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Bayesian transfer in a complex spatial localization task

R. Kiryakova,S. Aston,Ulrik R Beierholm,M. Nardini

Published 2019 in bioRxiv

ABSTRACT

Prior knowledge can help observers across a range of tasks. 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-learnt 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 (priors), using sensory and prior information. The sensory cue consisted of dots drawn from a Gaussian distribution centred 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 place significantly less weight on the cue relative to the prior 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 the ideal observer, placing too much weight on the medium and high variance likelihoods in all reported experiments. These results emphasise the limits of Bayesian models in complex tasks.

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