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ECE 51100 - Psychophysics

Lecture Hours: 3 Credits: 3

Professional Attributes
EE Elective

This is a Special Content course. No more than 6 credits of Special Content type courses may apply towards the ECE Requirements of the BSEE. Excess hours can be used for Unrestricted Electives.

Normally Offered: Each Fall

Requisites:
Must be enrolled as a junior, senior or graduate classification.

Catalog Description:
Psychophysics is the quantitative study of the relationship between a physical stimulus and perception. This course focuses on the theory and practice of assessing human performance in terms of detection, discrimination, reconstruction and identification of physical events. Furthermore, it discusses mathematical and computational modeling of the underlying psychological mechanisms. Course material is presented in the context of visual, auditory, and haptic human-machine interfaces. The laboratory component of the course enables the students to practice designing, implementing and conducting psychophysical experiments.

Required Text(s):
  1. Detection Theory: A User's Guide, 2nd Edition, N. A. Macmillan & C. D. Creelman, Cambridge University Press, ISBN No. 0-8058-4230-6.
Recommended Text(s):
  1. Detection Theory: A User's Guide, 1st Edition, N. A. Macmillan & C. D. Creelman, Cambridge University Press, ISBN No. 0-521-36359-4.
  2. Psychophysics: Method, Theory, and Application, 2nd Edition, G. A. Gescheider, Lawrence Erlbaum Associates, ISBN No. 0-89859-375-1.

Learning Objectives:

A student who successfully fulfills the course requirements will have demonstrated:
  1. Identify a psychophysical problem, and formulate the problem as a detection, discrimination, reconstruction or identification experiment. [a,b,e]
  2. Identify a psychophysical problem, and formulate the problem as a detection, discrimination, reconstruction or identification experiment. [a,b,e]
  3. Select an appropriate experimental paradigm. [b]
  4. Determine the range of physical parameters that are meaningful for the specific problem, and determine the experimental parameters such as number of subjects and total trials with consideration for the statistical robustness of experimental data. [a,b]
  5. Analyze experimental data in terms of threshold or information, and form mathematical models. [a,b,e]
Assessment Method for Learning Objectives: none

Lecture Outline:

Weeks Topics
0.5 Introduction: Psychophysics in a Nutshell
2 Fechnerian Psychophysics
0.5 Signal Detection Theory
1.5 One-interval Paradigms
0.5 Rating Experiment
1.5 Two-interval Paradigms
0.5 Adaptive Methods
1 Introduction to Information Theory
2 Absolute Identification Paradigm
1 Speed-accuracy Tradeoff
2 Perception as Inverse Problems
1 Multidimensional Scaling
1 Student Project Presentations