O'Reilly Randall C., Norman Kenneth A.
Dept of Psychology, University of Colorado Boulder, 345 UCB, 80309, Boulder, CO, USA
Trends Cogn Sci. 2002 Dec 1;6(12):505-510. doi: 10.1016/s1364-6613(02)02005-3.
The complementary learning systems framework provides a simple set of principles, derived from converging biological, psychological and computational constraints, for understanding the differential contributions of the neocortex and hippocampus to learning and memory. The central principles are that the neocortex has a low learning rate and uses overlapping distributed representations to extract the general statistical structure of the environment, whereas the hippocampus learns rapidly using separated representations to encode the details of specific events while minimizing interference. In recent years, we have instantiated these principles in working computational models, and have used these models to address human and animal learning and memory findings, across a wide range of domains and paradigms. Here, we review a few representative applications of our models, focusing on two domains: recognition memory and animal learning in the fear-conditioning paradigm. In both domains, the models have generated novel predictions that have been tested and confirmed.
互补学习系统框架提供了一组简单的原则，这些原则源自生物学、心理学和计算限制的融合，用于理解新皮层和海马体对学习和记忆的不同贡献。核心原则是，新皮层的学习速率较低，并使用重叠的分布式表征来提取环境的一般统计结构，而海马体则通过分离的表征快速学习，以编码特定事件的细节，同时将干扰降至最低。近年来，我们已在有效的计算模型中实例化了这些原则，并使用这些模型来处理广泛领域和范式中的人类和动物学习与记忆研究结果。在此，我们回顾我们模型的一些代表性应用，重点关注两个领域：识别记忆和恐惧条件范式中的动物学习。在这两个领域中，模型都产生了经过测试和证实的新预测。
