Modeling the individual variability of loudness perception with a multi-category psychometric function

Abstract

Loudness is a suprathreshold percept that provides insight into the status of the entire auditory pathway. Individuals with matched thresholds can show individual variability in their loudness perception that is currently not well understood. As a means to analyze and model listener variability, we introduce the multi-category psychometric function (MCPF), a novel representation for categorical data that fully describes the probabilistic relationship between stimulus level and categoricalloudness perception. We present results based on categorical loudness scaling (CLS) data for adults with normal-hearing (NH) and hearing loss (HL). We show how the MCPF can be used to improve CLS estimates, by combining listener models with maximum-likelihood (ML) estimation. We also describe how the MCPF could be used in an entropy-based stimulus-selection technique. These techniques utilize the probabilistic nature of categorical perception, a novel usage of this dimension of loudness information, to improve the quality of loudness measurements.