PSYCH-UA.46 - Fall 2023

Instructor: Brenden M. Lake

This course provides good hands-on experience with the tools, practices, and computer programming techniques used in psychological research. Students learn how to collect, analyze, and report psychological data concerning a variety aspects of human cognition and perception. By the end of the course students will have an first hand understanding of how to run psychological experiments, collect and analyze data, and write research reports. Students will also get substantial practice with computer programming in Python.

In addition to methods, the course will expose students to key concepts in cognition and perception including intelligence, learning, memory, attention, visual perception, mental imagery and imagination, and cognitive neuroscience. Data analyzed include choice data, reaction time, eye-movement data, and functional magnetic resonance imaging (fMRI). The course culminates in an intensive final project involving the design and analysis of a novel experiment.

This course is useful for undergraduate students interested in getting actively involved in research at NYU or another university. In addition, this course provides a strong background for graduate work in psychological science, particularly cognitive science, cognitive neuroscience, and perception. Even if you plan to not continue to do scientific research, the course should arm you with some of the necessary skills to be an effective worker in a world awash with data.


  • Learn the basics of using Python to explore and analyze data

  • Learn how scientific research is conducted in psychology

  • Learn how to communicate the results of psychological research to scientific community

  • Introduce key concepts used throughout the study of cognition and perception

  • Put knowledge from past coursework in statistics into practice.


Course content was created by Todd Gureckis with support of the Department of Psychology at New York University. Selected elements borrowed and adapted from Danielle Navarro’s Learning Statistics with R, Matt Crump’s Answering Questions with Data, Luke Chang’s Introduction to fMRI Data Analysis, Michael C. Frank’s Lab in Experimental Methods, Mike Landy’s Lab in Perception, Jess Hamrick’s Jupyter teaching materials, and resources developed by Clay Curtis and Liang Zhli via the Moore-Sloan Data Science Environment at NYU.

This content was supported by NSF CAREER award BCS-1255538 and DRL-1631436 to TM Gureckis.