Toward Personal Intelligence Through Cooperative Observation
研究通过合作观察方法,解决个人AI系统因观察瓶颈导致的用户模型质量受限问题,提升AI对用户的理解和帮助。
研究通过合作观察方法,解决个人AI系统因观察瓶颈导致的用户模型质量受限问题,提升AI对用户的理解和帮助。
This study investigates how “juiciness”—i.e., rich, responsive visual feedback—in interactive infographics affects user engagement and short-term information retention. Through three web-based A/B experiments, we employed multimodal measures including eye tracking, clickstream analysis, free-recall tests, and multiple-choice assessments to systematically compare juicy (high-feedback) and dry (minimal-feedback) designs. Results reveal a non-monotonic effect of juiciness on retention: moderate juiciness improves recall of textual and graphical content, whereas excessive juiciness can impede cognitive processing—e.g., the dry version of *Burcalories* yielded an 11.4% higher multiple-choice accuracy. Juicy designs increased average engagement by 7.2%, though gains were highly task-dependent. We propose a “engagement–usability trade-off” design principle and provide empirical evidence and a theoretical framework for quantitatively calibrating feedback intensity in interactive visualizations.
研究通过合作观察方法,解决个人AI系统因观察瓶颈导致的用户模型质量受限问题,提升AI对用户的理解和帮助。
This study investigates how “juiciness”—i.e., rich, responsive visual feedback—in interactive infographics affects user engagement and short-term information retention. Through three web-based A/B experiments, we employed multimodal measures including eye tracking, clickstream analysis, free-recall tests, and multiple-choice assessments to systematically compare juicy (high-feedback) and dry (minimal-feedback) designs. Results reveal a non-monotonic effect of juiciness on retention: moderate juiciness improves recall of textual and graphical content, whereas excessive juiciness can impede cognitive processing—e.g., the dry version of *Burcalories* yielded an 11.4% higher multiple-choice accuracy. Juicy designs increased average engagement by 7.2%, though gains were highly task-dependent. We propose a “engagement–usability trade-off” design principle and provide empirical evidence and a theoretical framework for quantitatively calibrating feedback intensity in interactive visualizations.