Statistically optimal models of cue integration have been immensely influential in multisensory research over the past two decades. Our laboratory here in Bielefeld is one of the centers of this line of research, led by our head of Department, Marc Ernst. As such, we are often asked to review manuscripts on optimal integration studies, including manuscripts from researchers who are new to the field and who are unexperienced with psychophysical methods and (Bayesian) computational modeling approaches. Unfortunately, such first contributions often use experimental paradigms and methods that are problematic and in some cases even render the results effectively meaningless.
For this reason, Loes van Dam, Marc Ernst and I have written an accessible and practical tutorial, a kind of MLE for dummies paper that points out the most important issues to consider when designing experimental to test for optimality of cue integration according to Maximum Likelihood Estimation (MLE). It is targeted at novice researchers, new graduate students entering the field and researchers from neighbouring disciplines (neural imaging, clinical neuroscience, etc.). It can also be used for classroom teaching. As a supplement to this tutorial, Loes and I have also developed a Matlab toolbox for data analysis and an example experiment coded in Matlab (part of the toolbox). With this toolbox, novices can take their first step by trying out MLE optimal integration first hand and play with the parameters described in the tutorial. Also experienced researcher can use the toolbox to analyze data from cue integration experiments. Enjoy!
- Rohde, M., van Dam, L.C.J. & Ernst, M.O. (2015) Statistically Optimal Multisensory Cue Integration: A Practical Tutorial. Multisensory Research DOI: 10.1163/22134808-00002510.
- van Dam, L.C.J., & Rohde, M. (2015) Maximum Likelihood Multisensory Integration Toolbox, Mathworks file exchange ID 50514.
If you don’t have a subscription to Multisensory Research, you can also access a copy of the full text on Researchgate or email me.
Optimal integration of redundant cues (unimodal and optimal bimodal Likelihood functions)