LAUSR

Abstract Detecting change points is crucial in analyzing time series data and single-subject designs. This study investigates factors influencing change point detection through visual perception by employing a visual inference experiment. Participants were tasked with selecting the scatterplot that appeared most different from the surrounding plots. The “different” plot was generated to have a shift in the vertical direction compared to its counterparts with no vertical shift. We used a factorial experiment with a balanced incomplete block design to assign factor combinations for participants to evaluate. Furthermore, we compared the performance of visual accuracy to conventional change point detection methods. Participants were found to identify higher shift magnitudes more accurately than lower shift magnitudes, consistent with conventional methods. Change point data with higher variances had lower identification rates. Direction and change point location effects impacted identifying change point scenarios with lower signal-to-noise ratios. Participants indicated varying reasons for selection across correct and incorrect data plot identifications. Additionally, confidence in selection was positively associated with identification accuracy for change plots with higher signal-to-noise ratios. These insights highlight the complexities of change point detection through visual inference and emphasize the multifaceted nature of human perception in identifying subtle changes within data. Supplementary materials for this article are available online.