TY - JOUR

T1 - From pre-processing to advanced dynamic modeling of pupil data

AU - Fink, Lauren

AU - Simola, Jaana

AU - Tavano, Alessandro

AU - Lange, Elke

AU - Wallot, Sebastian

AU - Laeng, Bruno

N1 - Funding Information: Open Access funding enabled and organized by Projekt DEAL. This project is supported by the Max Planck Society, Germany. SW acknowledges support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project numbers 397523278 and 442405919. Publisher Copyright: © The Author(s) 2023.

PY - 2024/3

Y1 - 2024/3

N2 - The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants’ pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

AB - The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants’ pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

KW - Convolution

KW - Correlation

KW - Phase coherence

KW - Recurrence

KW - Regression

KW - Scale-free dynamics

KW - Psychology

UR - http://www.scopus.com/inward/record.url?scp=85163196837&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/19106a41-a8ae-3c7b-ad5b-9ea195cc3986/

U2 - 10.3758/s13428-023-02098-1

DO - 10.3758/s13428-023-02098-1

M3 - Journal articles

C2 - 37351785

AN - SCOPUS:85163196837

VL - 56

SP - 1376

EP - 1412

JO - Behavior Research Methods

JF - Behavior Research Methods

SN - 1554-351X

IS - 3

ER -