Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08540, USA.
Hippocampus. 2010 Nov;20(11):1217-27. doi: 10.1002/hipo.20855.
We describe how the Complementary Learning Systems neural network model of recognition memory (Norman and O'Reilly (2003) Psychol Rev 104:611-646) can shed light on current debates regarding hippocampal and cortical contributions to recognition memory. We review simulation results illustrating three critical differences in how (according to the model) hippocampus and cortex contribute to recognition memory, all of which derive from the hippocampus' use of pattern separated representations. Pattern separation makes the hippocampus especially well-suited for discriminating between studied items and related lures; it makes the hippocampus especially poorly suited for computing global match; and it imbues the hippocampal ROC curve with a Y-intercept > 0. We also describe a key boundary condition on these differences: When the average level of similarity between items in an experiment is very high, hippocampal pattern separation can fail, at which point the hippocampal model will start to behave like the cortical model. We describe the implications of these simulation results for extant debates over how to describe hippocampal versus cortical contributions and how to measure these contributions.
我们描述了互补学习系统神经网络模型（Norman 和 O'Reilly（2003），心理评论 104:611-646）如何能够阐明当前关于海马体和皮质对识别记忆的贡献的争论。我们回顾了模拟结果，这些结果说明了海马体和皮质对识别记忆的贡献的三个关键差异，所有这些差异都源自海马体使用模式分离表示。模式分离使海马体特别适合区分研究过的项目和相关诱饵；它使海马体特别不适合计算全局匹配；它使海马体的 ROC 曲线具有>0 的 Y 截距。我们还描述了这些差异的一个关键边界条件：当实验中项目之间的平均相似度非常高时，海马体的模式分离可能会失败，此时海马体模型将开始表现得像皮质模型。我们描述了这些模拟结果对当前关于如何描述海马体与皮质贡献以及如何衡量这些贡献的争论的影响。
