System Identification Using Machine Learning Methods

author: Felix A. Wichmann, Max Planck Institute for Biological Cybernetics, Max Planck Institute
published: Dec. 3, 2012,   recorded: September 2012,   views: 4850


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


Understanding perception and the underlying cognitive processes on a behavioral level requires a solution to the feature identification problem: Which are the features on which sensory systems base their computations? What techniques can we use to identify them? Thus one of the central challenges in psychophysics is System Identification: We need to infer the critical features, or cues, human observers make use of when they see or hear. What aspect of the visual or auditory stimulus actually influences behaviour if faced with real-world, complex stimuli? In my laboratory we have developed exploratory, data-driven system identification techniques based on modern machine learning methods to infer the critical features from human behavioural judgments. I will present these methods and show what their benefits are over the traditional “reverse-correlation” approach and the “bubbles technique”.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: