Arousal level changes constantly and it has a profound influence on performance during everyday activities. Fluctuations in arousal are regulated by the autonomic nervous system, which is mainly controlled by the balanced activity of the parasympathetic and sympathetic systems, commonly indexed by heart rate (HR) and galvanic skin response (GSR), respectively. Although a growing number of studies have used pupil size to indicate the level of arousal, research that directly examines the relationship between pupil size and HR or GSR is limited. The goal of this study was to understand how pupil size is modulated by autonomic arousal. Human participants fixated various emotional face stimuli, of which low-level visual properties were carefully controlled, while their pupil size, HR, GSR, and eye position were recorded simultaneously. We hypothesized that a positive correlation between pupil size and HR or GSR would be observed both before and after face presentation. Trial-by-trial positive correlations between pupil diameter and HR and GSR were found before face presentation, with larger pupil diameter observed on trials with higher HR or GSR. However, task-evoked pupil responses after face presentation only correlated with HR. Overall, these results demonstrated a trial-by-trial relationship between pupil size and HR or GSR, suggesting that pupil size can be used as an index for arousal level involuntarily regulated by the autonomic nervous system.

For humans, visual tracking of moving stimuli often triggers catch-up saccades during smooth pursuit. The switch between these continuous and discrete eye movements is a trade-off between tolerating sustained position error (PE) when no saccade is triggered or a transient loss of vision during the saccade due to saccadic suppression. de Brouwer et al. (2002b) demonstrated that catch-up saccades were less likely to occur when the target re-crosses the fovea within 40-180 ms. To date, there is no mechanistic explanation for how the trigger decision is made by the brain. Recently, we proposed a stochastic decision model for saccade triggering during visual tracking (Coutinho et al., 2018) that relies on a probabilistic estimate of predicted PE (PE pred). Informed by model predictions, we hypothesized that saccade trigger time length and variability will increase when pre-saccadic predicted errors are small or visual uncertainty is high (e.g., for blurred targets). Data collected from human participants performing a double step-ramp task showed that large pre-saccadic PE pred (Ͼ10°) produced short saccade trigger times regardless of the level of uncertainty while saccade trigger times preceded by small PE pred (Ͻ10°) significantly increased in length and variability, and more so for blurred targets. Our model also predicted increased signal-dependent noise (SDN) as retinal slip (RS) increases; in our data, this resulted in longer saccade trigger times and more smooth trials without saccades. In summary, our data supports our hypothesized predicted error-based decision process for coordinating saccades during smooth pursuit.

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood and this decision process remains imprecisely quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioural observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.

10A fundamental problem in motor control is the coordination of complementary movement types to 11 achieve a common goal. As a common example, humans view moving objects through coordinated 12 pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image 13 velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete 14 execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and 15 velocity errors and the timing of saccades can be highly variable. The observed distributions of saccade 16 occurrence and trigger time remain poorly understood and this decision process remains imprecisely 17 quantified. Here we propose a predictive, probabilistic model explaining the decision to trigger saccades 18 during pursuit to foveate moving targets. In this model, expected position error and its associated 19 uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations 20 (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is 21 needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed 22 threshold. The model qualitatively explains behavioural observations on the probability of occurrence and 23 trigger time distributions of saccades during pursuit over a range of target motion trajectories.24 Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target 25 motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision 26 making strategy represents a fundamental principle for the probabilistic neural control of coordinated 27 movements. 28 New & Noteworthy 29This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting 30 for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively 31 estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a 32 threshold. This model explains saccade probability and trigger time distributions across target trajectories 33 and makes novel predictions about the influence of sensory uncertainty in saccade decisions during 34 pursuit.The coordination between continuously controlled and discretely triggered movements to achieve 37 a common goal remains a fundamental problem in neuroscience. This coordinated motor control is 38 exemplified in the pursuit and saccadic eye movements that humans perform when tracking moving 39 objects. Pursuit eye movements are continuously controlled to minimize the relative motion of a visual 40 image on the retina (Keller and Heinen, 1991; Lisberger, 2015). Due to noise and delays prevalent in 41 sensorimotor systems (Faisal et al., 2008; van Beers et al., 2002), pursuit trajectory may deviate from and 42 lag behind the true target trajectory (Osborne et al., 2005). Furthermore, pursuit gain in humans is 43 typically less than ...

No abstract