Conjoint psychometric field estimation for bilateral audiometry

Abstract

Behavioral testing in perceptual or cognitive domains requires querying a subject multiple times in order to quantify his or her ability in the corresponding domain. These queries must be conducted sequentially, and any additional testing domains are also typically tested sequentially, such as with distinct tests comprising a test battery. As a result, existing behavioral tests are often lengthy and do not offer comprehensive evaluation. The use of active machine-learning kernel methods for behavioral assessment provides extremely flexible yet efficient estimation tools to more thoroughly investigate perceptual or cognitive processes without incurring the penalty of excessive testing time. Audiometry represents perhaps the simplest test case to demonstrate the utility of these techniques. In pure-tone audiometry, hearing is assessed in the two-dimensional input space of frequency and intensity, and the test is repeated for both ears. Although an individual's ears are not linked physiologically, they share many features in common that lead to correlations suitable for exploitation in testing. The bilateral audiogram estimates hearing thresholds in both ears simultaneously by conjoining their separate input domains into a single search space, which can be evaluated efficiently with modern machine-learning methods. The result is the introduction of the first conjoint psychometric function estimation procedure, which consistently delivers accurate results in significantly less time than sequential disjoint estimators.

Online Machine Learning Audiometry

“…MLAG has the capability of incorporating more meaningful prior beliefs, such as the distribution of audiograms in the human population or in past measurements of a particular participant. Indeed, some of the best prior beliefs may be the audiogram of the contralateral ear or of the most recent audiogram on record (Barbour et al, 2018). In the present study, however, initial beliefs about the nature of a particular participant's audiogram were primed only by sampling with a single initial random tone.…”

Precision clinical trials: a framework for getting to precision medicine for neurobehavioural disorders

“…Bayesian estimators are particularly efficient for such complex problems, and a novel set of Bayesian estimators has been developed for this purpose. 80 , 123 These algorithms converge quickly to accurate, predictive behavioural models, telling us what we can expect of individuals rather than what we should expect of the group overall. Further, these novel frameworks can learn from new information, because they use an estimation process that improves automatically as data accumulate in individuals.…”

“… 33 New inferential methods allowing nomothetic findings to serve as prior beliefs for optimal idiographic search procedures provide a potential path forward. 80 We recommend that at minimum, researchers use intuitive treatment adaptations in PCTs, using the growing knowledge on contextual adaptations from implementation science and prospectively testing the benefits of adaptation strategies (e.g., preference-based v. outcome-based) to advance our fundamental knowledge of the most robust strategies.…”

Dynamically Masked Audiograms With Machine Learning Audiometry

“…Unmasked AMLAG includes the ability to estimate the hearing thresholds of both ears simultaneously through bilateral testing (Heisey et al 2018;Barbour et al 2019a). Adding air conduction masking to this procedure is straightforward.…”