Abstract：
Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena. A video abstract of this article can be viewed at https://youtu.be/1v1U6CjDZ1Q RESEARCH HIGHLIGHTS: We fit a large dataset by aggregating the existing scale error data to the zero-inflated Poisson (ZIP) model. Scale errors peaked along the different developmental indices, but the underlying statistical structure differed between the in-lab and classroom datasets. Repeated experiences with scale error tasks and the children\'s gender affected the number of scale errors produced per session. Predicate vocabulary size (e.g., adjectives or verbs) better predicts developmental changes in scale errors than noun vocabulary size.
摘要：
尺度错误是一种有趣的现象，在这种现象中，孩子试图对一个微小的物体执行特定于物体的动作。存在几种观点来解释尺度误差的发展机制；然而，对于不同因素如何相互作用和影响规模误差，没有统一的说法，以前的研究中使用的统计方法不能充分捕获数据的结构。通过对九项不同研究（n=528）的汇总数据集进行二次分析，并使用更合适的统计方法，这项研究提供了一个更准确的描述尺度误差的发展。我们实现了零膨胀泊松（ZIP）回归，该回归可以直接处理具有零观测值的堆栈的计数数据，并将发展指数视为连续变量。结果表明，尺度误差的发展趋势是由倒U形曲线而不是简单的线性函数记录的，尽管非线性捕获了实验室和教室数据之间的比例误差的不同方面。我们还发现，对尺度错误任务的重复体验减少了尺度错误的数量，而女孩比男孩犯的比例错误更多。此外，模型比较方法揭示了谓词词汇量的大小(例如，形容词或动词)，预测量表误差的发展变化优于名词词汇量，特别是在存在或不存在尺度误差方面。ZIP模型的应用使研究人员能够辨别不同因素如何影响规模误差产生，从而为揭开这些现象背后的机制提供了新的见解。本文的视频摘要可以在https://youtu查看。be/1v1U6CjDZ1Q研究亮点：我们通过将现有的比例误差数据聚合到零膨胀的泊松（ZIP）模型来拟合大型数据集。尺度误差沿不同的发育指数达到峰值，但是实验室和教室数据集之间的基本统计结构有所不同。对量表错误任务的重复体验和孩子的性别会影响每个会话产生的量表错误数量。谓词量(例如，形容词或动词）比名词词汇量大更好地预测量表错误的发展变化。
