Recently, computational models of reinforcement learning have been applied for the analysis of neuroimaging data. It has been clarified that the striatum plays a key role in decision making. We review the reinforcement learning theory and the biological structures such as the brain and signals such as neuromodulators associated with reinforcement learning. We also investigated the function of the striatum and the neurotransmitter serotonin in reward prediction. We first studied the brain mechanisms for reward prediction at different time scales. Our experiment on the striatum showed that the ventroanterior regions are involved in predicting immediate rewards and the dorsoposterior regions are involved in predicting future rewards. Further, we investigated whether serotonin regulates both the reward selection and the striatum function are specialized reward prediction at different time scales. To this end, we regulated the dietary intake of tryptophan, a precursor of serotonin. Our experiment showed that the activity of the ventral part of the striatum was correlated with reward prediction at shorter time scales, and this activity was stronger at low serotonin levels. By contrast, the activity of the dorsal part of the striatum was correlated with reward prediction at longer time scales, and this activity was stronger at high serotonin levels. Further, a higher proportion of small reward choices, together with a higher rate of discounting of delayed rewards is observed in the low-serotonin condition than in the control and high-serotonin conditions. Further examinations are required in future to assess the relation between the disturbance of reward prediction caused by low serotonin and mental disorders related to serotonin such as depression.