Two experiments demonstrated that the prior predictive history of a cue governs the extent to which that cue engages in sequence learning. Using a serial reaction time task, we manipulated the predictiveness of the stimulus locations (cues) with respect to the location of the stimulus on the next trial (outcome), such that half of the cues were good predictors of their outcomes, whilst the other half were poorer predictors. Following this, all cues were then paired with novel outcomes. Learning about those cues that were previously established as good predictors proceeded more rapidly than learning for those cues previously established as poor predictors. When the simple recurrent network is modified to include a variable associability parameter, the effects are easily modelled.