151 - 160 of 162 results for: PSYCH

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.

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.

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.

Students will apply their data analysis, data visualization, and data interpretation skills to address a cognitive neuroscience research question. The course will comprise introductory lectures on the most foundational discoveries and pressing mysteries in cognitive neuroscience, including pertinent examples of how data science has opened powerful new lines of discovery. Students will then identify a research question of interest, and apply their suite of data science tools to address their question with an open source neuroscience dataset. Students will be provided guidance in formulating their scientific question, and translating that question into an analysis plan. At the end of the course, students will produce a research report and a presentation on their question, analysis approach, and interpretation of results. Prerequisites: STATS 200, DATASCI 112 or STATS 202, and ENGR 108 or MATH 104 or STATS 203. Enrollment limited to senior undergraduates and graduate students. This course fulfills the capstone requirement for the Data Science BS and MCS.

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.