Methodological techniques for studying political attitudes and behaviors. May be repeated for credit.

Psychological science has been, and continues to be, an active contributor to systemic racism. This includes, but is not limited to, creating and perpetuating ideas that directly harm communities of color, excluding communities of color from the research process, and treating the thoughts, feelings, and behaviors of white samples (and white scientists) as the invisible standard from which to judge all others. Students will leave this seminar with a deepened understanding of the historical and contemporary origins of such practices, and with a concrete understanding of how to practice anti-racism in their own research, teaching, and service.

Our topic is the psychology of racial inequality - thinking, feeling, and behaving in ways that contribute to racial stereotyping, prejudice, and discrimination, and how these processes in turn maintain and perpetuate inequality between racial groups. We will examine how these processes unfold at both the individual and the institutional levels. Throughout this course, you will familiarize yourself with the psychological perspectives, methods, and findings that help explain racial inequality, and we will explore ways to promote racial equality. The course will be conducted as a seminar, but most of what you learn will be through the readings and discussions. That is, this course is minimally didactic; the goal is to have you engage thoughtfully with the issues and readings spurred in part by sharing perspectives, confusions, and insights through writing and discussion. Each student will facilitate at least one class session by providing an introductory framework for the readings (~10-minute presentation with handouts that overviews the concepts, issues, and controversies). Together, we will broaden our knowledge base on the subject and explain, from a psychological perspective, the pervasiveness of racial inequality. Prerequisites: PSYCH 1 and PSYCH 10

This graduate seminar will review classic and recent literature on such topics as motivation, attachment, and social comparison to explore (and expand) the role of "the self" in contemporary cognitive development research. Open to PhD students only.

How do people make sense of their relationship with a community or society and how does this affect their behavior and outcomes? We will examine classic and contemporary research and theory on what belonging is; how people draw inferences about their belonging in different contexts; cultural and social-group variation; and how belonging-related motivations affect diverse behaviors.

This course offers a project-based orientation to methodological issues associated with the analysis of multivariate and/or longitudinal data in the social sciences. General areas to be covered include the manipulation/organization/description of the types of empirical data obtained in social science research, and the application/implementation of multivariate analysis techniques to those data. Students will, through hands-on analysis of their data, acquire experiences in the formulation of research questions and study designs that are appropriately tethered to a variety of advanced analytical methods.

Digital communications (including social media) are the largest data sets of our time, and most of them are text. Social scientists need to be able to digest small and big data sets alike, process them and extract psychological insight. This applied and project-focused course introduces students to a Python codebase developed to facilitate text analysis in the social sciences (see dlatk.wwbp.org -- knowledge of Python is helpful but not required). The goal is to practice these methods in guided tutorials and project-based work so that the students can apply them to their own research contexts and be prepared to write up the results for publication. The course will provide best practices, as well as access to and familiarity with a Linux-based server environment to process text, including the extraction of words and phrases, topics, and psychological dictionaries. We will also practice the use of machine learning based on text data for psychological assessment, and the further statistical analysis of language variables in R. The course has no computer science prerequisites. Familiarity with Python, SSH, and basic Linux is helpful but not required ¿ they will be minimally introduced in the course, as will SQL (databases) and Jupyter notebooks. Understanding regression, basic familiarity with R, and the ability to wrangle your data into spreadsheet form are expected. For more information, please see psych290.stanford.edu, where you will be able to access the google form to apply for the class.

Causality is central to our understanding of the world and of each other. We think causally when we predict what will happen in the future, infer what happened in the past, and interpret other people's actions and emotions. Causality is intimately linked to explanation -- to answering questions about why something happened. In this discussion-based seminar class, we will first read foundational work in philosophy that introduces the main frameworks for thinking about causation. We will then read some work on formal and computational theories of causation that was inspired by these philosophical frameworks. Equipped with this background, we will study the psychology of causal learning, reasoning, and judgment. We will tackle questions such as: How can we learn about the causal structure of the world through observation and active intervention? What is the relationship between causal reasoning and mental simulation? Why do we select to talk about some causes over others when several causes led to an outcome? Toward the end of the course, we will discuss how what we have learned in psychology about causation may be useful for other fields of inquiry, such as legal science as well as machine learning and artificial intelligence.

This seminar will focus on advanced topics in the field of emotion regulation.Meetings will be discussion based.

