Temporal Expectation Indexed by Pupillary Response

Abstract

Forming temporal expectations plays an instrumental role for the optimization of behavior and allocation of attentional resources. Although the effects of temporal expectations on visual attention are well-established, the question of whether temporal predictions modulate the behavioral outputs of the autonomic nervous system such as the pupillary response remains unanswered. Therefore, this study aimed to obtain an online measure of pupil size while human participants were asked to differentiate between visual targets presented after varying time intervals since trial onset. Specifically, we manipulated temporal predictability in the presentation of target stimuli consisting of letters which appeared after either a short or long delay duration (1.5 vs. 3 s) in the majority of trials (75%) within different test blocks. In the remaining trials (25%), no target stimulus was present to investigate the trajectory of preparatory pupillary response under a low level of temporal uncertainty. The results revealed that the rate of preparatory pupillary response was contingent upon the time of target appearance such that pupils dilated at a higher rate when the targets were expected to appear after a shorter as compared to a longer delay period irrespective of target presence. The finding that pupil size can track temporal regularities and exhibit differential preparatory response between different delay conditions points to the existence of a distributed neural network subserving temporal information processing which is crucial for cognitive functioning and goal-directed behavior.

Neural repetition suppression modulates time perception: Evidence from electrophysiology and pupillometry

“…While we did not record EEG in Experiment 2, we explored the use of pupillary responses at interval offset as a potential measure to dissociate sensory magnitude effects from arousal. In particular, we assessed whether marker changes affected pupil sizes, either by causing larger pupil dilation, a signature of arousal or surprise (Akdoğan et al, 2016; Braem et al, 2015; Satterthwaite et al, 2007), or by evoking stronger constriction responses which are associated with stronger pre-attentive sensory processing (Binda et al, 2013; Mathôt et al, 2014; Naber et al, 2013).…”

“…Accompanying the behavioral results, we analyzed pupil size as another proxy for sensory magnitude and arousal. That is, previous work has shown that bottom-up stimulus strength can affect the pupil constriction response (Binda, Pereverzeva, & Murray, 2013; Mathôt, Dalmaijer, Grainger, & Stigchel, 2014; Naber, Frässle, Rutishauser, & Einhäuser, 2013), whereas dilation is often used as a signature of arousal or surprise (Akdoğan, Balcı, & Rijn, 2016; Braem, Coenen, Bombeke, van Bochove, & Notebaert, 2015; Satterthwaite et al, 2007).…”

Motivation alters implicit temporal attention through sustained and transient mechanisms: A behavioral and pupillometric study

“…This casts doubts on the exact mechanisms that are reflected in the prestimulus pupil size. With regard to temporal preparation, several studies have found increased pupil diameter prior to target onset in fixed foreperiod tasks with predictable target timing (Akdoǧan & van Rijn, 2016;Unsworth et al, 2018;Van der Molen, Boomsma, Jennings, & Nieuwboer, 1989). In contrast, prestimulus pupil increase in variable foreperiod paradigms is either smaller compared to fixed foreperiod tasks (Jennings, Molen, & Steinhauer, 1998) or unreported (Unsworth et al, 2018).…”

“…In particular, prestimulus pupil diameter in anticipation of a target has been associated with proactive, preparatory attention, whereas phasic poststimulus pupil responses may reflect reactive attention (Chatham, Frank, & Munakata, ; Chiew & Braver, ). Prestimulus pupil diameter is found to increase prior to stimulus onset in predictable timing paradigms (Akdoǧan & van Rijn, ; Unsworth, Robison, & Miller, ), reflecting attentional readiness. Moreover, reward manipulations have been shown to increase prestimulus pupil size (Chiew & Braver, ), possibly reflecting increased proactive attention.…”

CHAP: Open-source software for processing and analyzing pupillometry data

“…1. Exclusion of outlier samples (Akdoğan, Balcı, & van Rijn, 2016;Cohen et al, 2015;Wainstein et al, 2017). CHAP converts pupil size data to z score values based on the mean and standard deviation (SD) calculated for each trial separately.…”