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.

本文介绍了 COGnitive Neural GENerative 系统（CogNGen），它是一种认知架构，将两种神经生理学可信的计算模型：预测性处理和高维 / 矢量符号模型相结合。CogNGen 的表现与 ACT-R 和 Spaun/Nengo 相当，为设计能够不断从不同任务学习并模拟人类在更大尺度上表现的代理人奠定了基础。本文在四个迷宫学习任务上测试了 CogNGen，并发现其与深度强化学习模型相当，并在测试记忆的任务上超过了它们的表现。