Infants differentially update their internal models of a dynamic environment.

Unexpected events provide us with opportunities for learning about what to expect from the world around us. Using a saccadic-planning paradigm, we investigated whether and how infants and adults represent the statistics of a changing environment (i.e. build an internal model of the environment). Participants observed differently colored bees that appeared at an unexpected location every few trials. The color cues indicated whether the subsequent bees would appear at this new location (i.e. update trials) or at the same location as previously (i.e. no-update trials). Infants learned the predictive value of the color cues and updated their internal models when necessary. Unlike infants, adults had a tendency to update their models each time they observed a change in the structure. We argue that infants are open to learning from current evidence due to being less influenced by their prior knowledge. This is an advantageous learning strategy to form accurate representations in dynamic environments, which is fundamental for successful adaptation.