We assess whether a cardinal model can be used to relate neural observables to stochastic choice behaviour. We develop a general empirical framework for relating any neural observable to choice prediction, and propose a means of bench-marking their predictive power. In a previous study,...

We propose an accurate data-driven numerical scheme to solve stochastic differential equations (SDEs), by taking large time steps. The SDE discretization is built up by means of the polynomial chaos expansion method, on the basis of accurately determined stochastic collocation (SC) points. By...