Optimizing the human learnability of abstract network representations.
Optimizing the human learnability of abstract network representations.
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DOI：
10.1073/pnas.2121338119
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发表时间：
2022-08-30
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
作者：
信息通常可以被视为概念之间的关联网络。人类在周围的世界中建立信息网络的心理模型，但这些模型始终包含一些错误。在这里，我们提出了一个计算框架，通过故意强调或夸大某些网络特征来模拟人类网络学习的优化。我们在人类学习的计算模型中证明，有针对性的强调和去强调可以大大提高学习者对网络结构的把握。此外，我们确定了最佳强调模式如何随要学习的目标网络结构的拓扑结构以及人类学习者的基线准确性而变化。我们的研究结果阐明了设计原则和网络可学习性的优化。人类在知识、语言、音乐和社会中如何处理信息的关系模式还没有得到很好的理解。统计学习领域的先前工作已经证明，人类通过构建底层网络结构的内部模型来处理这些信息。然而，由于人类信息处理的局限性，这些心理地图往往是不准确的。这种限制的存在提出了明确的问题：给定一个希望人类学习的目标网络，应该向人类展示什么样的网络？我们应该简单地呈现目标网络，还是应该强调网络的某些部分，以主动减少学习中的预期错误？为了研究这些问题，我们在人类学习的计算模型中研究了网络可学习性的优化。通过评估一系列合成和真实世界的网络，我们发现，通过加强模块或集群内的连接，可学习性得到了增强。相比之下，当网络包含重要的核心-外围结构时，我们发现通过加强低度节点之间的外围边缘来优化可学习性。总的来说，我们的研究结果表明，人类网络学习的准确性可以通过有针对性地强调和不强调规定的信息部门来系统地提高。
Information can often be viewed as a network of associations between concepts. Humans build mental models of information networks in the world around them, yet those models consistently contain some errors. Here, we present a computational framework for simulating the optimization of human network learning by intentionally emphasizing or exaggerating some network features over others. We demonstrate in a computational model of human learning that targeted emphasis and de-emphasis can substantially enhance a learner’s grasp of network structure. Further, we identify how optimal emphasis patterns vary with the topology of the target network structure to be learned, as well as the baseline accuracy of the human learner. Our findings illuminate the principles of design and the optimization of network learnability. Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core–periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information.
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影响因子：
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