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：影响及其预测错误作为主观价值的窗口

科学家们越来越多地将强化学习的概念应用于影响，但应该应用哪些概念呢？它们的应用可以揭示哪些我们无法从直接可观察状态得知的内容？强化学习的一个重要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奖励（例如食物或金钱）的关注，人们相对较少关注人类可以另外报告相应的预期和经历的情感（例如感觉）这一事实。注意力已经开始转变，反映出预期和经历过的感受（包括预测错误）的解释力超出了可观察到的奖励，这反映出更广泛的“情感主义的兴起”。我们建议将强化学习的概念应用于情感有望阐明主观价值。同时，我们敦促科学家测试——而不是继承——可能不直接适用的概念。