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Abstract

Long perceptuo-motor sequences underlie skills from walking to language learning, and are often learned gradually and
unconsciously in the face of noise. We used a non-parametric Bayesian n-gram model (Teh, 2006) to characterize the
multi-day evolution of human subjects’ implicit representation of a serial reaction time task sequence with second-order
contingencies. The reaction time for an element in the sequence depended on zero, one and more preceding elements
at the same time, predicting frequency, repetition and higher-order learning effects. Our trial-level dynamic model
captured these coexistent facilitation effects by seamlessly combining information from shorter and longer windows
onto past events. We show how shifting their priors over window lengths allowed subjects to grow and refine their
internal sequence representations week by week.