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Assessing Pupil-linked Changes in Locus Coeruleus-mediated Arousal Elicited by Trigeminal Stimulation

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

Yuqing Cai¹, Christoph Strauch², Stefan Van der Stigchel²

¹Experimental Psychology, Helmholtz Institute, Faculty of Social Sciences, Utrecht University, Heidelberglaan 1, 3584, CS, Utrecht, The Netherlands. y.cai2@uu.nl.

Behavior Research Methods

|December 11, 2023

Summary

This summary is machine-generated.

This study introduces Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source toolkit. It accurately models pupil size changes from visual input, overcoming challenges from brightness and contrast effects.

Keywords:

Modeling pupil size Open-DPSM Pupillary light response Pupillometry

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Area of Science:

Cognitive Neuroscience
Computational Vision

Background:

Pupil size changes are sensitive indicators of cognitive and sensory processes.
Interpreting pupillometry data is challenging due to confounding low-level visual factors like luminance and contrast.
Existing models often lack the flexibility to address dynamic visual stimuli and real-world conditions.

Purpose of the Study:

To introduce Open Dynamic Pupil Size Modeling (Open-DPSM), an open-source computational toolkit.
To improve the accuracy of modeling pupil size changes in response to dynamic visual input.
To provide a tool that mitigates the influence of low-level visual features in pupillometry.

Main Methods:

Developed Open-DPSM using a convolution approach to model pupillary responses.
Incorporated modeling of luminance and contrast effects on pupil size.
Integrated weighing of visual event contributions across the visual field and gaze-contingent event extraction.
Implemented as Python functions and a graphical user interface (GUI).

Main Results:

Open-DPSM demonstrates improved prediction of pupil size changes compared to existing benchmarks.
The toolkit effectively models pupillary responses to dynamic changes in luminance and contrast.
The model accounts for the spatial distribution of visual events and gaze behavior.

Conclusions:

Open-DPSM offers a robust solution for analyzing pupillometry data in complex visual environments.
The open-source nature and GUI facilitate broader application and customization for researchers.
This tool enables more reliable interpretation of pupillometry by isolating cognitive effects from low-level visual influences.