  A classic definition of multisensory integration (MI) has been proposed as
``the presence of a (statistically) significant change in the response to a
cross-modal stimulus complex compared to unimodal stimuli''. However, this
general definition did not result in a broad consensus on how to quantify the
amount of MI in the context of reaction time (RT). In this brief note, we argue
that numeric measures of reaction times that only involve mean or median RTs do
not uncover the information required to fully assess the effect of multisensory
integration. We suggest instead novel measures that include the entire RT
distributions functions. The central role is played by relative entropy (aka
Kullback-Leibler divergence), a statistical concept in information theory,
statistics, and machine learning to measure the (non-symmetric) distance
between probability distributions. We provide a number of theoretical examples,
but empirical applications and statistical testing are postponed to later
study.
