Luminance-corrected Pupillometry for Reliable Effort-tracking in Dynamic Environments

Abstract

Pupillometry provides an objective way to index effort across domains. However, pupil size is strongly affected by luminance changes, which can obscure effort-related effects, and limit its use in most applied scenarios with dynamic visual input. We here introduce and validate a method to overcome this problem. To this end, participants performed an auditory n-back task of differing difficulty while viewing either constant visual input or dynamic driving movie clips. Effort was assessed physiologically (pupil size), behaviorally (accuracy), and subjectively (NASA-TLX). Accuracy, questionnaire scores, and pupil size were analyzed at the session level, while pupillometry additionally provided continuous time-resolved information. As expected, pupillometry tracked differences in effort, but its discriminability was substantially reduced under dynamic visual input. Correcting for the effects of overall luminance and moment-to-moment luminance changes using a dynamic, explainable, and open-source modeling procedure (Open-DPSM) considerably improved effort discriminability on both aggregate and time-resolved levels. At the aggregate level, luminance-corrected pupillometry slightly outperformed accuracy and NASA-TLX. Combining all three measures yielded the highest classification performance (AUC = 0.98), supporting the view that effort is multifaceted and best captured multimodally. These findings establish a practical basis for fine-grained physiological tracking of effort and arousal in both fundamental and applied research using complex, dynamic stimuli. A tutorial section guides researchers in applying luminance correction to their own pupillometric data in dynamic viewing environments using the here validated approach.