Optimal models of sound localization by barn owls

摘要

Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates.udWhile the matching models can explain properties of neural responses, no model explains how the owl resolves spatial ambiguity in the localization cues to produce accurate localization for sources near the center of gaze. Here, I examine two models for the barn owl’s sound localization behavior. First, I consider a maximum likelihood estimator in order to further evaluate the cue matchingudmodel. Second, I consider a maximum a posteriori estimator to test whether a Bayesian model with a prior that emphasizes directions near the center of gazeudcan reproduce the owl’s localization behavior. I show that the maximum likelihood estimator can not reproduce the owl’s behavior, while the maximum a posterioriudestimator is able to match the behavior. This result suggests that the standard cue matching model will not be sufficient to explain sound localization behavior inudthe barn owl. The Bayesian model provides a new framework for analyzing sound localization in the barn owl and leads to predictions about the owl’s localization behavior.