Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design - Sanchez, Gaëtan; Lecaignard, Françoise; Otman, Anatole; Maby, Emmanuel; Mattout, Jérémie (Frontiers in Human Neuroscience, 2016)
The fairly young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we here argue that, brought together, they may trigger a paradigm shift which could prove fruitful to both endeavors. The paradigm shift we propose simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new paradigm as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g. stimuli) in the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new though is our ability to process online, the huge amount of complex data that brain imaging techniques provide. At a time where physiological and psychological processes start being approached by more realistic generative models which may be difficult to tease apart empirically. ASAP proposes a generic and principled way to optimize the experimental design adaptively. In this perspective paper, we reprise the main steps in ASAP. Using synthetic data where the true underlying model is known, we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its potential for future basic and clinical neurosciences as well as some remaining challenges.