Estimation of perceptual scales using ordinal embedding

TL;DR

This paper introduces ordinal embedding methods to estimate perceptual scales from relative judgments, enabling more flexible and accurate measurement of sensation, including non-monotonous and multi-dimensional scales, with demonstrated effectiveness through simulations and real data.

Contribution

It applies machine learning ordinal embedding techniques to psychophysics, allowing estimation of complex perceptual scales from limited relative judgments, surpassing traditional methods.

Findings

Ordinal embedding performs well with few judgments.

It can estimate non-monotonous scales.

It effectively captures multi-dimensional perceptual scales.

Abstract

In this paper, we address the problem of measuring and analysing sensation, the subjective magnitude of one's experience. We do this in the context of the method of triads: the sensation of the stimulus is evaluated via relative judgments of the form: "Is stimulus S_i more similar to stimulus S_j or to stimulus S_k?". We propose to use ordinal embedding methods from machine learning to estimate the scaling function from the relative judgments. We review two relevant and well-known methods in psychophysics which are partially applicable in our setting: non-metric multi-dimensional scaling (NMDS) and the method of maximum likelihood difference scaling (MLDS). We perform an extensive set of simulations, considering various scaling functions, to demonstrate the performance of the ordinal embedding methods. We show that in contrast to existing approaches our ordinal embedding approach allows, first, to obtain reasonable scaling function from comparatively few relative judgments, second, the estimation of non-monotonous scaling functions, and, third, multi-dimensional perceptual scales. In addition to the simulations, we analyse data from two real psychophysics experiments using ordinal embedding methods. Our results show that in the one-dimensional, monotonically increasing perceptual scale our ordinal embedding approach works as well as MLDS, while in higher dimensions, only our ordinal embedding methods can produce a desirable scaling function. To make our methods widely accessible, we provide an R-implementation and general rules of thumb on how to use ordinal embedding in the context of psychophysics.