What representations and computations underpin the contribution of the hippocampus to generalization and inference?
What representations and computations underpin the contribution of the hippocampus to generalization and inference?
复制标题
DOI：
10.3389/fnhum.2012.00157
复制
发表时间：
2012
影响因子：
2.9
通讯作者：
Kumaran D
中科院分区：
文献类型：
--
作者：
Kumaran D
传统上，实证研究和理论解释都强调海马体在情景记忆中的功能。在这里，我们提请注意的重要性，海马体的泛化，并专注于神经表征和计算，可能会巩固其在任务中的作用，如配对关联推理（派）范式。我们对海马体可能支持泛化的两种不同机制进行了主要区分：一种基于编码的机制，它创建重叠的表示，捕捉不同项目之间的高阶关系[例如，时间上下文模型（TCM）：霍华德等人，和一个基于检索的模型[递归与情景记忆导致泛化（REMERGE）：Kumaran和McClelland，]，有效地计算这些关系在检索点，通过一个递归机制，允许多个模式分离的情景代码的动态交互。我们还讨论了我们所说的转移效应一个更抽象的泛化的例子，它也与海马体的功能有关。我们认为，这种现象是如何构成固有的挑战，如TCM和REMERGE模型，并概述了潜在的适用性的一个单独的类模型层次贝叶斯模型（HBM）在这种情况下。我们的希望是，这篇文章将提供一个基本的框架内考虑的理论机制的海马体的作用，在泛化，并在最低限度作为一个刺激未来的工作解决问题，去海马体的功能的核心。
Empirical research and theoretical accounts have traditionally emphasized the function of the hippocampus in episodic memory. Here we draw attention to the importance of the hippocampus to generalization, and focus on the neural representations and computations that might underpin its role in tasks such as the paired associate inference (PAI) paradigm. We make a principal distinction between two different mechanisms by which the hippocampus may support generalization: an encoding-based mechanism that creates overlapping representations which capture higher-order relationships between different items [e.g., Temporal Context Model (TCM): Howard et al., ]—and a retrieval-based model [Recurrence with Episodic Memory Results in Generalization (REMERGE): Kumaran and McClelland, ] that effectively computes these relationships at the point of retrieval, through a recurrent mechanism that allows the dynamic interaction of multiple pattern separated episodic codes. We also discuss what we refer to as transfer effects—a more abstract example of generalization that has also been linked to the function of the hippocampus. We consider how this phenomenon poses inherent challenges for models such as TCM and REMERGE, and outline the potential applicability of a separate class of models—hierarchical Bayesian models (HBMs) in this context. Our hope is that this article will provide a basic framework within which to consider the theoretical mechanisms underlying the role of the hippocampus in generalization, and at a minimum serve as a stimulus for future work addressing issues that go to the heart of the function of the hippocampus.
登录
查看更多内容
影响因子：
16.2
作者：
Kumaran, Dharshan;Summerfield, Jennifer J.;Hassabis, Demis;Maguire, Eleanor A.
通讯作者：
Maguire, Eleanor A.
影响因子：
3
作者：
Howard MW;Shankar KH;Jagadisan UK
通讯作者：
Jagadisan UK
DOI：
10.1073/pnas.0802631105
发表时间：
2008-08-05
影响因子：
11.1
作者：
Kemp, Charles;Tenenbaum, Joshua B.
通讯作者：
Tenenbaum, Joshua B.
影响因子：
3.8
作者：
Flusberg, Stephen J.;Thibodeau, Paul H.;Glick, Jeremy J.
通讯作者：
Glick, Jeremy J.
DOI：
10.1073/pnas.94.13.7109
发表时间：
1997-06-24
影响因子：
11.1
作者：
Dusek, JA;Eichenbaum, H
通讯作者：
Eichenbaum, H