Bayesian Mixed Multidimensional Scaling for Auditory Processing

TL;DR

This paper introduces a Bayesian mixed MDS approach to model individual and group differences in auditory perception, revealing interpretable latent features that relate to speech sound processing.

Contribution

The novel Bayesian mixed MDS method captures heterogeneity and recovers meaningful latent features in auditory perception data, advancing analysis of speech sound processing.

Findings

Latent features correlate with native and non-native language backgrounds.

Method accurately reconstructs observed perceptual distances.

Reveals individual differences in auditory feature representations.

Abstract

The human brain distinguishes speech sounds by mapping acoustic signals into a latent perceptual space. This space can be estimated via multidimensional scaling (MDS), preserving the similarity structure in lower dimensions. However, individual and group-level heterogeneity, especially between native and non-native listeners, remains poorly understood. Prior approaches often ignore such variability or cannot capture shared structure, limiting principled comparison. Moreover, the literature typically focuses on latent distances rather than the underlying features themselves. To address these issues, we develop a Bayesian mixed MDS method that accounts for both subject- and group-level heterogeneity, enabling recovery of biologically interpretable latent features. Simulations and an auditory neuroscience application demonstrate how these features reconstruct observed distances and vary with individual and language background, revealing novel insights.