Courses I Teach
A brief introduction to signal detection theory and optimal decision-making that I gave to an NYU symposium on Evidence in 2021.
PSYCH-GA.2211 / NEURL-GA.2201: Mathematical Tools for Neural and Cognitive Science (co-taught with Prof. Eero Simoncelli)
This is an awesome course (I can say that because Eero designed it and I’m just along for the ride) covering many of the areas of mathematics required for cutting-edge work in neural science, perception and cognitive science for stimulus generation, data analysis and modeling. Areas covered include linear algebra, regression, linear systems and Fourier analysis, probability theory, statistics and estimation (with a primary emphasis on Bayesian methods). It has a heavy workload consisting of computational problem sets. It is taught every Fall.
PSYCH-GA.2240: Psychophysics
This course reviews the basics of psychophysical methods and data analysis: signal detection theory, experimental design including adaptive methods (staircases, etc.), fitting psychometric functions to data, parameter estimation, error estimation and model comparison.
PSYCH-UA.22: Perception
This is a core course for the undergraduate Psychology major. It is a broad survey of the field of perception covering general methods for studying perception and then and in-depth look at to sensory modalities, audition and vision. For each modality we study the physical stimulus (sound, light), the underlying physiology of the sensory apparatus (ear, eye), the neural pathways, and the perceptual capabilities and phenomena.
PSYCH-UA.44: Lab in Perception
This is a hands-on laboratory course in how to design, run, analyze and report perception experiments. The course involves a series of experiments in several areas of perception: visual illusions, visual search, auditory psychophysics, lightness perception and motion adaptation. For each, there are pre-programmed experiments that have enough flexibility so that students can adapt them to answer their own research questions. Data are collected in class and students learn how to analyze the results and to make their results clear graphically using proper statistical procedures for displaying confidence intervals. Each experiment results in a proper write-up, so that students also learn about scientific writing.
PSYCH-GA.3405: Sensory Cue Integration
This was a Ph.D.-level seminar in the integration of sensory cues from multiple modalities. We reviewed the mathematical background on ideal observer models (optimal integration, causal inference). We looked at the corresponding behavioral data on cue integration, effects of causal inference and sensory recalibration. We also reviewed the neural data, both single-unit neurophysiology (with help from Prof. Dora Angelaki) and human fMRI.