Modeling pupil responses to rapid sequential events

Abstract

Pupil size is an easily accessible, noninvasive online indicator of various perceptual and cognitive processes. Pupil measurements have the potential to reveal continuous processing dynamics throughout an experimental trial, including anticipatory responses. However, the relatively sluggish (~2 s) response dynamics of pupil dilation make it challenging to connect changes in pupil size to events occurring close together in time. Researchers have used models to link changes in pupil size to specific trial events, but such methods have not been systematically evaluated. Here we developed and evaluated a general linear model (GLM) pipeline that estimates pupillary responses to multiple rapid events within an experimental trial. We evaluated the modeling approach using a sample dataset in which multiple sequential stimuli were presented within 2-s trials. We found: (1) Model fits improved when the pupil impulse response function (PuRF) was fit for each observer. PuRFs varied substantially across individuals but were consistent for each individual. (2) Model fits also improved when pupil responses were not assumed to occur simultaneously with their associated trial events, but could have non-zero latencies. For example, pupil responses could anticipate predictable trial events. (3) Parameter recovery confirmed the validity of the fitting procedures, and we quantified the reliability of the parameter estimates for our sample dataset. (4) A cognitive task manipulation modulated pupil response amplitude. We provide our pupil analysis pipeline as open-source software (Pupil Response Estimation Toolbox: PRET) to facilitate the estimation of pupil responses and the evaluation of the estimates in other datasets.

Separable pupillary signatures of perception and action during perceptual multistability

“…For the pupil analyses we used a different type of GLM approach that aims to reconstruct the event response using a set of basis functions (Friston et al 1998). This approach strikes a balance between deconvolution analyses and GLMs that are based on a standard response function (a pupil response function in this case; Hoeks & Levelt 1993; De Gee et al 2012; Denison et al 2020). The former have a high degree of flexibility in terms of the response shapes they can reconstruct, at the expense of many degrees of freedom (as many as there are time points in the reconstructed response).…”

A dynamic normalization model of temporal attention

“…A neurophysiological AB model proposes that the AB results from a refractory period in the release of norepinephrine by the locus coeruleus (LC), which limits norepinephrine-driven gain enhancement across time 89 . Future work should examine how voluntary temporal attention affects LC activity; so far there is no evidence that pupil responses, which are influenced by LC, depend on voluntary temporal attention 90 .…”

A unified model of the task-evoked pupil response

“…Specifically, existing models (Fig. 1A) assume that pupil size is the output of a dilatory low-pass filter, which operates on a series of impulses aligned with task events [34, 35, 36, 37, 38, 39, 40, 41, 42]. Arousal level, estimated as the amplitude of these inputs, is estimated using linear regression.…”

“…( A ) Consensus model. Arousal-related input (impulses at stimulus onset and reaction time plus a time-on-task boxcar) is filtered by a dilatory low-pass filter, predicting pupil size on a single trial [34, 35, 36, 38, 42]. ( B ) Linear-nonlinear linking model.…”

“…Specifically, saccade occurrence is suppressed nearly completely at trial onset, resumes quickly thereafter, and then is more slowly suppressed again in anticipation of the next trial [68, 69, 70, 71, 67, 60, 72]. The magnitude of this oculomotor suppression has been linked to temporal attention [60, 73], temporal expectation [15, 72, 73], and task difficulty [67, 74, 75], task variables that have also been linked to pupil responses [13, 44, 17, 76, 42].…”