Probabilistic Reinforcement Learning and Anhedonia.
Probabilistic Reinforcement Learning and Anhedonia.
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DOI：
10.1007/7854_2022_349
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发表时间：
2022-04
影响因子：
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通讯作者：
Brian D. Kangas;A. Der-Avakian;D. Pizzagalli
中科院分区：
文献类型：
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作者：
Brian D. Kangas;A. Der-Avakian;D. Pizzagalli
尽管与各种神经精神疾病相关的非享乐性症状很突出，但目前还没有经过批准的治疗方法来减轻对先前奖励的刺激的反应性丧失。然而，最近对快感缺乏症的结构进行了重新概念化，包括在研究领域标准(RDoC)倡议范围内，重新推动了对快感缺失改进治疗选择的探索。本章将主要讨论奖励学习的RDoC正价系统的一般结构和概率强化学习的子结构。一般框架强调通过非对称概率偶然性下的强化学习来客观地测量受试者对奖励的响应性，作为量化奖励学习的一种手段。事实上，迟钝的奖赏反应和奖赏学习是快感缺乏症的主要特征，在严重的抑郁症中被反复描述。此外，这些概率强化技术还可以揭示神经生物学机制，以帮助开发创新的治疗方法。在本章中，我们描述了研究奖赏学习如何通过RDoC推荐的四个任务来提高我们对快感缺失的理解，这些任务已经被用来探索对概率强化偶发事件的敏感性，以及这些任务的表现在各种神经精神状况下是如何被破坏的。我们还说明了在实验动物中概率强化分析的反向翻译方法如何有助于了解药理和生理机制。接下来，我们简要地总结了概率强化学习的神经生物学，主要集中在前额叶皮质、前扣带回皮质、纹状体和杏仁核。最后，我们讨论了这一新兴领域的治疗意义和未来方向。
Despite the prominence of anhedonic symptoms associated with diverse neuropsychiatric conditions, there are currently no approved therapeutics designed to attenuate the loss of responsivity to previously rewarding stimuli. However, the search for improved treatment options for anhedonia has been reinvigorated by a recent reconceptualization of the very construct of anhedonia, including within the Research Domain Criteria (RDoC) initiative. This chapter will focus on the RDoC Positive Valence Systems construct of reward learning generally and sub-construct of probabilistic reinforcement learning specifically. The general framework emphasizes objective measurement of a subject’s responsivity to reward via reinforcement learning under asymmetrical probabilistic contingencies as a means to quantify reward learning. Indeed, blunted reward responsiveness and reward learning are central features of anhedonia and have been repeatedly described in major depression. Moreover, these probabilistic reinforcement techniques can also reveal neurobiological mechanisms to aid development of innovative treatment approaches. In this chapter, we describe how investigating reward learning can improve our understanding of anhedonia via the four RDoC-recommended tasks that have been used to probe sensitivity to probabilistic reinforcement contingencies and how such task performance is disrupted in various neuropsychiatric conditions. We also illustrate how reverse translational approaches of probabilistic reinforcement assays in laboratory animals can inform understanding of pharmacological and physiological mechanisms. Next, we briefly summarize the neurobiology of probabilistic reinforcement learning, with a focus on the prefrontal cortex, anterior cingulate cortex, striatum, and amygdala. Finally, we discuss treatment implications and future directions in this burgeoning area.