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Cross-modality matching and the loudness growth function for click stimuli
Y C Serpanos1, H O'Malley, J S Gravel
1Adelphi University, Department of Communication Sciences and Disorders, Hy Weinberg Center, Garden City, New York 11530, USA.
The Journal of the Acoustical Society of America
|February 28, 1998
Summary
Cross-modality matching (CMM) effectively measures loudness growth in individuals with hearing loss. This method, using perceived line length, showed less variability than traditional techniques for assessing hearing impairments.
Area of Science:
- Audiology
- Psychoacoustics
- Hearing Science
Background:
- Understanding loudness perception is crucial for audiological assessment.
- High-frequency cochlear hearing loss affects loudness perception.
- Cross-modality matching (CMM) offers a novel approach to measure loudness growth.
Purpose of the Study:
- To investigate loudness-intensity functions for click stimuli in adults with normal, flat, or sloping high-frequency hearing loss.
- To validate the CMM procedure for assessing loudness growth functions.
- To compare CMM with traditional psychophysical methods in hearing-impaired listeners.
Main Methods:
- Obtained loudness-intensity functions using click stimuli in 30 adult listeners.
- Employed cross-modality matching (CMM) between loudness and perceived line length.
- Compared CMM results with magnitude estimation and production for listeners with cochlear hearing loss.
Main Results:
- Mean group loudness exponents for clicks were comparable to those from pure-tone stimuli studies.
- The loudness function for clicks was similar to tonal stimuli functions in listeners with moderate or better hearing.
- CMM demonstrated reduced variability compared to magnitude estimation and production in hearing-impaired groups.
Conclusions:
- CMM is a valid and reliable method for estimating loudness growth functions.
- CMM is particularly advantageous for assessing loudness in individuals with cochlear hearing loss.
- Individual loudness growth functions should be compared to normative data for accurate deviation analysis.