How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots
How Can Deep Neural Networks Aid Visualization Perception Research? Three Studies on Correlation Judgments in Scatterplots
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DOI：
10.1145/3544548.3581111
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发表时间：
2023-04
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影响因子：
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通讯作者：
Fumeng Yang;Yuxin Ma;Lane Harrison;J. Tompkin;D. Laidlaw
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文献类型：
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作者：
Fumeng Yang;Yuxin Ma;Lane Harrison;J. Tompkin;D. Laidlaw
深度神经网络如何帮助可视化感知研究是一个悬而未决的问题。本文从三个角度提供了见解-预测，泛化和解释-通过训练和分析三项研究中散点图中人类相关性判断的深度卷积神经网络。第一项研究评估了二十九种神经网络架构在预测人类判断方面的准确性，发现架构的一个子集（例如，VGG-19）具有与先前研究中表现最好的回归分析相当的准确性。第二项研究表明，从第一项研究中得到的模型显示更好的泛化能力比以前的模型在其他两个不同的散点图设计的判断数据集。第三项研究解释了卷积神经网络模型学习的视觉特征，提供了关于模型如何进行预测的见解，并确定了可以在人类相关感知研究中研究的潜在特征。总之，本文表明，深度神经网络可以作为可视化感知研究人员设计潜在实证研究设计和假设永久判断的工具。预印本、数据、代码和培训日志可在https://doi.org/10.17605/osf.io/exa8m上获得。
How deep neural networks can aid visualization perception research is a wide-open question. This paper provides insights from three perspectives—prediction, generalization, and interpretation—via training and analyzing deep convolutional neural networks on human correlation judgments in scatterplots across three studies. The first study assesses the accuracy of twenty-nine neural network architectures in predicting human judgments, finding that a subset of the architectures (e.g., VGG-19) has comparable accuracy to the best-performing regression analyses in prior research. The second study shows that the resulting models from the first study display better generalizability than prior models on two other judgment datasets for different scatterplot designs. The third study interprets visual features learned by a convolutional neural network model, providing insights about how the model makes predictions, and identifies potential features that could be investigated in human correlation perception studies. Together, this paper suggests that deep neural networks can serve as a tool for visualization perception researchers in devising potential empirical study designs and hypothesizing about perpetual judgments. The preprint, data, code, and training logs are available at https://doi.org/10.17605/osf.io/exa8m.