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Learning and representation of recent structure in the environment: Behavioral, neuroimaging, and computational investigations.

机译：学习和表示环境中的最新结构：行为，神经成像和计算研究。

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摘要

Environmental statistics gradually come to be represented in cortical areas of the brain after extensive experience and long periods of time. In many contexts, however, we are exposed to new environmental regularities that influence our behavior very rapidly. What kinds of neural processes and representations support such rapid statistical learning? The medial temporal lobe (MTL) can learn new information rapidly, but it is traditionally thought to specialize in learning new arbitrary---not structured---information. Much of this dissertation work investigates whether this rapid learning ability may in fact extend to learning new structured information. In support of this idea, we found that representations of objects that appear nearby in time become more similar to each other throughout the MTL. Beyond indicating that the MTL is involved, these findings begin to suggest what kinds of representations it may construct to support statistical learning. We found the same kind of representational similarity in the hippocampus in a paradigm with more complex structure. In this paradigm, stimulus sequences were generated by a graph with community structure, where the strength of transition probabilities---a cue commonly considered to be critical for event parsing---was uniform, and therefore uninformative for parsing. We found that participants learned the structure nonetheless, as evidenced by event parsing behavior, and that representations of items from the same community came to be represented more similarly than items from different communities in the hippocampus, as well as in the inferior frontal gyrus, anterior temporal lobe, and superior temporal gyrus. Connectivity analyses suggest that the hippocampus may be a central hub in the network of regions involved in learning new events. We additionally found that a patient with MTL damage failed to learn new temporal regularities, providing evidence that the area is necessary for this form of learning. Finally, we ran experiments and developed a computational model suggesting that sleep may help consolidate recently learned structured information. This work begins to characterize the neural mechanisms underlying our ability to rapidly extract and consolidate regularities in a new environment.
机译：经过大量的经验和长时间的研究，环境统计数据逐渐在大脑的皮质区域中体现出来。但是，在许多情况下，我们面临着新的环境法规，这些法规会很快影响我们的行为。什么样的神经过程和表示支持这种快速的统计学习？颞中叶（MTL）可以快速学习新信息，但传统上认为它专门学习新的任意（而非结构化）信息。本文的大部分工作都在研究这种快速学习能力是否实际上可以扩展到学习新的结构化信息。为了支持该想法，我们发现在整个MTL中，时间上出现在附近的对象的表示变得越来越相似。这些发现不仅表明涉及MTL，而且还开始暗示它可以构建哪种表示形式来支持统计学习。我们在具有更复杂结构的范例中在海马中发现了相同类型的表示相似性。在这种范式中，刺激序列是由具有社区结构的图生成的，其中过渡概率的强度（通常被认为是事件解析的关键提示）是统一的，因此对于解析无益。我们发现参与者仍然学会了结构，如事件解析行为所证明的那样，来自同一社区的项目的表示比来自海马以及前额下回中不同社区的项目的表示更加相似。颞叶和颞上回。连通性分析表明，海马可能是参与学习新事件的区域网络中的中心枢纽。我们还发现，患有MTL损伤的患者无法学习新的时间规律，这提供了该领域对于这种学习形式所必需的证据。最后，我们进行了实验并开发了一个计算模型，表明睡眠可能有助于巩固最近学到的结构化信息。这项工作开始刻画我们在新环境中快速提取和巩固规律性的能力所依据的神经机制。

著录项

  • 作者

    Schapiro, Anna C.;

  • 作者单位

    Princeton University.;

  • 授予单位 Princeton University.;
  • 学科 Biology Neuroscience.;Psychology Cognitive.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 177 p.
  • 总页数 177
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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