When a movement results in error, the nervous system amends the motor commands that generate the subsequent movement. Here we show that this adaptation depends not just on error, but also on passage of time between the two movements. We observed that subjects learned a reaching task faster, i.e., with fewer trials, when the inter-trial time intervals (ITIs) were lengthened. We hypothesized two computational mechanisms that could have accounted for this. First, learning could have been driven by a Bayesian process where the learner assumed that errors are due to perturbations that have multiple timescales. In theory, longer ITIs can produce faster learning because passage of time might increase uncertainty, which in turn increases sensitivity to error. Second, error in a trial may result in a trace that decays with time. If the learner continued to sample from the trace during the ITI, then adaptation would increase with increased ITIs. The two models made separate predictions: The Bayesian model predicted that when movements are separated by random ITIs, the learner would learn most from a trial that followed a long time interval. In contrast, the trace model predicted that the learner would learn most from a trial that preceded a long time interval. We performed two experiments to test for these predictions and in both experiments found evidence for the trace model. We suggest that motor error produces an error memory trace that decays with a time constant of about 4 seconds, continuously promoting adaptation until the next movement.