Mack Michael L, Love Bradley C, Preston Alison R
Department of Psychology, University of Toronto, Toronto, ON, Canada.
Experimental Psychology, University College London, London, UK; Alan Turing Institute, London, UK.
Neurosci Lett. 2018 Jul 27;680:31-38. doi: 10.1016/j.neulet.2017.07.061. Epub 2017 Aug 8.
Concepts organize our experiences and allow for meaningful inferences in novel situations. Acquiring new concepts requires extracting regularities across multiple learning experiences, a process formalized in mathematical models of learning. These models posit a computational framework that has increasingly aligned with the expanding repertoire of functions associated with the hippocampus. Here, we propose the Episodes-to-Concepts (EpCon) theoretical model of hippocampal function in concept learning and review evidence for the hippocampal computations that support concept formation including memory integration, attentional biasing, and memory-based prediction error. We focus on recent studies that have directly assessed the hippocampal role in concept learning with an innovative approach that combines computational modeling and sophisticated neuroimaging measures. Collectively, this work suggests that the hippocampus does much more than encode individual episodes; rather, it adaptively transforms initially-encoded episodic memories into organized conceptual knowledge that drives novel behavior.
概念组织我们的经验,并使我们能够在新情境中做出有意义的推断。获取新概念需要从多个学习经验中提取规律,这一过程在学习的数学模型中被形式化。这些模型提出了一个计算框架,该框架越来越多地与海马体相关的不断扩展的功能库相一致。在这里,我们提出了概念学习中海马体功能的“情节到概念”(EpCon)理论模型,并回顾了支持概念形成的海马体计算的证据,包括记忆整合、注意力偏向和基于记忆的预测误差。我们关注最近的研究,这些研究通过结合计算建模和复杂神经成像测量的创新方法,直接评估了海马体在概念学习中的作用。总的来说,这项工作表明,海马体的作用远不止于编码单个情节;相反,它会将最初编码的情节性记忆适应性地转化为有组织的概念性知识,从而驱动新的行为。