PSYC 2220. Sensation and perception I
Winter term 2023Thursday, 11:30 - 2:30
• syllabus
Description: The study of perception involves a complex combination of physiology, psychophysics, and cognition. In this course we will explore the major sensory systems focussing primarily on vision and hearing, using these topics as a framework. Throughout the course, issues will be explored from an experimental perspective. Therefore, in some lectures we will cover the fundamental experimental paradigms used to study perception (psychophysics). Over the course of the term we will discuss different sensory systems in turn; in each instance time will be spent describing the stimulus (light, sound), the physiology of the receptors and subsequent cortical areas that are activated. Very often what we perceive results not only from the information gathered by our sensory systems, but from our expectations and prior knowledge about the world we inhabit. As a result we will also study cognitive influences on perception and how they are identified and assessed.
PSYC 6273. Computer programming for experimental psychology
Fall term 2023Thursday, 11:30-2:30
• github
Description: This graduate course covers computer programming methods that are useful in experimental psychology. The course assumes no previous programming experience, and brings students to the point where they are able to write useful programs to advance their own research. Classes are held in a computer laboratory, and each week's class consists of a lecture followed by programming practice on assigned problems. Topics include the MATLAB programming language, data files, curve fitting, Monte Carlo simulations, statistical tests, journal-quality data plots, 2D and 3D graphics, and interfacing to external devices.
PSYC 6229. Statistical modelling of perception and cognition
Winter term 2020Tuesday, 11:30-2:30
• syllabus
Description: This graduate course covers fundamental statistical concepts and their application to statistical modelling in psychology. Topics in statistical foundations include probability, random variables, common statistical distributions, and Bayes' theorem. To illustrate these concepts we cover classic statistical models of behaviour and physiology, such as signal detection theory, optimal cue combination, diffusion models of reaction times, probability summation, and ideal observers. We also discuss model fitting and testing, e.g., parameter estimation, bootstrapping, goodness of fit, and model selection. The course uses R, a statistical programming language, for illustrations and problems.