PSYCH 242: A modern explainable AI approach to Theoretical Neuroscience (APPPHYS 293)
Modern artificial intelligence and neuroscience face a common problem: understanding how large distributed nonlinear networks of neurons learn and compute. In both AI and neuroscience we can use common experimental tools involving, observing their input-output behavior, recording the activity of neurons, inspecting the connectivity between them, and causally perturbing them. From these manipulations, how can we reverse engineer neural networks to obtain a conceptual understanding of how computation emerges from them? We will review recent literature in the emerging field of explainable AI that can shed light on these questions, with an eye towards analyzing both biological and artificial networks. Prerequisites: calculus, linear algebra, probability theory essential; basic knowledge of machine learning helpful.
| Units: 3
PSYCH 243: General Development Seminar
May be repeated for credit. Prerequisite: consent of instructors. Restricted to Developmental graduate students.
Terms: Win
| Units: 1-2
| Repeatable
for credit
Instructors:
Markman, E. (PI)
PSYCH 244: Designing Psychologically Wise Interventions
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.
Last offered: Autumn 2023
| Units: 3
PSYCH 245A: Understanding Racial and Ethnic Identity Development (AFRICAAM 245, CSRE 245, EDUC 245)
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)
Terms: Aut
| Units: 3-5
Instructors:
LaFromboise, T. (PI)
;
Keller, E. (TA)
PSYCH 247: Topics in Natural and Artificial Intelligence (SYMSYS 206)
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"
Last offered: Winter 2024
| Units: 3
PSYCH 249: Large-Scale Neural Network Modeling for Neuroscience (CS 375)
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).
Terms: Win
| Units: 3
PSYCH 249B: Topics in Neurodiversity: Design Thinking Approaches (PSYC 223B)
This course provides essential background on various aspects of neurodiversity, including, but not limited to, a new conceptualization of neurodiversity, disability laws, positive psychology, strengths-based model of neurodiversity, self-determination theory, autism, ADHD, dyslexia, savantism, mental wellness and neurodiversity, universal design for learning (UDL), and the neuroscience of neurodiversity. Through case studies, guest speakers, community engagement, and project-based learning, students will explore approaches to maximizing human potential in education, employment, and healthcare settings. Using the design thinking approach, students will use their knowledge to design and develop processes, systems, experiences, and/or products to maximize inclusivity and the potential of neurodiverse individuals. This course is open to undergraduate and graduate students in all schools. Cardinal Course certified by the Haas Center.
Terms: Win
| Units: 3
Instructors:
Fung, L. (PI)
PSYCH 250: High-level Vision: From Neurons to Deep Neural Networks (CS 431, PSYCH 151)
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.
Last offered: Winter 2021
| Units: 1-3
PSYCH 251: Experimental Methods (SYMSYS 195E)
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
Terms: Aut
| Units: 4
PSYCH 252: Statistical Methods for Behavioral and Social Sciences (COMM 352)
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
Terms: Win
| Units: 5
Instructors:
Gerstenberg, T. (PI)
;
Ram, N. (PI)
;
Lua, V. (TA)
;
Subrahmanya, S. (TA)
;
Tan, A. (TA)
;
Wu, C. (TA)
;
Yang, J. (TA)