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Abstract：
Human cognition is unique in its ability to perform a wide range of tasks and to learn new tasks quickly. Both abilities have long been associated with the acquisition of knowledge that can generalize across tasks and the flexible use of that knowledge to execute goal-directed behavior. We investigate how this emerges in a neural network by describing and testing the Episodic Generalization and Optimization (EGO) framework. The framework consists of an episodic memory module, which rapidly learns relationships between stimuli; a semantic pathway, which more slowly learns how stimuli map to responses; and a recurrent context module, which maintains a representation of task-relevant context information, integrates this over time, and uses it both to recall context-relevant memories (in episodic memory) and to bias processing in favor of context-relevant features and responses (in the semantic pathway). We use the framework to address empirical phenomena across reinforcement learning, event segmentation, and category learning, showing in simulations that the same set of underlying mechanisms accounts for human performance in all three domains. The results demonstrate how the components of the EGO framework can efficiently learn knowledge that can be flexibly generalized across tasks, furthering our understanding of how humans can quickly learn how to perform a wide range of tasks-a capability that is fundamental to human intelligence.
摘要：
人类认知在执行广泛任务和快速学习新任务的能力方面是独一无二的。这两种能力长期以来都与获取可以跨任务概括的知识以及灵活使用该知识来执行目标导向行为有关。我们通过描述和测试情节概括和优化（EGO）框架来研究这种情况如何在神经网络中出现。该框架由一个情景存储器模块组成，快速学习刺激之间的关系；语义通路，更慢地学习刺激如何映射到反应；和一个循环上下文模块，它维护与任务相关的上下文信息的表示，随着时间的推移，并使用它来回忆与上下文相关的记忆（在情景记忆中）和偏向处理，以支持与上下文相关的特征和响应（在语义途径中）。我们使用该框架来解决强化学习中的经验现象，事件分割，和类别学习，在模拟中显示，同一组基础机制说明了人类在所有三个领域中的表现。结果展示了EGO框架的组件如何有效地学习可以跨任务灵活推广的知识，进一步了解人类如何快速学习如何执行广泛的任务-这种能力是人类智能的基础。
