论文标题
好奇心作为填充,压缩和重新配置知识网络
Curiosity as filling, compressing, and reconfiguring knowledge networks
论文作者
论文摘要
由于好奇心在我们的生活中起着重要的作用,因此,几种理论构造(例如信息差距理论和压缩进步理论)试图解释我们如何参与其实践。根据前者的说法,好奇心是获取我们对世界的理解中缺少的信息的动力。根据后者的说法,好奇心是构建越来越多的世界心理模型的动力。为了补充这些理论固有的致密过程,我们提出了构象变化理论,其中我们认为好奇心会导致具有明显概念灵活性的心理模型。我们将好奇心形式化为建立不断增长的知识网络的过程,以定量研究信息差距理论,压缩进步理论和好奇心的构象变化理论。在知识网络中,可以将差距识别为拓扑空腔,可以使用网络可压缩性来量化压缩进度,并且可以将灵活性衡量为构象的自由度数量。我们利用从在线百科全书Wikipedia获得的数据来确定每个理论解释个人和集体建立的知识网络的增长程度。我们的发现提供了对好奇心的多元化观点的支持,其中本质上动机的信息获取填补了知识差距,并同时导致了越来越可压缩和灵活的知识网络。在个人和集体中,我们确定了每个理论说明可能是解释性的上下文,从而澄清了他们对好奇心的互补和独特的解释。我们的发现提供了一种新颖的网络理论观点,这些观点是关于本质上动机的信息获取的,该视角可能与传统的好奇心分类法扩展或迫使扩展。
Due to the significant role that curiosity plays in our lives, several theoretical constructs, such as the information gap theory and compression progress theory, have sought to explain how we engage in its practice. According to the former, curiosity is the drive to acquire information that is missing from our understanding of the world. According to the latter, curiosity is the drive to construct an increasingly parsimonious mental model of the world. To complement the densification processes inherent to these theories, we propose the conformational change theory, wherein we posit that curiosity results in mental models with marked conceptual flexibility. We formalize curiosity as the process of building a growing knowledge network to quantitatively investigate information gap theory, compression progress theory, and the conformational change theory of curiosity. In knowledge networks, gaps can be identified as topological cavities, compression progress can be quantified using network compressibility, and flexibility can be measured as the number of conformational degrees of freedom. We leverage data acquired from the online encyclopedia Wikipedia to determine the degree to which each theory explains the growth of knowledge networks built by individuals and by collectives. Our findings lend support to a pluralistic view of curiosity, wherein intrinsically motivated information acquisition fills knowledge gaps and simultaneously leads to increasingly compressible and flexible knowledge networks. Across individuals and collectives, we determine the contexts in which each theoretical account may be explanatory, thereby clarifying their complementary and distinct explanations of curiosity. Our findings offer a novel network theoretical perspective on intrinsically motivated information acquisition that may harmonize with or compel an expansion of the traditional taxonomy of curiosity.