route for the study of search problems.

In the following section, after presenting the characteristics of the game, we show the empirical results obtained from two different setups. In the first case, players have no information about the configuration of the board, whereas in the second study they are provided with partial information about it. Then, we formulate a family of models that capture the main mechanisms behind the experimental results and derive analytical approximations to show the robustness of the results. Finally, all the previous steps are combined to develop a comprehensive framework in which it is possible to predict the optimal configuration of the landscape that yields faster searches. The paper finishes with a discussion of the results and opportunities for new lines of research.

Results {#results .unnumbered}
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Experimental setup {#experimental-setup .unnumbered}
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We consider a simple game in which a single target has to be found. It slightly resembles the classic [*minesweeper*]{}, although the objective is to find a unique target ([*mine*]{}) instead of avoiding a collection of them. The interface consists of $N\times N$ squares that can be explored by the player through successive clicks with the mouse. There are three classes of cells depending 