A likelihood-based method for point and interval estimation on auditory filter data

Abstract

A novel method for determining auditory filter (AF) shapes given a set of n-alternative forced choice (nAFC) responses from a single human subject or set of subjects is discussed. The method works by developing a function which maps individual nAFC responses into likelihood values—either supporting or conflicting with a proposed model AF shape. The aggregate of these likelihoods is then used as an objective function for optimization schemes for point estimation, or as the basis function for Metropolis Hastings-like algorithms for interval estimation, both of either parameters of the AF model or of the entire AF shape. The method is demonstrated on simulated up-down staircase data. The consistency of the method is discussed in the context of canonical methods for AF data analysis, some of which are shown to produce systematic errors. Other possible benefits of this approach are discussed including the ability of the method to: combine data from heterogeneous nAFC tasks (e.g., notched-noise maskers with ton...