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

Journal of the Acoustical Society of America

Volumn 131, Issue 5, 2012, Pages 3953-3969

  Schönfelder, Vinzenz H ^a,c   Wichmann, Felix A ^b,d,e  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

^b UNIVERSITY OF TÜBINGEN   (Germany)

^c BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

^d Bernstein Center for Computational Neuroscience   (Germany)

^e MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; SIGNAL TO NOISE RATIO;

BEHAVIORAL DATA; LINEAR COMBINATIONS; LINEAR MODELING; MULTIPLE LOGISTIC REGRESSION; NOISE DETECTION; REGRESSION METHOD; SENSORY PROCESSING; STIMULUS RESPONSE;

REGRESSION ANALYSIS;

ARTICLE; ARTIFICIAL INTELLIGENCE; ASSOCIATION; AUDITORY STIMULATION; COMPUTER SIMULATION; DECISION MAKING; HEARING; HUMAN; NOISE; OBSERVER VARIATION; PERCEPTION; PHYSIOLOGY; SIGNAL NOISE RATIO;

ACOUSTIC STIMULATION; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; CUES; DECISION MAKING; HUMANS; NOISE; OBSERVER VARIATION; PERCEPTUAL MASKING; PITCH PERCEPTION; SIGNAL-TO-NOISE RATIO;

EID: 84863806186     PISSN: 00014966     EISSN: None     Source Type: Journal    
DOI: 10.1121/1.3701832     Document Type: Article

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