  Modelling human function learning has been the subject of in-tense research
in cognitive sciences. The topic is relevant in black-box optimization where
information about the objective and/or constraints is not available and must be
learned through function evaluations. In this paper we focus on the relation
between the behaviour of humans searching for the maximum and the probabilistic
model used in Bayesian Optimization. As surrogate models of the unknown
function both Gaussian Processes and Random Forest have been considered: the
Bayesian learning paradigm is central in the development of active learning
approaches balancing exploration/exploitation in uncertain conditions towards
effective generalization in large decision spaces. In this paper we analyse
experimentally how Bayesian Optimization compares to humans searching for the
maximum of an unknown 2D function. A set of controlled experiments with 60
subjects, using both surrogate models, confirm that Bayesian Optimization
provides a general model to represent individual patterns of active learning in
humans
