MEJORAS A LA BALANZA.

A1.1. Numerical Estimates for 1D Psychometric Functions

Figures A1.1-A1.3 show plots of error in 50% point and 25-75% interquartile range (IQR) for

unidimensional PFs estimated using probit regression and probabilistic classification. 50% point is the numerical equivalent to α, while the IQR is a numerical analog for β. Figure A1.1 shows 50% point and IQR error averaged across all conditions; Figure A1.2 shows 50% point and IQR error for various true values of β, and Figure A1.3 shows 50% point and IQR error for various distributions of number of intensities sampled/number of samples per intensity. These figures correspond to the plots for parametric estimates in Figures 4.3, 4.4, and 4.5, respectively.

Trends in each plot for numerical estimates are identical to those in the corresponding plot for parametric estimates from Section 4.3.1. As with the parametric estimates for α and β, there is no appreciable difference in performance between PR and GPC.

Figure A1.1: Error in 1D 50% point and IQR estimates for PR and GPC across all conditions. Absolute error in estimates of (A) 50% point and (B) IQR as a function of number of observed samples in the unidimensional case.

Blue solid and red dashed lines denote mean absolute errors of the PR and GPC estimates, respectively, and the matching shaded regions designate 1 standard deviation above and below the mean.

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Figure A1.2: Error in 1D PR and GPC 50% point and IQR estimates for different β values. Absolute error in estimates of (A-G) 50% point and (H-N) IQR as a function of number of observed samples for unidimensional PFs.

Blue solid and red dashed lines denote mean absolute errors of the PR and GPC estimates, respectively, and the matching shaded region designates 1 standard deviation above and below the mean. Each subplot corresponds to a

distinct β value.

Figure A1.3: Error in 1D PR and GPC 50% point and IQR estimates by sample split. Absolute error in estimates of (A-E) 50% point and (F-J) IQR as a function of number of observed samples for unidimensional PFs. Blue solid and

red dashed lines denote mean absolute errors of the PR and GPC estimates, respectively, and the matching shaded region designates 1 standard deviation above and below the mean. Each subplot corresponds to a distinct condition

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A2.1. Numerical Estimates for 2D Psychometric Functions

Tables A1.1-A1.3 show tables of error in 50% point (equivalent to α) and 25-75% IQR (analog

for β) for multidimensional PFs estimated using Gaussian process classification. Figure A1.1 shows 50% point and IQR error for various total sample numbers; Table A1.2 shows 50% point and IQR error for various true values of β, and Table A1.3 shows 50% point and IQR error for various audiogram phenotypes (Dubno et al., 2013). These figures correspond to the tables for parametric estimates in Tables 4.1, 4.2, and 4.3, respectively.

Trends in each table for numerical estimates are identical to those in the corresponding table for parametric estimates from Section 4.3.2.

Number of samples 20 50 100 200 500 1000

Mean 50% point abs. error (dB) 6.71 4.29 3.04 2.08 1.30 0.937 Std. 50% point abs. error (dB) 6.31 5.32 2.82 1.74 1.23 0.915 Mean IQR absolute error (dB) 4.29 3.52 1.97 1.74 0.867 0.548 Std. IQR absolute error (dB) 3.55 3.20 2.22 1.59 0.763 0.461

Table A1.1: Absolute errors of 2D GP 50% point and IQR estimates by number of samples. Accuracy and reliability are quantified using mean and standard deviation, respectively.

β (dB/%) 0.2 0.5 1 2 5 10

Mean 50% point abs. error (dB) 1.54 1.60 1.70 1.86 2.47 3.32 Std. 50% point abs. error (dB) 2.03 1.09 1.17 1.32 1.90 2.60 Mean IQR absolute error (dB) 1.26 1.10 1.44 1.97 2.38 2.29 Std. IQR absolute error (dB) 1.43 1.52 1.39 1.18 1.80 1.69

Table A1.2: Absolute errors of 2D GP 50% point and IQR estimates by value of β. Accuracy and reliability are quantified using mean and standard deviation, respectively. Sample number is fixed at 200 samples.

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Audiogram Phenotype Older Metabolic Sensory Metabolic + Sensory

Mean 50% point abs. error (dB) 2.19 1.69 2.23 2.22

Std. 50% point abs. error (dB) 1.68 1.47 1.96 1.74

Mean IQR absolute error (dB) 1.40 1.33 3.03 1.20

Std. IQR absolute error (dB) 1.53 1.19 1.52 1.35

Table A1.3: Absolute errors of 2D GP 50% point and IQR estimates by phenotype. Accuracy and reliability are quantified using mean and standard deviation, respectively. Values are averaged across all β values, and sample

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Curriculum Vitae

Education

Washington University in St. Louis, St. Louis, MO 2011-2017 PhD, Biomedical Engineering, May 2017

M.S. Biomedical Engineering, May 2014

Duke University, Durham, NC 2007-2011

B.S. Biomedical Engineering, May 2011 B.A. Music, May 2011

Research Experience

Laboratory of Sensory Neuroscience and Neuroengineering, Washington Univ. 2012-2017

Doctoral Student, Biomedical Engineering; Mentor: Dennis Barbour

• Developed novel machine learning technique for more efficient auditory testing and tested this technique in human subjects, finding that this technique can significantly reduce the number of trials needed to estimate a standard pure-tone threshold audiogram • Demonstrated the ability of machine-learning algorithm to accurately and efficiently

estimate nonparametric multidimensional psychometric functions

• Designed, developed, and programmed video games for remote diagnosis of auditory disorders as well as subsequent training to improve function

• Maintained collaborations with other researchers in the Departments of Computer Science, Psychology and Audiology & Communication Sciences

