Trust is an essential aspect of data visualization, as it plays a crucial role in the interpretation and decision-making processes of users. While research in social sciences outlines the multi-dimensional factors that can play a role in trust formation, most data visualization trust researchers employ a single-item scale to measure trust. We address this gap by proposing a comprehensive, multidimensional conceptualization and operationalization of trust in visualization. We do this by applying general theories of trust from social sciences, as well as synthesizing and extending earlier work and factors identified by studies in the visualization field. We apply a two-dimensional approach to trust in visualization, to distinguish between cognitive and affective elements, as well as between visualization and data-specific trust antecedents. We use our framework to design and run a large crowd-sourced study to quantify the role of visual complexity in establishing trust in science visualizations. Our study provides empirical evidence for several aspects of our proposed theoretical framework, most notably the impact of cognition, affective responses, and individual differences when establishing trust in visualizations.

翻译：信任是数据可视化中的一个关键方面，它在用户的解释和决策过程中起着至关重要的作用。尽管社会科学研究概述了可能影响信任形成的多维因素，但大多数数据可视化信任研究者仍采用单一项目量表来衡量信任。我们通过提出一个全面的、多维度的可视化信任概念化和操作化来填补这一空白。为此，我们应用社会科学中关于信任的一般理论，同时综合并扩展了可视化领域早期研究及已被识别出的因素。我们采用二维方法来处理可视化中的信任，以区分认知和情感元素，以及可视化与数据特定信任前因。我们利用该框架设计并实施了一项大规模众包研究，以量化视觉复杂性在建立科学可视化信任中的作用。我们的研究为我们提出的理论框架的多个方面提供了实证证据，尤其是在建立可视化信任时，认知、情感反应以及个体差异的影响。