nature of the informed search process as the player uses all the previous steps to discard cells that have not been visited yet and results in self-adaptive strategies that evolve towards displacements of length $N_{\mbox{\tiny{y}}}$. Also, as the value of $N_{\mbox{\tiny{y}}}$ is known, the number of exits from the neighborhood of the target diminishes (Fig. \[split\]). In a first approach to model this effect, we modify the model used for blind searches using the new experimental distribution of the length of the displacements in both the extensive and the intensive modes (Fig. \[fig3\]a). Therefore, instead of using the exponential distributions of Fig. \[fig:blindexp\] we sample the histograms of Fig. \[fig3\]a (red circles) and its inset, that are obtained from experimental searches with initial information. This approach overestimates both the length of the extensive and the intensive phases, which results in a clearly higher average number of movements; $33.50$ jumps, $\sigma=19.00$ for the model and $25.50$ jumps, $\sigma=10.50$ in the data (MB green bars and DI gray bars in Fig. \[fig-comp\] respectively). This is due to the fact that the model does not integrate the information about the size of the target to a priori discard some of the cells during 