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Found 7 papers, 3 papers with code
Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences
Models for human choice prediction in preference learning and psychophysics often consider only binary response data, requiring many samples to accurately learn preferences or perceptual detection thresholds.
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks
Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension.
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation
We demonstrate a clear benefit to using this new class of acquisition functions on benchmark problems, and on a challenging real-world task of estimating a high-dimensional contrast sensitivity function.
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years.
Matrix-normal models for fMRI analysis
We show how the matrix-variate normal (MN) formalism can unify some of these methods into a single framework.
A Theory of Decision Making Under Dynamic Context
We also show how the model generalizes re- cent work on the control of attention in the Flanker task (Yu et al., 2009).
