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We present a refined model for online development of value system. As an indispensable part of a developmental robot, the value system signals the occurrence of salient sensory inputs, modulates the mapping from sensory inputs to action outputs, and evaluates candidate actions. No salient feature is predefined in the value system but instead novelty based on experience, which is applicable to any task. Furthermore, reinforcer is integrated with novelty. Thus, the value system of a robot can be developed through interactions with trainers. In the experiment, we treat vision-based neck action selection as a behavior guided by the value system. The robot's behavior is consistent with the attention mechanism in human infants.