Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences
This work addresses the challenge of efficiently learning human preferences and perceptual thresholds in psychophysics and preference learning, though it is incremental as it builds on existing diffusion decision models with a differentiable approximation.
The authors tackled the problem of improving choice prediction and function estimation in Gaussian process models for perception and preferences by incorporating response times, which are typically ignored or handled with intractable models. They achieved better latent value estimation and held-out choice prediction compared to baselines on three real-world datasets.
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. The response time (RT) to make each choice captures additional information about the decision process, however existing models incorporating RTs for choice prediction do so in fully parametric settings or over discrete stimulus sets. This is in part because the de-facto standard model for choice RTs, the diffusion decision model (DDM), does not admit tractable, differentiable inference. The DDM thus cannot be easily integrated with flexible models for continuous, multivariate function approximation, particularly Gaussian process (GP) models. We propose a novel differentiable approximation to the DDM likelihood using a family of known, skewed three-parameter distributions. We then use this new likelihood to incorporate RTs into GP models for binary choices. Our RT-choice GPs enable both better latent value estimation and held-out choice prediction relative to baselines, which we demonstrate on three real-world multivariate datasets covering both human psychophysics and preference learning applications.