Optimal models of sound localization by barn owls
- Creators
- Fischer, Brian J.
Abstract
Sound localization by barn owls is commonly modeled as a matching procedure where localization cues derived from auditory inputs are compared to stored templates. While 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 matching model. Second, I consider a maximum a posteriori estimator to test whether a Bayesian model with a prior that emphasizes directions near the center of gaze can reproduce the owl's localization behavior. I show that the maximum likelihood estimator can not reproduce the owl's behavior, while the maximum a posteriori estimator is able to match the behavior. This result suggests that the standard cue matching model will not be sufficient to explain sound localization behavior in the 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.
Additional Information
© 2009 Neural Information Processing Systems Foundation. I thank Kip Keller, Klaus Hartung, and Terry Takahashi for providing the head-related transfer functions and Mark Konishi and José Luis Peña for comments and support.Attached Files
Published - 3244-optimal-models-of-sound-localization-by-barn-owls.pdf
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Additional details
- Eprint ID
- 65750
- Resolver ID
- CaltechAUTHORS:20160329-153442103
- Created
-
2016-03-30Created from EPrint's datestamp field
- Updated
-
2019-10-03Created from EPrint's last_modified field
- Series Name
- Advances in Neural Information Processing Systems
- Series Volume or Issue Number
- 20