Rohde, Marieke and van Dam, Loes CJ and Ernst, Marc O (2016) 'Statistically Optimal Multisensory Cue Integration: A Practical Tutorial.' Multisensory Research, 29 (4-5). pp. 279-317. ISSN 2213-4794

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Abstract

<jats:p>Humans combine redundant multisensory estimates into a coherent multimodal percept. Experiments in cue integration have shown for many modality pairs and perceptual tasks that multisensory information is fused in a statistically optimal manner: observers take the unimodal sensory reliability into consideration when performing perceptual judgments. They combine the senses according to the rules of Maximum Likelihood Estimation to maximize overall perceptual precision. This tutorial explains in an accessible manner how to design optimal cue integration experiments and how to analyse the results from these experiments to test whether humans follow the predictions of the optimal cue integration model. The tutorial is meant for novices in multisensory integration and requires very little training in formal models and psychophysical methods. For each step in the experimental design and analysis, rules of thumb and practical examples are provided. We also publish Matlab code for an example experiment on cue integration and a Matlab toolbox for data analysis that accompanies the tutorial online. This way, readers can learn about the techniques by trying them out themselves. We hope to provide readers with the tools necessary to design their own experiments on optimal cue integration and enable them to take part in explaining when, why and how humans combine multisensory information optimally.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Multisensory integration; ideal-observer models; Bayesian models; experimental design; methods
Subjects:	B Philosophy. Psychology. Religion > BF Psychology R Medicine > RE Ophthalmology
Divisions:	Faculty of Science and Health Faculty of Science and Health > Psychology, Department of
SWORD Depositor:	Elements
Depositing User:	Elements
Date Deposited:	27 Jul 2016 13:43
Last Modified:	15 Jan 2022 00:47
URI:	http://repository.essex.ac.uk/id/eprint/17324

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