A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks

TL;DR

This paper introduces a semi-parametric model for high-dimensional sensory discrimination that combines traditional psychometric models with Gaussian process priors, enabling efficient learning and decision-making in complex sensory spaces.

Contribution

The paper presents a novel semi-parametric approach that integrates psychophysical models with Gaussian processes to better characterize high-dimensional sensory decision tasks.

Findings

Achieves high performance with less data than baseline models

Effective in synthetic and real-world psychophysics datasets

Shows strong results in Bayesian active learning scenarios

Abstract

Psychometric functions typically characterize binary sensory decisions along a single stimulus dimension. However, real-life sensory tasks vary along a greater variety of dimensions (e.g. color, contrast and luminance for visual stimuli). Approaches to characterizing high-dimensional sensory spaces either require strong parametric assumptions about these additional contextual dimensions, or fail to leverage known properties of classical psychometric curves. We overcome both limitations by introducing a semi-parametric model of sensory discrimination that applies traditional psychophysical models along a stimulus intensity dimension, but puts Gaussian process (GP) priors on the parameters of these models with respect to the remaining dimensions. By combining the flexibility of the GP with the deep literature on parametric psychophysics, our semi-parametric models achieve good performance with much less data than baselines on both synthetic and real-world high-dimensional psychophysics datasets. We additionally show strong performance in a Bayesian active learning setting, and present a novel active learning paradigm for the semi-parametric model.