Pupillary Light Reflex Correction for Robust Pupillometry in Virtual Reality

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Summary

This paper presents a method to correct for pupillary light reflex (PLR) artifacts in VR-based pupillometry, allowing more accurate isolation of cognitive load and emotional state signals from luminance-driven pupil changes. The approach has practical implications for using VR eye-tracking in cognitive performance assessment and potentially in lighting research where separating PLR from other pupil responses is critical.

Key Findings

Estimating luminance from a weighted average of the fixation area and background yielded the best PLR correction performance compared to other luminance estimation methods.
Calibration sequences using either solid gray or realistic scene brightness levels presented for 6 seconds in pseudo-random order proved most robust for individual luminance-to-pupil dilation mapping functions.
The method was validated in both a structured n-back cognitive task and free exploration of a 6-degrees-of-freedom VR scene, demonstrating generalizability across VR interaction types.

Categories

The Science of Light: Investigates pupillary light reflex calibration methods to isolate luminance-driven from cognitively-driven pupil size changes, directly relevant to photoreceptor and pupillometric science.

Workplace Performance: The corrected pupillometry technique enables real-time measurement of cognitive load and emotional state during VR tasks, with implications for attention and performance monitoring in applied settings.