A commonly held idea is that people engaged in guessing
tasks try to detect sequential dependencies between the
occurring events and behave accordingly. For instance,
previous accounts of the popular Rock Paper Scissors game
assume that people try to anticipate the move an opponent is
likely to make and play a move capable of beating it. In the
paper we propose that players modulate their behavior by
reacting to the effects it produces on the environment, i.e.,
that they behave exactly as they do in non competitive
situations. We present an experiment in which participants
play against a computer controlled by different algorithms and
develop a procedural model, based on the new ACT-R utility
learning mechanism, that is able to replicate the participants'
behavior in all the experimental conditions.
