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. 2018 Nov 6;8:16399. doi: 10.1038/s41598-018-34512-6

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Response gain as a function of the response standard deviation. Test of the different models for (A) azimuth, and (B) elevation response components, pooled for the SNR and low-pass experiments (data from 11 subjects; N = 32 per component; one subject excluded, see Methods). (A) Azimuth responses invariably have a high gain, regardless of the stimulus conditions, and are best described by a constant gain around 0.9 (MLE). (B) The elevation responses show a fundamentally different behaviour. The data in panel B were fitted with Eqn. 9 (MAP model, red line, for which the average prediction error is zero), Eqn. 10 (AS model, blue, with standard deviation of the optimal fit in shading), and Eqn. 11 (PM model, black, shading: standard deviation). Note that all three models have the same free parameter: σ_P. The PM and AS models both outperform the MAP decision strategy by far.