Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization
Promoting Representational Fluency for Cognitive Bias Mitigation in Information Visualization
复制标题
促进表征流畅性以减轻信息可视化中的认知偏差
DOI：
10.1007/978-3-319-95831-6_10
复制
发表时间：
2018
期刊：
影响因子：
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通讯作者：
Paul C. Parsons
中科院分区：
文献类型：
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作者：
Paul C. Parsons
信息可视化涉及使用数据的视觉表示来放大认知。虽然视觉化通常会放大认知，但它们也有代表性的偏见，鼓励以某些方式进行思考和推理，而牺牲其他方式。我建议，可视化设计师和用户的发展代表流畅性可以帮助减轻这种偏见，促进可视化教育和实践中的代表流畅性可以是一个有用的一般策略，减轻认知偏见。来自不同学科的文献进行了讨论，包括元可视化，表征能力和元表征能力的观点。可视化研究，教育和实践的一些影响进行了检查。深入，努力的认知处理的用户参与的需要进行了讨论，并位于文献中建立的偏见缓解策略。还提出了包括五个挑战的初步研究议程。
Information visualization involves the use of visual representations of data to amplify cognition. While visualizations do generally amplify cognition, they also have representational biases that encourage thinking and reasoning in certain ways at the expense of others. I propose that the development of representational fluency by visualization designers and users can help mitigate such biases, and that promoting representational fluency in visualization education and practice can be a useful general strategy for mitigating cognitive biases. Literature from various disciplines is discussed, including perspectives on meta-visualization, representational competence, and meta-representational competence. Some implications for visualization research, education, and practice are examined. The need for engaging users in deep, effortful cognitive processing is discussed and is situated within literature on established bias-mitigating strategies. A preliminary research agenda comprising five challenges is also proposed.