Effect of Prior Direction Expectation on the Accuracy and Precision of Smooth Pursuit Eye Movements

Abstract

The integration of sensory with top-down cognitive signals for generating appropriate sensory-motor behaviors is an important issue in understanding the brain's information processes. Recent studies have demonstrated that the interplay between sensory and high-level signals in oculomotor behavior could be explained by Bayesian inference. Specifically, prior knowledge for motion speed introduces a bias in the speed of smooth pursuit eye movements. The other important prediction of Bayesian inference is variability reduction by prior expectation; however, there is insufficient evidence in oculomotor behaviors to support this prediction. In the present study, we trained monkeys to switch the prior expectation about motion direction and independently controlled the strength of the motion stimulus. Under identical sensory stimulus conditions, we tested if prior knowledge about the motion direction reduced the variability of open-loop smooth pursuit eye movements. We observed a significant reduction when the prior expectation was strong; this was consistent with the prediction of Bayesian inference. Taking advantage of the open-loop smooth pursuit, we investigated the temporal dynamics of the effect of the prior to the pursuit direction bias and variability. This analysis demonstrated that the strength of the sensory evidence depended not only on the strength of the sensory stimulus but also on the time required for the pursuit system to form a neural sensory representation. Finally, we demonstrated that the variability and directional bias change by prior knowledge were quantitatively explained by the Bayesian observer model.

Prior expectation enhances sensorimotor behavior by modulating population tuning and subspace activity in the sensory cortex

“…In this study, single neuronal activity was recorded from area MT of two rhesus monkeys performing a smooth pursuit eye movement task wherein the strength of the sensory motion information and prior knowledge of motion direction were controlled. Consistent with the findings of our previous behavioral study 35 , the variation in pursuit directions across trials was reduced by prior expectations only when the sensory input was weak and imprecise. The neural recordings indicated that prior expectations systematically reduced the MT neural responses in a manner that sharpened the population direction tuning curve only when the sensory evidence was weak.…”

Retinal error signals and fluctuations in eye velocity influence oculomotor behavior in subsequent trials

“…However, there were clear systematic differences in the test trials depending on the type and contrast of prior trials, which adds to the view of a reliability-weighted integration of prior information with the current sensory input in the oculomotor system. Such an integration of prior information cannot only reduce the noise and lead to more accurate pursuit responses ( Kim et al, 2019 ), but over multiple trials can also produce anticipatory behavior to minimize delays and error signals within the oculomotor system ( Kowler, Aitkin, Ross, Santos & Zhao, 2014 ; Kowler, Rubinstein, Santos & Wang, 2019 ). Previous related studies investigated this reliability-weighted integration by varying the reliability of the current sensory input and investigating the influence of comparable priors ( Darlington et al, 2017 ; Deravet et al, 2018 ).…”

“…prior experience of where a ball will go and the available visual signals) has had a lot of success with explaining human eye movement behavior ( Yang & Lisberger, 2012 ; Bogadhi, Montagnini & Masson, 2013 ; Orban de Xivry, Coppe, Blohm & Lefèvre, 2013 ). Recent studies ( Darlington, Tokiyama & Lisberger, 2017 ; Deravet, Blohm, de Xivry & Lefèvre, 2018 ; see Kim, Park, & Lee, 2019 for an example with target direction) demonstrated that the velocity of a target in the previous trial has a systematic effect on the oculomotor behavior in the following trial. When comparing the pursuit response to a target movement based on a faster or slower previous trial, the faster previous trial led to a faster pursuit response in the next trial, despite the same sensory input.…”

Predicting Trial-by-Trial Variation in Oculomotor Behavior Using Multivariate Electroencephalography Theta Phase

“…1B). Then, we decomposed the open-loop period of pursuit (approximately the first 100 ms of smooth pursuit from the average pursuit latency; [24], [25]) into speed, direction, and latency components in individual trials using a method described previously [11], [12], [26]. Briefly, we averaged horizontal and vertical velocity traces from all trials in each direction condition and determined the mean pursuit latency by visually inspecting the average velocity traces.…”

“…Finally, we removed ICs classified as artifacts using the ADJUST EEGLab plugin [35]. We band-pass filtered the preprocessed EEG signal into theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma . We epoched the preprocessed and filtered signals from −400 to 600 ms relative to the visual stimulus onset in each trial.…”