Robust and efficient Bayesian adaptive psychometric function estimation

Doire, Clement S. J.; Brookes, Mike; Naylor, Patrick A.

doi:10.1121/1.4979580

Cited by 13 publications

(9 citation statements)

References 31 publications

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“…The BALD technique has been used previously to infer multidimensional neural receptive field and fMRI structure (Park et al , 2011; Park, 2013) as well as audiogram thresholds (Gardner et al , 2015b), which is extended here to infer the entire PF. Furthermore, posterior entropy minimization techniques have been used to estimate thresholds and spreads for parametric uni- and multidimensional psychometric functions (Kontsevich and Tyler, 1999; DiMattina, 2015; Doire et al , 2017; Watson, 2017). The current work applies this sampling framework for estimation of nonparametric PFs.…”

Section: Methodsmentioning

confidence: 99%

Bayesian active probabilistic classification for psychometric field estimation

Song

Sukesan

Barbour

2017

Atten Percept Psychophys

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Psychometric functions are typically estimated by fitting a parametric model to categorical subject responses. Procedures to estimate unidimensional psychometric functions (i.e., psychometric curves) have been subjected to the most research, with modern adaptive methods capable of quickly obtaining accurate estimates. These capabilities have been extended to some multidimensional psychometric functions (i.e., psychometric fields) that are easily parameterizable, but flexible procedures for general psychometric field estimation are lacking. This study introduces a nonparametric Bayesian psychometric field estimator operating on subject queries sequentially selected to improve the estimate in some targeted way. This estimator implements probabilistic classification using Gaussian processes trained by active learning. The accuracy and efficiency of two different actively sampled estimators were compared to two non-actively sampled estimators for simulations of one of the simplest psychometric fields in common use: the pure-tone audiogram. The actively sampled methods achieved estimate accuracy equivalent to the non-actively sampled methods with fewer observations. This trend held for a variety of audiogram phenotypes representative of the range of human auditory perception. Gaussian process classification is a general estimation procedure capable of extending to multiple input variables and response classes. Its success with a two-dimensional psychometric field informed by binary subject responses holds great promise for extension to complex perceptual models currently inaccessible to practical estimation.

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Section: Methodsmentioning

confidence: 99%

Bayesian active probabilistic classification for psychometric field estimation

Song

Sukesan

Barbour

2017

Atten Percept Psychophys

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“…Equation 3 provides an efficient way of looking one step ahead. Less myopic policies that look several steps ahead (e.g., Doire et al., 2017 ) may further speed up BAL procedures, but this is usually computationally intractable when using GPs.…”

Section: Methodsmentioning

confidence: 99%

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method

2020

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Time-efficient hearing tests are important in both clinical practice and research studies. This particularly applies to notched-noise tests, which are rarely done in clinical practice because of the time required. Auditory-filter shapes derived from notched-noise data may be useful for diagnosis of the cause of hearing loss and for fitting of hearing aids, especially if measured over a wide range of center frequencies. To reduce the testing time, we applied Bayesian active learning (BAL) to the notched-noise test, picking the most informative stimulus parameters for each trial based on nine Gaussian Processes. A total of 11 hearing-impaired subjects were tested. In 20 to 30 min, the test provided estimates of signal threshold as a continuous function of frequency from 500 to 4000 Hz for nine notch widths and for notches placed both symmetrically and asymmetrically around the signal frequency. The thresholds were found to be consistent with those obtained using a 2-up/1-down forced-choice procedure at a single center frequency. In particular, differences in threshold between the methods did not vary with notch width. An independent second run of the BAL test for one notch width showed that it is reliable. The data derived from the BAL test were used to estimate auditory-filter width and asymmetry and detection efficiency for center frequencies from 500 to 4000 Hz. The results agreed with expectations for cochlear hearing losses that were derived from the audiogram and a hearing model.

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“…The PSE for each of the test frequencies was estimated using one experimental block. Each experimental block consisted of two randomly interleaved adaptive tracks, and each adaptive track followed the UML procedure (Shen and Richards, 2012;Hatzfeld et al, 2015;Doire et al, 2017) and was implemented using the UML toolbox for the MATLAB platform (Shen et al, 2015).…”

Section: The Iml Proceduresmentioning

confidence: 99%

Feasibility of interleaved Bayesian adaptive procedures in estimating the equal-loudness contour

Shen

Zhang

2018

The Journal of the Acoustical Society of America

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A Bayesian adaptive procedure, the interleaved-equal-loudness contour (IELC) procedure, was developed to improve the efficiency in estimating the equal-loudness contour. Experiment 1 evaluated the test-retest reliability of the IELC procedure using six naive normal-hearing listeners. Two IELC runs of 200 trials were conducted and excellent test-retest reliability was found at both the group and individual levels. Using the same group of listeners, Experiment 2 compared the IELC procedure to two other procedures that required frequency-by-frequency testing. One of these procedures was the commonly adopted interleaved staircase (ISC) procedure from Jesteadt [(1980). Atten. Percept. Psychophys. 28, 85-88]. The other procedure, the interleaved maximum-likelihood (IML) procedure, was a modification of the updated maximum-likelihood procedure [Shen and Richards (2012). J. Acoust. Soc. Am. 132, 957-967]. For each of the ISC and IML procedures, two runs of approximately 500 trials were conducted, followed by one additional IELC run. The testretest reliability of the IELC procedure was comparable to that of the ISC and IML procedure. The accuracies of all three procedures measured in Experiment 2 were similar, which was superior to the accuracies of the IELC runs from Experiment 1, indicating a potential training effect.

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Robust and efficient Bayesian adaptive psychometric function estimation

Cited by 13 publications

References 31 publications

Bayesian active probabilistic classification for psychometric field estimation

Bayesian active probabilistic classification for psychometric field estimation

Application of Bayesian Active Learning to the Estimation of Auditory Filter Shapes Using the Notched-Noise Method

Feasibility of interleaved Bayesian adaptive procedures in estimating the equal-loudness contour

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