Biology subjects

Coutinho, J. D.

Publications and source records attributed to Coutinho, J. D..

3 recordsLinked to original sources

bioRxiv2024

Main Sequence of Human Luminance-Evoked Pupil Dynamics

Pupil responses are commonly used to provide insight into visual perception, autonomic control, cognition, and various brain disorders. However, making inferences from pupil data can be complicated by nonlinearities in pupil dynamics and variability within and across individuals, which challenge the assumptions of linearity or group level homogeneity required for common analysis methods. In this study, we evaluated luminance evoked pupil dynamics in young healthy adults (N=10, 5:5 M:F, ages 19-25) by identifying nonlinearities, variability, and conserved relationships across individuals to improve the ability to make inferences from pupil data. We found a nonlinear relationship between final pupil diameter and luminance, linearized by considering the logarithm of luminance. Peak diameter change and peak velocity were nonlinear functions of log-luminance for constriction but not dilation responses. Across participants, curve fit parameters characterizing pupil responses as a function of luminance were highly variable, yet there was an across-participant linear correlation between overall pupil size and pupil gain (i.e. diameter change per unit log-luminance change). In terms of within-participant trial-by-trial variability, participants showed greater variability in final pupil size compared to constriction peak diameter change as a function of log-luminance. Despite the variability in stimulus-response metrics within and across participants, we found that all participants showed a highly stereotyped "main sequence" relationship between peak diameter change and peak velocity (independent of luminance). The main sequence relationship can be used to inform models of the neural control of pupil dynamics and as an empirical analysis tool to evaluate variability and abnormalities in pupil behaviour.

neuroscience↗

bioRxiv2024

Timing and Amplitude of Catch-up Saccades to Accelerating Targets

To track moving targets, humans move their eyes using both saccades and smooth pursuit. If pursuit eye movements fail to accurately track the moving target, catch-up saccades are initiated to rectify the tracking error. It is well known that retinal position and velocity errors determine saccade timing and amplitude, but the extent to which retinal acceleration error influences these aspects is not well quantified. To test this, 13 adult human participants performed an experiment where they pursued accelerating / decelerating targets. During ongoing pursuit, we introduced a randomly sized target step to evoke a catch-up saccade and analyzed its timing and amplitude. We observed that retinal acceleration error was a statistically significant predictor of saccade amplitude and timing. A multiple linear regression supported our hypothesis that retinal acceleration errors influence saccade amplitude in addition to the influence of retinal position and velocity errors. We also found that saccade latencies were shorter when retinal acceleration error increased the tracking error and vice versa. In summary, our findings support a model in which retinal acceleration error is used to compute a predicted position error [~]100ms into the future to trigger saccades and determine saccade amplitude. Significance statementWhen visually tracking object motion, humans combine smooth pursuit and saccadic eye movements to maintain the target image on the fovea. Retinal position and velocity errors are known to determine catch-up saccade amplitude and timing, however it is unknown if retinal acceleration error is also used to predict future target position. This study provides evidence of a small but statistically significant contribution of retinal acceleration error in determining saccade amplitude and timing.

neuroscience↗

bioRxiv2018

Confidence in predicted position error explains saccadic decisions during 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 occurrence 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 probability of occurrence and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions about the influence of sensory uncertainty and target motion parameters on saccade decisions. We suggest that this predictive, confidence-based decision making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.\n\nNew & NoteworthyThis is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade probability and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.

neuroscience↗