The effects of cognitive abilities and task demands on tonic and phasic pupil sizes

Aminihajibashi, Samira; Hagen, Thomas; Andreassen, Ole A.; Laeng, Bruno; Espeseth, Thomas

doi:10.1016/j.biopsycho.2020.107945

Cited by 34 publications

(42 citation statements)

References 98 publications

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“…But recently, it has become evident that pupil diameter can be used as a proxy for LC activity (Aston-Jones & Cohen, 2005;Joshi & Gold, 2020;Joshi, Li, Kalwani, & Gold, 2016;Varazzani, San-Galli, Gilardeau, & Bouret, 2015). Consistent with this notion, several recent studies have measured arousal regulation by measuring variability in pupil diameter and found relations among arousal regulation, long-term memory, attention control, sustained attention, working memory capacity, self-reported instances of mind-wandering and distraction, and self-reported media multitasking (Aminihajibashi et al, 2020;Aminihajibashi, Hagen, Foldal, Laeng, & Espeseth, 2019;Madore et al, 2020;Robison & Brewer, 2020, 2021Robison & Unsworth, 2019). Therefore, it appears that arousal regulation is a task-and domain-general individual difference that can impact a host of cognitive performance measures.…”

Section: Pupillary Correlates Of Individual Differences In Long-term Memorymentioning

confidence: 88%

“…Based on prior studies finding correlations between recall performance and pupillary measures of intensity of attention (Miller et al, 2019;Miller & Unsworth, 2020, 2021, we hypothesized that larger average evoked pupillary responses at encoding would correlate with better recall. Also, based on prior work showing correlations between arousal dysregulation and cognitive ability measures like attention control, working memory capacity, and recognition memory (Aminihajibashi et al, 2019(Aminihajibashi et al, , 2020Madore et al, 2020;Robison & Brewer, 2020;Robison & Unsworth, 2019;Unsworth & Robison, 2015, 2017b, we expected arousal dysregulation (i.e., more list-to-list variation in pupil diameter) to correlate with lower recall. As hypothesized, both arousal dysregulation (r = -0.37) and intensity of attention correlated with recall (r = 0.26).…”

Section: Discussionmentioning

confidence: 99%

“…Here we leverage pupillometry to investigate these two additional potential sources of interindividual variation in long-term memory: intensity of attention and arousal (dys)regulation Kahneman and Beatty (1966) were the first to demonstrate that pupil diameter was sensitive to the outlay of effort toward encoding and retrieving information in memory. A host of subsequent studies have also found that the pupil dilates in response to the encoding of information, either for maintenance in working memory (Alnaes et al, 2014;Aminihajibashi, Hagen, Andreassen, Laeng, & Espeseth, 2020;Heitz, Schrock, Payne, & Engle, 2008;Kursawe & Zimmer, 2015;Meghanathan, Leeuwen, & Nikolaev, 2015;Robison & Unsworth, 2019;Siegle, Steinhauer, Stenger, Konecky, & Carter, 2003;Unsworth & Robison, 2015 or later retrieval from long-term memory (Ariel & Castel, 2014;Gross & Dobbins, 2021;Kahneman & Peavler, 1969;Miller et al, 2019;Miller & Unsworth, 2020, 2021Papesh, Goldinger, & Hout, 2012;Peavler, 1974;. At the level of individual differences, Miller et al (2019) found a positive correlation between evoked pupillary responses during encoding and performance on delayed free recall.…”

Section: Pupillary Correlates Of Individual Differences In Long-term Memorymentioning

confidence: 96%

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Pupillary correlates of individual differences in long-term memory

Robison¹,

Trost²,

Schor

et al. 2021

Preprint

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The present study is the first to examine individual differences in long-term memory, arousal dysregulation, and intensity of attention within the same experiment. Participants (N = 106) completed 28 lists of an immediate free recall task while their pupil diameter was recorded via an eye-tracker during the encoding period. Two main pupillary measures were extracted: intraindividual variability in pre-list pupil diameter and evoked pupillary responses during item encoding. Variability in pre-list pupil diameter served as a measure of arousal dysregulation, and evoked pupillary responses served as a measure of intensity of attention. Based on prior work, we hypothesized that there would be a positive association between intensity of attention and recall ability, and that there would be a negative association between arousal dysregulation and recall ability. Collectively these two measures accounted for 19% of interindividual variance in recall, with 5% attributable uniquely to intensity of attention and 12% attributable uniquely to arousal regulation. The findings demonstrate that there are sources of individual differences in long-term memory that can be revealed via pupillometry, notably the amount of effort deployed during item encoding and the degree to which people exhibit dysregulated arousal. Both findings are consistent with recent theorizing regarding the role of the locus coeruleus-norepinephrine (LC-NE) system’s role in goal-directed cognition. Specifically, the LC governs both moment-to-moment arousal and NE release to cortical regions subserving cognitive processing. Among people for whom this system operates most optimally, long-term memory retention is superior.

