Response Time Improves Gaussian Process Models for Perception and Preferences

Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen L Keeley

Keywords: Gaussian process, preference learning, diffusion decision model

TL;DR: Combining response times with binary choice data improves both latent function estimation and choice prediction in Gaussian process models of preference and perception.

Abstract: Models for human choice prediction in preference learning and perception science often use binary response data, requiring many samples to accurately learn latent utilities or perceptual intensities. The response time (RT) to make each choice captures additional information about the decision process, but existing models incorporating RTs for choice prediction do so in a fully parametric way or over discrete inputs. At the same time, state-of-the-art Gaussian process (GP) models of perception and preferences operate on choices only, ignoring RTs. We propose two approaches for incorporating RTs into GP preference and perception models. The first is based on stacking GP models, and the second uses a novel differentiable approximation to the likelihood of the diffusion decision model (DDM), the de-facto standard model for choice RTs. Our RT-choice GPs enable 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.

List Of Authors: Shvartsman, Michael and Letham, Benjamin and Bakshy, Eytan and Keeley, Stephen

Code Url: https://github.com/facebookresearch/response-time-gps

Submission Number: 569