Corticostriatal mechanisms of causal inference and temporal credit assignment.

从经验中学习所需的行动需要通过整合 随着时间的推移，分配给每个动作的后果。在现实世界中，行动和结果发生在复杂的 序列和连续的事件流必须被解析成适当的因果动作对和 在这些配对可以被评估之前的结果。然而，大脑如何解决这个问题，被称为时间 信用分配(TCA)未知。拟议项目的目标是使用创新的范例和 测试关于前额叶皮质异质记忆动力学在TCA中的作用的新假设。 在我们的上下文滞后的强盗任务中，猴子将在两个选项中的一个中选择三个选项 交替的上下文，但对一个上下文中的选择的反馈将暂时延迟，并在 另一种选择是在另一种情况下做出的。在这个任务中学习最优选择包括两个部分：因果 推理用于学习任务的因果结构或模型，以及基于模型的TCA用于学习 每一个选择。从延迟结果中学习需要记忆所选择的行动或资格痕迹(ET)。 虽然理论假设ET随时间呈指数衰减(即EXP-ET)，但EXP-ET不能解决TCA 当因果行为与结果在时间上和不相关的事件分开时。我们假设 灵活的ET动力学可能是因果推理和基于模型的TCA的关键。 更具体地说，我们假设基于模型的TCA需要动态调制的ET(动态- ET)，其在其结果的预测时间被选择性地激活以避免从 相互干扰的事件。对于因果推理，我们假设对过去行为的记忆可能通过 后见之明，寻找新的因果联系(即假设的ET，Hyp-ET)。我们还假设外星人可能是 强烈支持直到滞后反馈(即持久ET、持久ET)，以测试新链路的准确性 混淆干预投入的代价。我们将调查ETS的灵活性有多大 由皮质-纹状体网络不同区域的神经活动的异质性动力学支持。 首先，我们将评估灵长类前额叶皮质(PFC)是否为基于模型的模型提供动态ET TCA，而纹状体为基于邻接性的TCA提供了EXP-ET。第二，我们将评估是否 背内侧和背外侧分别提供Hyp-ET和Persistent-ET。我们将采取高度的 综合方法并结合多尺度神经记录、扰动和计算建模来 研究前额叶皮质神经活动的复杂模式和动力学是否以及如何构成 支持基于模型的TCA的充要条件。拟议中的项目将使 传统的将记忆视为存储的观点，将记忆重塑为学习和推理的组成部分 时间动力学是它的关键结构。因果推论是有精神障碍的个体的核心 精神病症状和我们的工作将有助于了解潜在的前额叶病理生理学。

Learning desired actions from experience requires evaluating alternative actions by integrating the consequences assigned to each action over time. In the real world, actions and outcomes occur in complex sequences, and a continuous stream of events must be parsed into appropriate pairs of causative action and outcome before such pairs can be evaluated. However, how the brain solves this problem, known as temporal credit assignment (TCA) is unknown. The goal of the proposed project is to use an innovative paradigm and test novel hypotheses regarding the role of heterogeneous dynamics of memory in the prefrontal cortex in TCA. In our contextual lagged bandit task, monkeys will choose between three options offered in one of two alternating contexts, but feedback for a choice in one context will be temporally delayed and delivered after another choice is made in the other context. Learning optimal choices in this task consists of two parts: causal inference for learning the causal structure or model of the task, and model-based TCA for learning the value of each choice. Learning from delayed outcome requires memory of a chosen action or eligibility trace (ET). Although theories postulate that ET exponentially decays over time (i.e. exp-ET), exp-ET cannot resolve TCA when causative action is separated from the outcome in time and by irrelevant events. We hypothesize the flexible dynamics of ET might be crucial for causal inference and model-based TCA. More specifically, we hypothesize that model-based TCA requires dynamically-modulated ET (dynamic- ET) which is selectively activated at the predicted time of its outcome to obviate receiving credits from intervening events. For causal inference, we hypothesize that memory of past actions might be re-activated by hindsight in search of a new causal link (i.e. hypothetical ET, hyp-ET). We also hypothesize that ET might be strongly sustained until the lagged feedback (i.e. persistent ET, persist-ET) to test the accuracy of new link at the expense of confounding intervening inputs. We will investigate how flexible dynamics of ETs might be supported by heterogeneous dynamics of neural activity across different regions of cortico-striatal network. First, we will assess whether the primate prefrontal cortex (PFC) provides dynamic-ET for model-based TCA, whereas the striatum provides exp-ET for contiguity-based TCA. Second, we will assess whether dorsomedial and dorsolateral PFC provide hyp-ET and persist-ET, respectively. We will take a highly integrative approach and combine multi-scale neural recordings, perturbations and computational modeling to examine whether and how complex patterns and dynamics of neural activity in the prefrontal cortex constitute necessary and sufficient conditions to support model-based TCA. The proposed project will transform the conventional view of memory as storage, recasting memory as an integral part of learning and reasoning with temporal dynamics being its key structure. Causal inference is central to dysfunction in the individuals with psychotic symptoms and our work will contribute to understanding underlying prefrontal pathophysiology.