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Section: Pupillary Correlates Of Individual Differences In Long-term Memorymentioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Pupillary Correlates Of Individual Differences In Long-term Memorymentioning

confidence: 96%

See 1 more Smart Citation

Pupillary correlates of individual differences in long-term memory

Robison¹,

Trost²,

Schor

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In future, only the neural network's input count need to be changed if more dependencies should be modelled since the base function has a sufficient degree of freedom for describing any temporal pupil response. The research applications in the field of pupillometry are highly interdisciplinary [80][81][82][83][84][85][86][87][88] across species 89 , covering the topic of clinical diagnostics 41,[90][91][92][93][94][95] , cognitive science [96][97][98][99][100][101][102][103] , neuroscience 104 , vision science 105,106 , autonomous nervous system [107][108][109] and quantification of the circadian photoentrainment 39,[110][111][112][113] . A reliable data-driven pupil model that integrates the findings of past years could also be an essential step forward for these research areas.…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based pupil model predicts time and spectral dependent light responses

Zandi

Khanh

2021

Sci Rep

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Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil’s time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 $$\pm$$ ± 1 K, 4983 $$\pm$$ ± 3 K, 10,138 $$\pm$$ ± 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m2. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.

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“…Since the early days of pupillary research (Reeves, 1918), the modeling of the pupil light response and its retinal processing path was the main focus of investigations . Additionally, the pupil diameter is used as a biomarker in research disciplines such as cognitive science (Aminihajibashi et al, 2020;Cherng et al, 2020;Clewett et al, 2020;Sibley et al, 2020), circadian photoentrainment (Münch et al, 2012;Bonmati-Carrion et al, 2016;Spitschan et al, 2019;Tähkämö et al, 2019;Van Egroo et al, 2019), clinical diagnostics (Lim et al, 2016;Joyce et al, 2018;Chougule et al, 2019) or neuroscience (Schwalm and Jubal, 2017;Carle et al, 2019). Pupil changes of 0.015 to 0.5 mm are the range of interest in such studies, leading to increased resolution and robustness requirements for pupil measurement equipment.…”

Section: Introductionmentioning

confidence: 99%

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

et al. 2021

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The human pupil behavior has gained increased attention due to the discovery of the intrinsically photosensitive retinal ganglion cells and the afferent pupil control path’s role as a biomarker for cognitive processes. Diameter changes in the range of 10–2 mm are of interest, requiring reliable and characterized measurement equipment to accurately detect neurocognitive effects on the pupil. Mostly commercial solutions are used as measurement devices in pupillometry which is associated with high investments. Moreover, commercial systems rely on closed software, restricting conclusions about the used pupil-tracking algorithms. Here, we developed an open-source pupillometry platform consisting of hardware and software competitive with high-end commercial stereo eye-tracking systems. Our goal was to make a professional remote pupil measurement pipeline for laboratory conditions accessible for everyone. This work’s core outcome is an integrated cross-platform (macOS, Windows and Linux) pupillometry software called PupilEXT, featuring a user-friendly graphical interface covering the relevant requirements of professional pupil response research. We offer a selection of six state-of-the-art open-source pupil detection algorithms (Starburst, Swirski, ExCuSe, ElSe, PuRe and PuReST) to perform the pupil measurement. A developed 120-fps pupillometry demo system was able to achieve a calibration accuracy of 0.003 mm and an averaged temporal pupil measurement detection accuracy of 0.0059 mm in stereo mode. The PupilEXT software has extended features in pupil detection, measurement validation, image acquisition, data acquisition, offline pupil measurement, camera calibration, stereo vision, data visualization and system independence, all combined in a single open-source interface, available at https://github.com/openPupil/Open-PupilEXT.

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The effects of cognitive abilities and task demands on tonic and phasic pupil sizes

Cited by 34 publications

References 98 publications

Pupillary correlates of individual differences in long-term memory

Pupillary correlates of individual differences in long-term memory

Deep learning-based pupil model predicts time and spectral dependent light responses

PupilEXT: Flexible Open-Source Platform for High-Resolution Pupillometry in Vision Research

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