Anomaly Detection in Interactive Visualizations of Bivariate Time Series Data: Modeling Human Accuracy and Confidence

Human-in-the-loop anomaly detection in visualized data, where analysts visually inspect multivariate time series to spot changes in inter-variable relationships such as shifts in correlations or model fit, remains vital when domain expertise must complement automated analysis. Yet, the perceptual and cognitive mechanisms that enable or may bias this task are not well understood, limiting the design of intelligent visual interfaces. We conducted an online experiment with 212 participants who examined line-plot visualizations to detect anomalies arising from changes in the generative mechanism of a dependent variable. We modeled both detection accuracy and self-reported confidence using a combination of data-related metrics (information-theoretic complexity, similarity via correlation and mutual information, and exploratory psychophysical “JND-like” indicators) and user-related traits (cognitive style and subjective numeracy). Results show that chart complexity and similarity have a strong influence on anomaly detection, while individual differences further modulate both accuracy and confidence. These predictive models provide insight into the perceptual and cognitive processes underlying visual analysis, suggesting paths toward adaptive, user-aware interfaces that support decision-making regarding dynamic processes.