JOURNAL ARTICLE

Sample discrimination of frequency by hearing-impaired and normal-hearing listeners

Joshua M. AlexanderRobert A. Lutfi

Year: 2008 Journal:   The Journal of the Acoustical Society of America Vol: 123 (1)Pages: 241-253   Publisher: Acoustical Society of America

Abstract

In a multiple observation, sample discrimination experiment normal-hearing (NH) and hearing-impaired (HI) listeners heard two multitone complexes each consisting of six simultaneous tones with nominal frequencies spaced evenly on an ERBN logarithmic scale between 257 and 6930Hz. On every trial, the frequency of each tone was sampled from a normal distribution centered near its nominal frequency. In one interval of a 2IFC task, all tones were sampled from distributions lower in mean frequency and in the other interval from distributions higher in mean frequency. Listeners had to identify the latter interval. Decision weights were obtained from multiple regression analysis of the between- interval frequency differences for each tone and listeners’ responses. Frequency difference limens (an index of sensorineural resolution) and decision weights for each tone were used to predict the sensitivity of different decision-theoretic models. Results indicate that low-frequency tones were given much greater perceptual weight than high-frequency tones by both groups of listeners. This tendency increased as hearing loss increased and as sensorineural resolution decreased, resulting in significantly less efficient weighting strategies for the HI listeners. Overall, results indicate that HI listeners integrated frequency information less optimally than NH listeners, even after accounting for differences in sensorineural resolution.

Keywords:
Audiology Mathematics Weighting Hearing impaired Tone (literature) Sensorineural hearing loss Acoustics Interval (graph theory) Logarithm Pitch perception Statistics Perception Hearing loss Psychology Medicine Physics Mathematical analysis

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Citation History

Topics

Hearing Loss and Rehabilitation
Life Sciences →  Neuroscience →  Cognitive Neuroscience
Noise Effects and Management
Health Sciences →  Health Professions →  Speech and Hearing
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing

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