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散点图中异常值和熟悉背景对趋势线估计的影响。

Effects of outlier and familiar context in trend-line estimates in scatterplots.

作者信息

Oral Başak, Boduroglu Aysecan

机构信息

Department of Information and Computing Science, Utrecht University, Utrecht, Netherlands.

Department of Psychology, Rumelifeneri, Koc University, Sarıyer Rumeli Feneri Yolu, Sarıyer, 34450, İstanbul, Türkiye.

出版信息

Mem Cognit. 2024 Oct 21. doi: 10.3758/s13421-024-01646-0.

DOI:10.3758/s13421-024-01646-0
PMID:39432211
Abstract

Lately, there has been a growing fascination with blending research on visualizing data and understanding how our basic visual perception works. Taking this path, this research delved into the connection between ensemble perception, which involves quickly and accurately grasping essential information from sets of visually similar objects, and how we process scatterplots. Across two experiments, we aimed to answer a couple of connected questions. First, we investigated whether having an outlier in a scatterplot affects how people draw trend-line estimates. Second, we explored whether what we are familiar with and the presence of outliers that match the trend affect how we draw trend-line estimates in scatterplots. In both experiments, we showed participants scatterplots for a short time, manipulating whether there were outliers or not. Then, using a computer mouse, participants drew their trend-line estimates. By comparing what they drew with possible trend-line solutions, we discovered that when there is no context, the outlier and the other points in a scatterplot are seen as equally important in drawing the trend-line estimate. But when the scatterplot depicted a familiar context and the outlier fitted the trend, people tended to give more weight to those outlier points in their drawings. This suggested that what we already believe can sway how we draw trend-line estimates even from quickly shown scatterplots.

摘要

最近,人们越来越热衷于将数据可视化研究与理解我们基本视觉感知的工作方式相结合。沿着这条道路,本研究深入探讨了整体感知(即快速准确地从视觉上相似的物体集合中掌握基本信息)与我们处理散点图的方式之间的联系。在两项实验中,我们旨在回答几个相关问题。首先,我们研究了散点图中存在异常值是否会影响人们绘制趋势线估计。其次,我们探讨了我们熟悉的内容以及与趋势匹配的异常值的存在是否会影响我们在散点图中绘制趋势线估计。在两项实验中,我们向参与者短暂展示散点图,控制是否存在异常值。然后,参与者使用电脑鼠标绘制他们的趋势线估计。通过将他们绘制的结果与可能的趋势线解决方案进行比较,我们发现,在没有背景信息时,散点图中的异常值和其他点在绘制趋势线估计时被视为同等重要。但当散点图描绘的是熟悉的背景且异常值符合趋势时,人们在绘图时往往会更重视那些异常值点。这表明,我们已有的认知甚至会影响我们从快速展示的散点图中绘制趋势线估计的方式。

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本文引用的文献

1
Outlier detection and rejection in scatterplots: Do outliers influence intuitive statistical judgments?散点图中的异常值检测与剔除:异常值是否会影响直观的统计判断?
J Exp Psychol Hum Percept Perform. 2023 Jan;49(1):129-144. doi: 10.1037/xhp0001065. Epub 2022 Nov 17.
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Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation.眼见为信还是信以为见?信念偏差与相关性估计。
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Can humans perform mental regression on a graph? Accuracy and bias in the perception of scatterplots.人类能够对图表进行心理回归吗?散点图感知中的准确性和偏差。
Cogn Psychol. 2021 Aug;128:101406. doi: 10.1016/j.cogpsych.2021.101406. Epub 2021 Jun 29.
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The outlier paradox: The role of iterative ensemble coding in discounting outliers.离群值悖论:迭代集成编码在离群值折扣中的作用。
J Exp Psychol Hum Percept Perform. 2020 Nov;46(11):1267-1279. doi: 10.1037/xhp0000857. Epub 2020 Aug 6.
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The visual system does not compute a single mean but summarizes a distribution.视觉系统并非计算单个均值,而是对分布进行总结。
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One bad apple spoils the whole bushel: The neural basis of outlier processing.一个坏苹果烂一筐:异常值处理的神经基础。
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