Sumários
Disinformation, Trust, and Ethical Data Communication
20 Abril 2026, 19:30 • Tomás Almeida e Silva Martins Alves
Building on the affective and cognitive mechanisms introduced previously, this class focuses on how visualizations can intentionally exploit human biases to mislead. First, we introduce trust as a central construct in visualization. We distinguish cognitive and affective trust, analyze how design features influence credibility and confidence, and discuss how trust can become miscalibrated (either inflated or eroded) by poor or deceptive practices. From there, we examine how common disinformation strategies that exploit design flaws and reasoning errors can be introduced in data visualization design and deployment. The goal is not only to recognize misleading graphics but to design visualizations that responsibly calibrate trust, communicate uncertainty, and support sound decisions. The class concludes with an exercise where students first design a misleading or sensationalist visualization. Then, another student redesigns it into a transparent, trust-calibrated alternative and justify their design choices.
Disinformation, Trust, and Ethical Data Communication
20 Abril 2026, 18:00 • Tomás Almeida e Silva Martins Alves
Building on the affective and cognitive mechanisms introduced previously, this class focuses on how visualizations can intentionally exploit human biases to mislead. First, we introduce trust as a central construct in visualization. We distinguish cognitive and affective trust, analyze how design features influence credibility and confidence, and discuss how trust can become miscalibrated (either inflated or eroded) by poor or deceptive practices. From there, we examine how common disinformation strategies that exploit design flaws and reasoning errors can be introduced in data visualization design and deployment. The goal is not only to recognize misleading graphics but to design visualizations that responsibly calibrate trust, communicate uncertainty, and support sound decisions. The class concludes with an exercise where students first design a misleading or sensationalist visualization. Then, another student redesigns it into a transparent, trust-calibrated alternative and justify their design choices.
Individual Differences and User-Adaptive Data Visualization
13 Abril 2026, 19:30 • Tomás Almeida e Silva Martins Alves
This class examined why data visualizations are not interpreted uniformly across viewers and how user-adaptive data visualization systems are built. Rather than treating charts as neutral representations, we approached them as information vehicles whose meaning depends on perceptual constraints, reasoning strategies, and individual differences such as visualization literacy, personality, prior beliefs, and cognitive traits. We began with the perceptual and cognitive foundations of visual interpretation, covering how encoding choices, psychological constructs and traits, and literacy affect what people notice, compare, and remember. We then analyze systematic biases that influence judgment even when the data are accurate, including anchoring effects, framing effects, and the phantom effect. The class concluded by examining how these user-adaptive data visualization systems are evaluated and performing a user study to assess the impact of visualization literacy on perceived readability evaluations of charts varying in visual complexity.
Individual Differences and User-Adaptive Data Visualization
13 Abril 2026, 18:00 • Tomás Almeida e Silva Martins Alves
This class examined why data visualizations are not interpreted uniformly across viewers and how user-adaptive data visualization systems are built. Rather than treating charts as neutral representations, we approached them as information vehicles whose meaning depends on perceptual constraints, reasoning strategies, and individual differences such as visualization literacy, personality, prior beliefs, and cognitive traits. We began with the perceptual and cognitive foundations of visual interpretation, covering how encoding choices, psychological constructs and traits, and literacy affect what people notice, compare, and remember. We then analyze systematic biases that influence judgment even when the data are accurate, including anchoring effects, framing effects, and the phantom effect. The class concluded by examining how these user-adaptive data visualization systems are evaluated and performing a user study to assess the impact of visualization literacy on perceived readability evaluations of charts varying in visual complexity.
Aula 2
23 Fevereiro 2026, 19:30 • Elsa Cardoso
Fundamentos de Visualização de Informação para Tomada de Decisão