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Inattention plays a critical role in the accuracy of threshold measurements, e.g., when using mobile devices. To describe the influence of distraction, long- and short-term inattention models based on either a stationary or a non-stationary psychometric function were developed and used to generate three simulated listeners: fully-, moderately-, and non-concentrated listeners. Six established adaptive procedures were assessed via Monte-Carlo simulations in combination with the inattention models and compared with a newly proposed method: the graded response bracketing procedure (GRaBr). Robustness was examined by bias and root mean square error between the "true" and estimated thresholds while efficiency was evaluated using rates of convergence and a normalized efficiency index. The findings show that inattention has a detrimental impact on adaptive procedure performance-especially for the short-term inattentive listener-and that several model-based procedures relying on a consistent response behavior of the listener are prone to errors owing to inattention. The model-free procedure GRaBr, on the other hand, is considerably robust and efficient in spite of the (assumed) inattention. As a result, adaptive techniques with desired properties (i.e., high robustness and efficiency) as revealed in our simulations-such as GRaBr-appear to be advantageous for mobile devices or in laboratory tests with untrained subjects., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The benefit in speech-recognition performance due to the compensation of a hearing loss can vary between listeners, even if unaided performance and hearing thresholds are similar. To accurately predict the individual performance benefit due to a specific hearing device, a prediction model is proposed which takes into account hearing thresholds and a frequency-dependent suprathreshold component of impaired hearing. To test the model, the German matrix sentence test was performed in unaided and individually aided conditions in quiet and in noise by 18 listeners with different degrees of hearing loss. The outcomes were predicted by an individualized automatic speech-recognition system where the individualization parameter for the suprathreshold component of hearing loss was inferred from tone-in-noise detection thresholds. The suprathreshold component was implemented as a frequency-dependent multiplicative noise (mimicking level uncertainty) in the feature-extraction stage of the automatic speech-recognition system. Its inclusion improved the root-mean-square prediction error of individual speech-recognition thresholds (SRTs) from 6.3 dB to 4.2 dB and of individual benefits in SRT due to common compensation strategies from 5.1 dB to 3.4 dB. The outcome predictions are highly correlated with both the corresponding observed SRTs ( R 2  = .94) and the benefits in SRT ( R 2  = .89) and hence might help to better understand-and eventually mitigate-the perceptual consequences of as yet unexplained hearing problems, also discussed in the context of hidden hearing loss.