Explanation is a topic of longstanding interest in philosophy and psychology, and has recently attracted renewed attention due to novel challenges in interpreting and interacting with relatively opaque AI systems. In this graduate seminar, we will study the science and engineering of explanations, combining perspectives from philosophy, psychology, AI, and the legal sciences. We will ask questions like: When do we ask for explanations? What makes a good explanation? How can we build machines that can understand and explain? This interdisciplinary seminar is co-taught by Thomas Icard (Philosophy) and Tobias Gerstenberg (Psychology). We will meet twice a week (Tuesdays and Thursdays 10:30am-11:50am) to discuss research articles from a range of disciplines. Students are expected to write responses based on their readings, lead the discussion on one of the papers, and actively participate in the discussion otherwise. As a final project, students will outline a novel study on explanation that makes an empirical, modeling, or theoretical contribution. Participation is restricted to a maximum of 12 graduate students (by application). The course website, with information about application, can be found here: phil350.stanford.edu

This course is a survey of growth modeling methods useful for study of developmental and change processes. General areas to be covered include conceptualization and organization of longitudinal panel data, linear growth modeling, inclusion of time-invariant and time-varying covariates, nonlinear growth models (including a variety of exponential, sigmoid and spline models), multiple-group models, and growth mixture models. Students will work through application/implementation of the models through hands-on analysis of simulated and empirical data in both structural equation modeling (SEM) and multilevel modeling (MLM) frameworks, 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.

What is meditation? What immediate and longer-term effects does this practice have on cognition? What are the mechanisms of these effects? In this small seminar we will try to gain insight into these questions by reading and discussing recent papers drawn from psychology and neuroscience. Emphasis will be placed on careful consideration of the evidence within papers and theoretical synthesis across papers. We will also use ancient and modern studies of meditation to reflect on possibilities for the scientific study subjective experience.May be repeat for credit.

Graduate seminar. A perennial theme in cognitive science is the idea that the mind/brain can be studied at different levels of abstraction, leading to influential frameworks positing levels of analysis and of explanation. The aim of this seminar is to revisit this theme in light of new methods and tools, both theoretical and empirical. Topics will include formal and philosophical theories of (causal) abstraction, discussion of techniques for analyzing (deep) neural networks, and related ideas involving approximation, abstraction, emergence, criticality, and other themes. Note: Enrollment is limited and by application only. Please send an email to the instructors with a few words about your research areas and your interest in the seminar themes.

This course will focus on the methodological foundations of research in social psychology and allied fields, and on the background scientific and career decision-making that fosters strong research in these fields. It will focus on such topics as: why do science; how to develop research ideas and formulate a research program; classic experimental design; experimental approaches to social problems - the Lewinian tradition; the choice between laboratory, on-line, field and intervention research strategies; the role of theory in methodological choices; how to build experiments that reflect the real world; crafting IV's and DV's; the many routes to statistical power; the precautions of research hygiene; refining theory - generalizing and replicating; research productivity and the life of a research psychologist, effective approaches to writing.

This course provides an overview of the major concepts and questions in the field of health psychology. Through reading, lecture and interactive discussion, students have the opportunity to explore and think critically about a number of psychological and social influences in determining health including: emotions, beliefs, relationships, stress, motivation, behavior change, spirituality, culture, and social influence. Students will also discuss the role of important and current topics in the field of health psychology and medicine such as the changing role of the patient and provider relationship, health-care policy and the environment, placebo effects, wearable health devices, and the use of technology in medicine. Course is offered to graduate students and advanced undergraduates with permission from the instructor.

(Formerly IPS 250) (Same as LAW 5009; formerly Law 656) This seminar examines the challenges of managing and resolving intractable political and violent intergroup and international conflicts. Employing an interdisciplinary approach drawing on social psychology, political science, game theory, and international law, the course identifies various tactical, psychological, and structural barriers that can impede the achievement of efficient solutions to conflicts. We will explore a conceptual framework for conflict management and resolution that draws not only on theoretical insights, but also builds on historical examples and practical experience in the realm of conflict resolution. This approach examines the need for the parties to conflicts to address the following questions in order to have prospects of creating peaceful relationships: (1) how can the parties to conflict develop a vision of a mutually bearable shared future; (2) how can parties develop trust in the enemy; (3) how can each side be persuaded, as part of a negotiated settlement, to accept losses that it will find very painful; and (4) how do we overcome the perceptions of injustice that each side are likely to have towards any compromise solution? We will consider both particular conflicts, such as the Israeli-Palestinian conflict and the South African transition to majority rule, as well as cross-cutting issues, such as the role international legal rules play in facilitating or impeding conflict resolution, the ways intragroup dynamics affect intergroup conflict resolution efforts, and the role of criminal accountability for atrocities following civil wars. Special Instructions: Section 01: Grades will be based on class participation, written assignments, and a final exam. Section 02: Up to five students, with consent of the instructor, will have the option to write an independent research paper for Research (R) credit in lieu of the written assignments and final exam for Section 01. After the term begins, students (max 5) accepted into the course can transfer from section (01) into section (02), which meets the R requirement, with consent of the instructor.

Students register through their affiliated department; otherwise register for CHEMENG 459. For specialists and non-specialists. Sponsored by the Stanford BioX Program. Three seminars per quarter address scientific and technical themes related to interdisciplinary approaches in bioengineering, medicine, and the chemical, physical, and biological sciences. Leading investigators from Stanford and the world present breakthroughs and endeavors that cut across core disciplines. Pre-seminars introduce basic concepts and background for non-experts. Registered students attend all pre-seminars; others welcome. See http://biox.stanford.edu/courses/459.html. Recommended: basic mathematics, biology, chemistry, and physics.