Introducing Open-DPSM: An open-source toolkit for modeling pupil size changes to dynamic visual inputs

Pupil size change is a widely adopted, sensitive indicator for sensory and cognitive processes. However, the interpretation of these changes is complicated by the influence of multiple low-level effects, such as brightness or contrast changes, posing challenges to applying pupillometry outside of extremely controlled settings. We introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit to model pupil size changes to dynamically changing visual inputs using a convolution approach. Building on and extending previous models, we successfully modeled pupillary responses to luminance and contrast changes in movies. We further improved the model by weighing the distinct contributions of the visual events across the visual field on pupil size change and incorporating gaze-contingent visual event extraction and modeling. Open-DPSM is fully available to the community with Python scripts and an accessible graphical user interface (see GitHub page: https://github.com/caiyuqing/Open-DPSM), enabling the extension of its applications to versatile scenarios. By obtaining a predicted pupil trace with dynamic visual stimuli and unconstrained eye movements, users can mitigate the effects of low-level features by subtracting the predicted trace, or assess the efficacy of the low-level feature manipulations a priori by comparing estimated traces across conditions.