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  Machine learning in psychophysical research

Wichman, F. (2010). Machine learning in psychophysical research. Talk presented at Brain Connectivity Workshop (BCW 2010). Berlin, Germany. 2010-06-01 - 2010-06-04.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0002-B100-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-B110-3
Genre: Talk

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http://brain-connectivity-workshop.org/ (Table of contents)
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 Creators:
Wichman, F1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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 and what techniques can we use to extract them? Thus one of the central challenges in psychophysics is to try and infer the critical features, or cues, human observers make use of when they see or hear: for real-world, complex stimuli, what aspect of the visual or auditory stimulus actually influences behaviour? Over the last years in my laboratory we have developed exploratory, data-driven non-linear 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 “classification image” and “bubbles technique” approaches.

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 Dates: 2010-06-04
 Publication Status: Published online
 Pages: -
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 Table of Contents: -
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Title: Brain Connectivity Workshop (BCW 2010)
Place of Event: Berlin, Germany
Start-/End Date: 2010-06-01 - 2010-06-04
Invited: Yes

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