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The process of conditioning via reward learning is highly relevant to the study of food choice and obesity. Learning is itself shaped by environmental exposure, with the potential for such exposures to vary substantially across individuals and across place and time. In this paper, we use computational techniques to extend a well-validated standard model of reward learning, introducing both substantial heterogeneity and dynamic reward exposures. We then apply the extended model to a food choice context. The model produces a variety of individual behaviors and population-level patterns which are not evident from the traditional formulation, but which offer potential insights for understanding food reward learning and obesity. These include a “lock-in” effect, through which early exposure can strongly shape later reward valuation. We discuss potential implications of our results for the study and prevention of obesity, for the reward learning field, and for future experimental and computational work.