Estimating the contribution of central noise from composite performance across multiple tasks

Abstract

According to signal detection theory, the ability to detect a signal is limited only by internal noise, which comprises peripheral and central sources. Here, we develop a statistical approach to parse central from peripheral noise. Fifty-two Veterans (mean age = 47.8, range = 30–60) with normal or near-normal hearing performed AXB discrimination for several temporal processing tasks: gap duration discrimination, forward masking, frequency modulation detection, and interaural phase modulation detection. After training, a single adaptive run (40 reversals) was completed for each task. Subjects also completed speech-in-noise testing (“Theo-Victor-Michael") with four masker types (48 trials ea.): speech-shaped noise, speech-envelope modulated noise, one and two competing talkers. Composite speech performance was estimated using principal component analysis. Bayesian hierarchical regression was used to estimate two-parameter psychometric functions (threshold, slope) simultaneously for all temporal tasks and subjects. Crucially, fixed (group-level) thresholds were estimated per task but only a single random (subject-level) intercept was estimated (mean across-task deviation from the group thresholds). We assume central noise is the primary factor limiting across-task performance. The principal speech scores were entered as regressors on this “central threshold.” Indeed, central threshold was correlated with the principal speech scores, suggesting that central noise limits temporal processing and speech-in-noise.