论文标题
迷宫学习使用高维预测处理认知架构
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture
论文作者
论文摘要
我们介绍了认知神经生成系统（Cogngen），这是一种结合了两个神经生物学上可行的计算模型的认知结构：预测性处理和超值/矢量符号模型。我们从ACT-R和Spaun/Nengo等体系结构中汲取灵感。 Cogngen与这些相吻合，在ACT-R对人类认知的高级象征性描述与Spaun的低级神经生物学描述之间提供了一定的详细信息，此外，还为设计代理创造了基础，以设计基础，以从各种任务中学习，并从更大的人类绩效中学习比当前系统更大的规模建模。我们在四个迷宫学习任务上测试Cogngen，包括测试记忆和计划的任务，发现Cogngen与深度强化学习模型的性能相匹配，并且超越了旨在测试内存的任务。
We present the COGnitive Neural GENerative system (CogNGen), a cognitive architecture that combines two neurobiologically-plausible, computational models: predictive processing and hyperdimensional/vector-symbolic models. We draw inspiration from architectures such as ACT-R and Spaun/Nengo. CogNGen is in broad agreement with these, providing a level of detail between ACT-R's high-level symbolic description of human cognition and Spaun's low-level neurobiological description, furthermore creating the groundwork for designing agents that learn continually from diverse tasks and model human performance at larger scales than what is possible with current systems. We test CogNGen on four maze-learning tasks, including those that test memory and planning, and find that CogNGen matches performance of deep reinforcement learning models and exceeds on a task designed to test memory.
