Flexible behavior requires the creation, updating, and expression of memories to depend on context. While the neural underpinnings of each of these processes have been intensively studied, recent advances in computational modeling revealed a key challenge in context-dependent learning that had been largely ignored previously: Under naturalistic conditions, context is typically uncertain, necessitating contextual inference. We review a theoretical approach to formalizing context-dependent learning in the face of contextual uncertainty and the core computations it requires. We show how this approach begins to organize a large body of disparate experimental observations, from multiple levels of brain organization (including circuits, systems, and behavior) and multiple brain regions (most prominently the prefrontal cortex, the hippocampus, and motor cortices), into a coherent framework. We argue that contextual inference may also be key to understanding continual learning in the brain. This theory-driven perspective places contextual inference as a core component of learning.

中文翻译：

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情境相关学习的计算和神经基础

灵活的行为要求记忆的创建、更新和表达取决于上下文。虽然每个过程的神经基础都得到了深入研究，但计算模型的最新进展揭示了上下文相关学习的一个关键挑战，而这个挑战之前在很大程度上被忽视了：在自然条件下，上下文通常是不确定的，需要上下文推理。我们回顾了一种在面对上下文不确定性的情况下形式化上下文相关学习的理论方法及其所需的核心计算。我们展示了这种方法如何开始组织大量不同的实验观察，从大脑组织的多个层面（包括回路、系统和行为）和多个大脑区域（最突出的是前额皮质、海马体和运动皮质），纳入一个连贯的框架。我们认为，情境推理也可能是理解大脑持续学习的关键。这种理论驱动的观点将情境推理视为学习的核心组成部分。