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Published on: January 27, 2018
How to define thresholds for level and interaural-level-difference discrimination: Insights from scedasticities and
1Department of Systems Physiology of Learning, Leibniz Institute for Neurobiology, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany.
This study found that expressing auditory discrimination thresholds as the logarithm of the Weber fraction for stimulus amplitude, and averaging them arithmetically, best suits statistical analysis. This method ensures greater consistency and normal distribution for auditory perception research.
Area of Science:
- Auditory perception
- Psychoacoustics
- Signal detection theory
Background:
- Sensitivity to sound level and interaural level difference (ILD) is crucial for hearing.
- Various methods exist for defining and averaging auditory thresholds, but their statistical suitability is unclear.
Purpose of the Study:
- To determine the most statistically suitable definition and averaging method for auditory discrimination thresholds.
- To assess homoscedasticity and normality of different threshold definitions.
Main Methods:
- Measured thresholds in human listeners across six conditions using an adaptive two-alternative forced-choice paradigm.
- Investigated thresholds defined as log-ratios, log-Weber fractions for intensity, and log-Weber fractions for amplitude.
- Analyzed thresholds for homoscedasticity and adherence to normal distribution.
Main Results:
- Log-ratio thresholds were heteroscedastic; log-transformation did not improve this.
- Log-Weber fraction thresholds showed better homoscedasticity.
- Log-Weber fraction for stimulus amplitude best approximated homoscedasticity and normal distribution.
Conclusions:
- Auditory discrimination thresholds should be defined as the logarithm of the Weber fraction for stimulus amplitude.
- Arithmetic averaging of these thresholds across listeners is recommended for statistical analysis.
- This approach enhances the reliability and interpretability of auditory sensitivity data.
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