  In this article we address two related issues on the learning of
probabilistic sequences of events. First, which features make the sequence of
events generated by a stochastic chain more difficult to predict. Second, how
to model the procedures employed by different learners to identify the
structure of sequences of events. Playing the role of a goalkeeper in a video
game, participants were told to predict step by step the successive directions
-- left, center or right -- to which the penalty kicker would send the ball.
The sequence of kicks was driven by a stochastic chain with memory of variable
length. Results showed that at least three features play a role in the first
issue: 1) the shape of the context tree summarizing the dependencies between
present and past directions; 2) the entropy of the stochastic chain used to
generate the sequences of events; 3) the existence or not of a deterministic
periodic sequence underlying the sequences of events. Moreover, evidence
suggests that best learners rely less on their own past choices to identify the
structure of the sequences of events.