• Pioneered new research direction in laboratory and secured over $100,000 in funding

Laboratory of Neural Computation and Motor Behavior, Washington Univ. 2011-2012

Rotation Student, Biomedical Engineering; Mentor: Kurt Thoroughman

• Investigated electrocorticography (ECoG) signals as an indicator of motor adaptation • Obtained and performed spectral analysis of ECoG recordings from primary motor cortex

of behaving rhesus macaque monkeys and kinematic data from a robot manipulandum

Vision and Image Processing Laboratory, Duke Univ. 2009-2011

Pratt Research Fellow, Biomedical Engineering; Mentor: Sina Farsiu

• Developed algorithms for automatic alignment of retinal optical coherence tomography (OCT) images by combining registration and segmentation techniques

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from collection of OCT slices for better diagnosis of disorders such as glaucoma • Programmed a graphical user interface for manual segmentation of optic disc and cup,

combined with automatic area calculations, for use by doctors and researchers

Duke Interactive Studio, Duke Univ. 2009-2010

Undergraduate Researcher, Music Technology; Mentor: Scott Lindroth

• Composed, designed, and implemented interactive music exhibit for Museum of Life and Science (Durham, North Carolina) Halloween program

• Wrote code for dynamic image processing and audio synthesis in Supercollider that translated user movement captured by mounted cameras to real-time changes in music

Teaching Experience

Biological Neural Computation Minicourse Instructor, Washington Univ. 2015-2017 • Developed and taught intensive 1-week MATLAB courses for new programmers

Quantitative Physiology Teaching Assistant, Washington Univ. 2013-2014 • Managed laboratory of biomedical engineering class; held office hours for assignments

Cognitive, Computational & Systems Neuroscience Outreach Program 2012-2013 • Presented auditory neuroscience demonstrations to the public at St. Louis Science Center

Peer Tutoring Program, Duke Univ. 2009-2011 • Held individual tutoring sessions for Duke University students in undergraduate physics

Awards & Honors

Center for Integration of Medicine and Innovative Technology Primary Healthcare Prize

2014-2015

• Annual national award granted to proposed collaborative research projects that

demonstrate high potential for improving patient care in the primary healthcare setting, with emphasis on novel ideas and technologies that may benefit several disciplines • Second place, 2015: $110,000 awarded

• Finalist (top 10), 2014: $10,000 awarded

Cognitive, Computational & Systems Neuroscience Fellowship 2012-2014 • Full tuition and fellowship awarded to doctoral candidates in the brain-related sciences

who demonstrate the capacity to develop a project that uniquely approaches a particular research question utilizing tools and concepts from across neuro-related disciplines • Completed required curricular pathway including coursework from disciplines along with

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Pratt Undergraduate Research Fellowship 2009-2011 • Competitive stipend fellowship awarded to high-achieving Duke University

undergraduate students to perform intensive research in their engineering major in collaboration with a faculty member

Publications

Song X.D., Sukesan K.A., D.L. Barbour. “Active probabilistic classification for psychometric

field estimation.” Attention, Perception & Psychophysics (in submission).

Song X.D., Garnett R., D.L. Barbour. “Psychometric function estimation by probabilistic

classification.” The Journal of the Acoustical Society of America 141(4), 2513-2525, 2017. Gardner J.R., Song X., Weinberger K.Q., Barbour, D., J.P. Cunningham. “Psychophysical detection testing with Bayesian active learning.” Uncertainty in Artificial Intelligence, 2015.

Song X.D., Wallace B.M., Gardner J.R., Ledbetter N.M., Weinberger K.Q., D.L. Barbour. “Fast,

continuous audiogram estimation using machine learning.” Ear and Hearing 36(6), e326-335, 2015.

Conference Presentations/Abstracts

Howard, R.T., Song, X.D., Metzger, N.M., DiLorenzo, J.C., Snyder, B.R.D., Sukesan, K.A., D.L. Barbour. “Web-Based Audiometric Threshold Estimation.” American Auditory Society, Poster No. 18, Scottsdale, AZ, 2017.

Barbour D.L., X.D. Song. “The Development of Machine Learning Audiometry.” Association for Research in Otolaryngology, Poster No. PS-320, Baltimore, MD, 2017.

Song X.D., Sun W., D.L. Barbour. “Rapid estimation of neuronal frequency response area using

Gaussian process regression.” Society for Neuroscience, Poster No. 231.20, Chicago, IL, 2015. Barbour D.L., Wallace B.M., Gardner J.R., Ledbetter N.M., Weinberger K.Q., X.D. Song. “Optimizing pure-tone audiometry using Gaussian processes.” Association for Research in Otolaryngology, Poster No. PS-171, Baltimore, MD, 2015.

Song X.D., Ledbetter N.M., D.L. Barbour. “A platform for automated diagnosis of speech and

hearing disorders.” IEEE Engineering in Medicine and Biology Society, Chicago, IL, 2014.

Song X.D., Ledbetter N.M., Sommers M.S., Tye-Murray N, D.L. Barbour. “Auditory games as a

novel tool for aural rehabilitation.” Association for Research in Otolaryngology, Poster No. PS- 516, San Diego, CA, 2014.

Song X.D., Ledbetter N.M., Sommers M.S., Tye-Murray N, D.L. Barbour. “Training the brain

with auditory games.” Entertainment Software and Cognitive Neurotherapeutics Society, Poster No. A19, Los Angeles, CA, 2013.

Song X., Estrada R., Chiu S.J., Dhalla A.H., Toth C.A., Izatt J.A., S. Farsiu. “Segmentation-

based registration of retinal optical coherence tomography images with pathology.” Association for Research in Vision and Ophthalmology, Program No. 1309, Fort Lauderdale, FL, 2011.