Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Bayesian transfer in a complex spatial localization task.

Kiryakova, R. and Aston, S. and Beierholm, U. and Nardini, M . (2020) 'Bayesian transfer in a complex spatial localization task.', Journal of vision., 20 (6). p. 17.

Abstract

Prior knowledge can help observers in various situations. Adults can simultaneously learn two location priors and integrate these with sensory information to locate hidden objects. Importantly, observers weight prior and sensory (likelihood) information differently depending on their respective reliabilities, in line with principles of Bayesian inference. Yet, there is limited evidence that observers actually perform Bayesian inference, rather than a heuristic, such as forming a look-up table. To distinguish these possibilities, we ask whether previously learned priors will be immediately integrated with a new, untrained likelihood. If observers use Bayesian principles, they should immediately put less weight on the new, less reliable, likelihood (“Bayesian transfer”). In an initial experiment, observers estimated the position of a hidden target, drawn from one of two distinct distributions, using sensory and prior information. The sensory cue consisted of dots drawn from a Gaussian distribution centered on the true location with either low, medium, or high variance; the latter introduced after block three of five to test for evidence of Bayesian transfer. Observers did not weight the cue (relative to the prior) significantly less in the high compared to medium variance condition, counter to Bayesian predictions. However, when explicitly informed of the different prior variabilities, observers placed less weight on the new high variance likelihood (“Bayesian transfer”), yet, substantially diverged from ideal. Much of this divergence can be captured by a model that weights sensory information, according only to internal noise in using the cue. These results emphasize the limits of Bayesian models in complex tasks.

Item Type:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution.
Download PDF
(1054Kb)
Full text:Publisher-imposed embargo
(AM) Accepted Manuscript
File format - PDF
(878Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1167/jov.20.6.17
Publisher statement:Copyright 2020 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.
Date accepted:17 April 2020
Date deposited:12 May 2020
Date of first online publication:24 June 2020
Date first made open access:30 June 2020

Save or Share this output

Export:
Export
Look up in GoogleScholar