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  Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data

Schönfelder, V., & Wichmann, F. (2012). Sparse regularized regression identifies behaviorally-relevant stimulus features from psychophysical data. Journal of the Acoustical Society of America, 131(5), 3953-3969. doi:10.1121/1.3701832.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-B772-E Version Permalink: http://hdl.handle.net/21.11116/0000-0001-1A04-E
Genre: Journal Article

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 Creators:
Schönfelder, VH, Author              
Wichmann, FA1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, DE, ou_1497647              

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 Abstract: As a prerequisite to quantitative psychophysical models of sensory processing it is necessary to learn to what extent decisions in behavioral tasks depend on specific stimulus features, the perceptual cues. Based on relative linear combination weights, this study demonstrates how stimulus-response data can be analyzed in this regard relying on an L1-regularized multiple logistic regression, a modern statistical procedure developed in machine learning. This method prevents complex models from over-fitting to noisy data. In addition, it enforces “sparse” solutions, a computational approximation to the postulate that a good model should contain the minimal set of predictors necessary to explain the data. In simulations, behavioral data from a classical auditory tone-in-noise detection task were generated. The proposed method is shown to precisely identify observer cues from a large set of covarying, interdependent stimulus features—a setting where standard correlational and regression methods fail. The proposed method succeeds for a wide range of signal-to-noise ratios and for deterministic as well as probabilistic observers. Furthermore, the detailed decision rules of the simulated observers were reconstructed from the estimated linear model weights allowing predictions of responses on the basis of individual stimuli.

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 Dates: 2012-05
 Publication Status: Published in print
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 Identifiers: DOI: 10.1121/1.3701832
BibTex Citekey: SchonfelderW2012
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Title: Journal of the Acoustical Society of America
Source Genre: Journal
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Pages: - Volume / Issue: 131 (5) Sequence Number: - Start / End Page: 3953 - 3969 Identifier: -