Psychology and Brain Sciences | Topical Seminar
P657 | 12083 | Busey, T.
This graduate seminar looks at the topic of perception and
perceptual learning using analysis techniques that are more advanced
than traditional voltage- or percent-BOLD change measures. The
emphasis will be on machine learning algorithms, data reduction
techniques and Bayesian methods that relate implicit measures such
as EEG and fMRI data to behavioral data in perceptual tasks.
Programming knowledge is not required, but students will be
encouraged to relate the course material to their own areas of
research, and programming skills may help in this endeavor. We will
rely on primary source articles and chapters from Perceptual
Learning by Fahle & Poggio:
Time and interest permitting, students may also have an opportunity
to collect their own EEG data to analyze as part of the course.
Enrollment is limited, and post-docs who might want to sit in on the
course are advised to speak with Dr. Busey about the possibility of
auditing the course prior to the start of the semester.