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

Andrea C. Trevino, Walt Jesteadt, Stephen T Neely

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)155-164
Number of pages10
JournalAdvances in experimental medicine and biology
Volume894
DOIs
StatePublished - Jan 1 2016

Fingerprint

Loudness Perception
Psychometrics
Audition
Auditory Pathways
Maximum likelihood estimation
Entropy
Hearing Loss
Hearing

Keywords

  • Categorical
  • Hearing loss
  • Loudness
  • Maximum likelihood
  • Modeling
  • Normal hearing
  • Perception
  • Probability
  • Psychoacoustics
  • Suprathreshold

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

Cite this

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title = "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.",
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N2 - 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.

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