Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader “rise of affectivism,” attention has started to shift, revealing explanatory power of expected and experienced feelings—including prediction errors—above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test—rather than inherit—concepts that may not apply directly.

中文翻译：

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隐藏的奖励：影响及其预测误差作为 Windows 进入主观值


科学家越来越多地应用强化学习中的概念来影响，但哪些概念应该适用呢？他们的应用可以揭示什么我们无法从直接可观察的状态中知道的呢？一个重要的强化学习概念是奖励期望和结果之间的差异。这种奖励预测错误已成为人类、动物和机器适应性行为研究的基础。然而，由于历史上对动物模型和可观察的奖励（例如，食物或金钱）的关注，相对较少地关注人类还可以报告相应的预期和经历的影响（例如，感觉）这一事实。反映出更广泛的 “情感主义的兴起”，注意力已经开始转移，揭示了预期和体验到的感受（包括预测错误）的解释力，超出了可观察的回报。我们建议将强化学习中的概念应用于影响有望阐明主观价值。同时，我们敦促科学家测试而不是继承可能无法直接应用的概念。