This research investigates the influences of temporal structure on the representation of serial order. Experiments are performed in a neural network model of sequence learning and in human subjects. In the sequence learning model, a recurrent network of leaky integrator neurons encodes a succession of internal states that become associated, by reinforcement learning, with the correct sequential responses. First, the model is shown to learn a simple temporal discrimination task. The model is then exposed to two novel serial reaction time (SRT) experiments. In the standard SRT task (M.J. Nissen, P. Bullemer, Attentional requirements of learning: evidence from performance measures, Cogn. Psychol. 19 (1987) 1-32 [16]), reaction times for stimuli presented in a repeating sequence are reduced with respect to those for random stimuli, providing a measure of sequence learning. The novelty of the current experiments is that imbedded in the serial order of the sequences, there is a temporal structure of delays. The model is sensitive to both the serial structure and the temporal structure of the sequences. This observation is then confirmed in human subjects. These results demonstrate how a novel recurrent architecture encodes the interaction of temporal and serial structure and provide insight into related aspects of human sensori-motor sequence learning.