Neurons are sensitive to specific aspects of natural stimuli, which are according to different statistical criteria an optimal representation of the natural sensory input. Since these representations are purely sensory, it is still an open question whether they are suited to generate meaningful behavior. Here we introduce an optimization scheme that applies a statistical criterion to an agent s sensory input while taking its motor behavior into account. We first introduce a general cognitive model, and second develop an optimization scheme that increases the predictability of the sensory outcome of the agent s motor actions and apply this to a navigational paradigm.In the cognitive model, place cells divide the environment into discrete states, similar to hippocampal place cells. The agents learned the sensory outcome of its action by the state-to-state transition probabilities and the extent to which these motor actions are caused by sensory-driven reflexive behavior (obstacle avoidance). Navigational decision making integrates both learned components to derive the actions that are most likely to lead to a navigational goal. Next we introduced an optimization process that modified the state distributions to increase the predictability of the sensory outcome of the agent s actions.The cognitive model successfully performs the navigational task, and the differentiation between transitions and reflexive processing increases both behavioral accuracy, as well as behavioral adaptation to changes in the environment. Further, the optimized sensory states are similar to place fields found in behaving animals. The spatial distribution of states depends on the agent s motor capabilities as well as on the environment. We proofed the generality of predictability as a coding principle by comparing it to the existing ones. Our results suggest that the agent s motor apparatus can play a profound role in the formation of place fields and thus in higher sensory representations.