A statistical model of horizontal auditory localization performance data

Abstract

Horizontal localization experiments are used to evaluate the listener’s ability to locate the position of a sound source, and determine how signal characteristics affect this ability. These experiments generate circular, bimodal, and repeated data that are challenging to statistically analyze. A two-part mixture of wrapped Cauchys is proposed for these data, with the effects of signal type and position on localization bias, precision, and front-back confusion modeled using regression. The model is illustrated using mid- (1.0–2.0 kHz) and high- (3.0–6.0 kHz) frequency narrow band noises localization collected among ten normal hearing listeners.