Model-based prioritization for acquiring protection.

Model-based prioritization for acquiring protection.
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DOI:
10.1371/journal.pcbi.1010805
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发表时间:
2022-12
影响因子:
4.3
通讯作者:
--
中科院分区:
生物学2区
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保护往往涉及前瞻性计划减轻损害所需行动的能力。涉及保护的决策的计算架构仍然不清楚,以及这些决策是否与其他有益的预期行为(如奖励获取)不同。在这里,我们比较了保护获取、奖励获取和惩罚回避,以检查三种行为类型之间的重叠和不同特征。获得保护与奖励一样具有积极的价值。无论是保护还是奖励,行为者获得的越多,利益也就越大。然而,奖励和保护发生在不同的情境中,保护存在于厌恶情境中。惩罚回避也发生在厌恶情境中,但不同于保护,因为惩罚是负价值的,会激发回避。在三项独立研究(总N = 600)中,我们应用计算建模来检验基于模型的强化学习对人类的保护、奖励和惩罚。以获得保护为动机的决定比获得奖励或避免惩罚引起更高程度的基于模型的控制,在学习率上没有显著差异。保护的情境效价不对称特征增加了灵活决策策略的部署,这表明基于模型的控制取决于结果所处的情境以及结果的效价。获得保护是人类获得安全的普遍方式。当人类预测到未来危险的可能性时,他们会做出面向未来的决定来获得安全。这些前瞻性的安全决策可能涉及基于模型的控制系统,它通过创建外部环境的心理地图来促进目标导向的决策。无法有效地使用基于模型的控制可能会揭示精神病理学中安全决策是如何出错的新见解。然而,能够识别基于模型控制的贡献的计算决策框架尚未应用于安全领域。相反,临床科学主导并调查了寻求安全的决定,作为对威胁的不适应反应。专注于适应不良的安全阻碍了对人类如何在适应目标的激励下做出决定的充分理解。目前的研究应用决策控制系统的计算模型来理解人类如何做出适应性决策来获得保护,而不是获得奖励和避免威胁。与奖励或威胁驱动的决策相比,安全驱动的决策引发了更多基于模型的控制。这些发现表明,安全不仅仅是一种不同形式的奖励寻求或威胁回避,相反,安全引发了对目标导向行为重要的决策控制系统的独特贡献。
Protection often involves the capacity to prospectively plan the actions needed to mitigate harm. The computational architecture of decisions involving protection remains unclear, as well as whether these decisions differ from other beneficial prospective actions such as reward acquisition. Here we compare protection acquisition to reward acquisition and punishment avoidance to examine overlapping and distinct features across the three action types. Protection acquisition is positively valenced similar to reward. For both protection and reward, the more the actor gains, the more benefit. However, reward and protection occur in different contexts, with protection existing in aversive contexts. Punishment avoidance also occurs in aversive contexts, but differs from protection because punishment is negatively valenced and motivates avoidance. Across three independent studies (Total N = 600) we applied computational modeling to examine model-based reinforcement learning for protection, reward, and punishment in humans. Decisions motivated by acquiring protection evoked a higher degree of model-based control than acquiring reward or avoiding punishment, with no significant differences in learning rate. The context-valence asymmetry characteristic of protection increased deployment of flexible decision strategies, suggesting model-based control depends on the context in which outcomes are encountered as well as the valence of the outcome. Acquiring protection is a ubiquitous way humans achieve safety. Humans make future-oriented decisions to acquire safety when they anticipate the possibility of future danger. These prospective safety decisions likely engage model-based control systems, which facilitate goal-oriented decision making by creating a mental map of the external environment. Inability to effectively use model-based control may reveal new insights into how safety decisions go awry in psychopathology. However, computational decision frameworks that can identify contributions of model-based control have yet to be applied to safety. Clinical science instead dominates and investigates decisions to seek out safety as a maladaptive response to threat. Focusing on maladaptive safety prevents a full understanding of how humans make decisions motivated by adaptive goals. The current studies apply computational models of decision control systems to understand how humans make adaptive decisions to acquire protection compared with acquiring reward and avoiding threat. Safety-motivated decisions elicited increased model-based control compared to reward- or threat-motivated decisions. These findings demonstrate that safety is not simply reward seeking or threat avoidance in a different form, but instead safety elicits distinct contributions of decision control systems important for goal-directed behavior.
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