AI 中文总结
研究旨在填补可视化解释机制空白，用主动推理将认知理论转化为模拟。以柱状图平均估计任务为例实现两类主体，分析其易出现的偏差及产生的认知痕迹，为可视化解释机制假设提供框架，支持更优的计算机模拟评估。
AI 中文摘要
实证用户研究对于评估视觉编码至关重要，能揭示感知和认知机制，但无法提供因果性、预测性的解释错误说明。评估往往是事后的。为填补这一机制空白，我们利用主动推理将可视化解释的认知理论转化为可执行模拟。我们将图表阅读建模为动态视觉搜索，其中主体更新信念并选择平衡不确定性降低与认知努力的行动。作为概念验证，我们为柱状图平均估计任务实现了快速启发式（类型1）和慢速分析式（类型2）主体。快速主体易受刻度显著性偏差影响，慢速主体更易受工作记忆衰退影响。两者都产生可检查的认知痕迹。该架构为形式化和测试可视化解释的机制假设提供了框架，实证研究可对这些模拟进行参数化、完善或证伪，支持对可视化效果进行更早、更具预测性的计算机模拟评估。
英文摘要
Empirical user studies are essential for evaluating visual encodings and can reveal perceptual and cognitive mechanisms, but they do not by themselves provide causal, predictive accounts of interpretation errors. Evaluations are therefore often post hoc: they measure performance after a design has been specified rather than predicting how attention, uncertainty, memory, and bias may produce accurate or erroneous judgments. To address this mechanistic gap, we translate a cognitive theory of visualization interpretation into executable simulation using Active Inference, a probabilistic framework for perception, learning, and action. We model chart reading as dynamic visual search in which agents update beliefs and choose actions that balance uncertainty reduction against cognitive effort. As a proof of concept, we implement Fast, heuristic (Type 1) and Slow, analytic (Type 2) agents for a bar-chart average-estimation task. The Fast agent is vulnerable to tick-salience bias, whereas the Slow agent is more vulnerable to working-memory decay. Both produce inspectable cognitive traces, including evolving belief uncertainty and fixation sequences. By expressing these hypothesized failure mechanisms as interpretable parameters, the architecture provides a framework for formalizing and testing mechanistic hypotheses about visualization interpretation. Empirical studies can then parameterize, refine, or falsify these simulations, supporting earlier and more predictive in silico evaluation of visualization efficacy.
