Center on Social Dynamics and Policy, The Brookings Institution Washington, DC, USA.
Front Comput Neurosci. 2012 Oct 11;6:82. doi: 10.3389/fncom.2012.00082. eCollection 2012.
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.
通过奖励学习进行调节的过程与食物选择和肥胖的研究密切相关。学习本身受到环境暴露的影响，这种暴露在个体之间、地点和时间之间可能有很大差异。在本文中，我们使用计算技术扩展了一个经过充分验证的奖励学习标准模型，引入了大量的异质性和动态奖励暴露。然后，我们将扩展后的模型应用于食物选择的情境中。该模型产生了各种个体行为和群体模式，这些在传统的表述中并不明显，但为理解食物奖励学习和肥胖提供了潜在的见解。其中包括一种“锁定”效应，即早期暴露可以强烈塑造后期的奖励价值。我们讨论了我们的结果对肥胖研究和预防、奖励学习领域以及未来的实验和计算工作的潜在影响。
