cognitive tools lab

reverse engineering the human cognitive toolkit

teaching

Here are some courses that Dr. Fan regularly teaches.

PSYC 230: Computational Approaches to Visual Abstraction

From etchings on cave walls to digital media, the ability to convey abstract information in visual form is fundamental to many human innovations (e.g., painting, cartography, data visualization). This graduate seminar explores the problem of how humans produce and comprehend graphical representations, combining approaches from computational neuroscience, cognitive science, and artificial intelligence. Course participants will discuss both classic and contemporary work in these fields, work with modern computer-vision and human-behavioral datasets, and formulate an original research project to address outstanding questions. Previous experience using the Python programming language for scientific computing and prior coursework in neuroscience/psychology, calculus, and linear algebra is strongly preferred, but not strictly required, provided the student is willing to spend additional time learning foundational concepts on their own. Syllabus

PSYC 199: New Statistics Lab

This is a virtual seminar on statistical methods and other tools commonly used in psychological research. Moreover, you’ll have the opportunity to give feedback on the instructional materials we are developing. Your input will later be used to help improve the way introductory statistics is taught at UCSD. In addition, you’ll be able to complete a final project that combines a review of the primary research literature on statistics pedagogy and quantitative analyses of real-world data using the tools you have learned in the course. No prior experience with statistics is required to take this course. Syllabus

PSYC 193: Perception & Computation

This advanced undergraduate seminar explores how people perceive and understand the visual world, combining approaches from computational neuroscience, cognitive science, and artificial intelligence. Students will be introduced to both classic and contemporary work in these fields, and learn to work with modern computer-vision datasets. Prior coursework in statistics, calculus, linear algebra is strongly preferred, as well as prior experience using the Python programming language for scientific computing. Syllabus

PSYC 60: Introduction to Statistics

It is impossible to understand the modern world without an understanding of statistics. From public opinion polls to clinical trials in medicine to online systems that recommend purchases to us, statistics play a role in nearly every aspect of our lives. The goal of this course is to provide an understanding of essential concepts in statistics — how to construct models to explain variation in data — as well as the skills to apply these concepts to real data. Syllabus