





























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 comparisons. Moreover, the literature often 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, allows for the recovery of unique, identifiable latent features, facilitating their biological interpretability, while also determining the effective dimensionality of the latent space in an automated, data-adaptive manner. Simulations and an auditory neuroscience application demonstrate how these features reconstruct observed distances and vary with individual and language background, revealing novel insights.
From: Giovanni Rebaudo [view email]
[v1]
Wed, 31 Aug 2022 20:36:31 UTC (6,293 KB)
[v2]
Fri, 1 Dec 2023 13:19:16 UTC (4,936 KB)
[v3]
Mon, 28 Jul 2025 07:44:47 UTC (4,871 KB)
[v4]
Mon, 1 Jun 2026 15:36:21 UTC (4,902 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。