  While perceptual decision making in humans is often considered to be governed
by evidence accumulator models (like drift-diffusion), mechanisms driving
harder situations where prospection of future scenarios is necessary remain
largely unknown. Here, experimental and computational evidence is given in
favour of a mechanism in which prospection of possible future payoffs
associated to each available choice could be used, through the internal
estimation of the corresponding Shannon's entropy $S$. So, the decision would
be triggered as soon as $S$ reaches a threshold which ensures that a choice is
reliable enough. We illustrate this idea using a task in which subjects have to
navigate sequentially through a maze on the computer screen while avoiding
trajectory overlaps, forcing them to use memory and prospection skills that we
indirectly capture through eye-tracking. Comparison of the experimental data to
that from virtual (ideal) subjects allows us to verify that the performances
observed, as well as the distribution of decision-making times, of humans are
compatible with the aforementioned mechanism.
