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Bayesian adaptive procedures accurately estimate loudness growth and equal-loudness contours in listeners with normal and impaired hearing. These methods provide reliable audiological data for diverse hearing conditions.

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Area of Science:

  • Audiology
  • Psychoacoustics
  • Computational statistics

Background:

  • Estimating loudness growth is crucial for understanding auditory perception.
  • Accurate loudness scaling across frequencies is challenging, especially for individuals with hearing loss.
  • Bayesian adaptive procedures offer a potential solution for efficient data collection.

Purpose of the Study:

  • To develop and compare Bayesian adaptive procedures for estimating loudness growth.
  • To evaluate the performance of different models in guiding stimulus selection.
  • To assess the accuracy of these procedures for listeners with normal and sensorineural hearing loss.

Main Methods:

  • Development of Bayesian adaptive procedures for categorical loudness scaling.
  • Simulation experiments using multinomial psychometric functions across ten frequencies.
  • Comparison of procedures using phenomenological and data-driven category-boundary models.
  • Inclusion of a non-Bayesian adaptive procedure for baseline comparison.

Main Results:

  • All adaptive procedures yielded convergent estimates of loudness category boundaries and equal-loudness contours (250-8000 Hz).
  • Data-driven model fitting demonstrated satisfactory accuracy.
  • Procedures achieved root-mean-square errors under 6 dB for equal-loudness-level contours (20-100 phons) within 100 trials.

Conclusions:

  • Bayesian adaptive procedures are effective for estimating loudness growth across a wide frequency range.
  • The choice of modeling approach and stimulus-selection rules did not significantly impact the overall accuracy.
  • These methods provide reliable audiological data for both normal-hearing and hearing-impaired individuals.