Our dataset consists of $500$ rounds with $N_{\mbox{\tiny{y}}}$ ranging from $N_{\mbox{\tiny{y}}}=1$, which means that the target does not have a neighborhood, to $N_{\mbox{\tiny{y}}}=13$. A distribution of the number of rounds for each value of $N_{\mbox{\tiny{y}}}$ is shown in the Supplementary Table I. We first measure the mean number of clicks needed to find the target as a function of the lateral length of its yellow neighborhood (black squares in Fig. \[fig:blindjumps\]a).

Due to the design of the experiments, there is a tradeoff between finding the yellow region and finding the target inside it. Larger neighborhoods are easier to locate but make the final detection of the target inside them harder. Smaller neighborhoods, however, need on average more steps to be found but make the target within them easier to locate (Fig. \[fig:blindjumps\]b). According to our results, this tradeoff is balanced at intermediate sizes of the neighborhood, $N_{\mbox{\tiny{y}}}^{\mbox{\tiny{opt}}}=5$. This resembles the foraging dynamics of animals that exchange information about food location with their conspecifics, so that both spreading information over distances that are either too large or too short may slow down the search [@Martinez-Garcia2013b]. Following this analogy, we refer to the the size of the yellow area that minimizes the 