Topic: serial reaction time task

Perceptuo-motor sequences that underlie our everyday skills from walking to language have higher-order dependencies such that the statistics of one sequence element depend on a variably deep window of past elements. We used a non-parametric, hierarchical, forgetful, Bayesian sequence model to characterize the multi-day evolution of human participants’ implicit representation of a serial reaction time task sequence with higher-order dependencies. The model updates trial-by-trial, and seamlessly combines predictive information from shorter and longer windows onto past events, weighting the windo