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Background Caffeine is a widely used dietary stimulant that can reverse the effects of fatigue on cognitive, motor and oculomotor function. However, few studies have examined the effect of caffeine on the oculomotor system when homeostasis has not been disrupted by physical fatigue. This study examined the influence of a moderate dose of caffeine on oculomotor control and visual perception in participants who were not fatigued. Methods Within a placebo-controlled crossover design, 13 healthy adults ingested caffeine (5 mg·kg −1 body mass) and were tested over 3 h. Eye movements, including saccades, smooth pursuit and optokinetic nystagmus, were measured using infrared oculography. Results Caffeine was associated with higher peak saccade velocities (472 ± 60° s −1 ) compared to placebo (455 ± 62° s −1 ). Quick phases of optokinetic nystagmus were also significantly faster with caffeine, whereas pursuit eye movements were unchanged. Non-oculomotor perceptual tasks (global motion and global orientation processing) were unaffected by caffeine. Conclusions These results show that oculomotor control is modulated by a moderate dose of caffeine in unfatigued humans. These effects are detectable in the kinematics of rapid eye movements, whereas pursuit eye movements and visual perception are unaffected. Oculomotor functions may be sensitive to changes in central catecholamines mediated via caffeine’s action as an adenosine antagonist, even when participants are not fatigued.

Nicotine and methylphenidate are putative cognitive enhancers in healthy and patient populations. Although they stimulate different neurotransmitter systems, they have been shown to enhance performance on overlapping measures of attention. So far, there has been no direct comparison of the effects of these two stimulants on behavioural performance or brain function in healthy humans. Here, we directly compare the two compounds using a well-established oculomotor biomarker in order to explore common and distinct behavioural and neural effects. Eighty-two healthy male non-smokers performed a smooth pursuit eye movement task while lying in an fMRI scanner. In a between-subjects, double-blind design, subjects either received placebo (placebo patch and capsule), nicotine (7mg nicotine patch and placebo capsule), or methylphenidate (placebo patch and 40mg methylphenidate capsule). There were no significant drug effects on behavioural measures. At the neural level, methylphenidate elicited higher activation in left frontal eye field compared to nicotine, with an intermediate response under placebo. The reduced activation of task-related regions under nicotine could be associated with more efficient neural processing, while increased hemodynamic response under methylphenidate is interpretable as enhanced processing of task-relevant networks. Together, these findings suggest dissociable neural effects of these putative cognitive enhancers. •We investigated neural and behavioural effects of nicotine and methylphenidate in 82 healthy males•A smooth pursuit eye movement paradigm was used in fMRI to investigate performance and BOLD effects•There was no influence of either compound on behavioural measures of smooth pursuit•The methylphenidate group exhibited increased left frontal eye field activation in contrast to the nicotine group

The smooth pursuit system has the ability to perform predictive feedforward control of eye movements. This study attempted to examine how stimulus and behavioral histories of past trials affect the control of predictive pursuit of target motion with randomized velocities. We used sequential ramp stimuli where the rightward velocity was fixed at 16 deg/s while the leftward velocity was either fixed (predictable) at one of seven velocities (4, 8, 12, 16, 20, 24, or 28 deg/s) or randomized (unpredictable). As a result, predictive pursuit responses were observed not only in the predictable condition but also in the unpredictable condition. Linear mixed-effects (LME) models showed that both stimulus and behavioral histories of the previous two or three trials influenced the predictive pursuit responses in the unpredictable condition. Intriguingly, the goodness of fit of the LME model was improved when both historical effects were fitted simultaneously rather than when each type of historical data was fitted alone. Our results suggest that predictive pursuit systems allow us to track randomized target motion using weighted averaging of the information of target velocity (stimulus) and motor output (behavior) in past time sequences.

We provide a brief review of the current status of the traditional problem of pursuit and evasion. The new approach proposed by Eliezer and Barton is presented with an example of the one-to-one pursuit and evasion. Consequently, we describe recently proposed “Group Chase and Escape” and its characteristic behaviors. The directions of possible topics for future investigations and possible applications are also discussed.

Smooth pursuit eye movements (SPEM) are essential to guide behaviour in complex visual environments. SPEM accuracy is known to be degraded by the presence of a structured visual background and at higher target velocities. The aim of this preregistered study was to investigate the neural mechanisms of these robust behavioural effects. N = 33 participants performed a SPEM task with two background conditions (present and absent) at two target velocities (0.4 and 0.6 Hz). Eye movement and BOLD data were collected simultaneously. Both the presence of a structured background and faster target velocity decreased pursuit gain and increased catch‐up saccade rate. Faster targets additionally increased position error. Higher BOLD response with background was found in extensive clusters in visual, parietal, and frontal areas (including the medial frontal eye fields; FEF) partially overlapping with the known SPEM network. Faster targets were associated with higher BOLD response in visual cortex and left lateral FEF. Task‐based functional connectivity analyses (psychophysiological interactions; PPI) largely replicated previous results in the basic SPEM network but did not yield additional information regarding the neural underpinnings of the background and velocity effects. The results show that the presentation of visual background stimuli during SPEM induces activity in a widespread visuo‐parieto‐frontal network including areas contributing to cognitive aspects of oculomotor control such as medial FEF, whereas the response to higher target velocity involves visual and motor areas such as lateral FEF. Therefore, we were able to propose for the first time different functions of the medial and lateral FEF during SPEM. Visual background stimuli reduce smooth pursuit accuracy and are associated with enhanced brain activity in a widespread occipito‐parieto‐frontal brain network. Faster targets similarly reduce smooth pursuit performance but yield enhanced BOLD response only in visual cortex and lateral frontal eye fields. Functional connectivity analyses confirmed the interactions amongst components of the SPEM network but did not yield differences between task conditions.

Objective While positive impacts of osteopathic manipulative treatment (OMT) on the human visual system have long been proposed, even in individuals without visual or oculomotor symptoms, scientific evidence supporting this hypothesis remains insufficient. This randomized, three-arm parallel clinical trial aimed to compare the effects of OMT vs. sham OMT and test-retest improvements on ocular pursuit. Methods 145 participants aged 18–35 years were randomly assigned to one of three groups: an experimental OMT group (n = 53), a sham OMT group (n = 51), or a no-treatment control group that received no manual contact (n = 54). The experimental intervention consisted of a single OMT session delivered by an osteopathic practitioner. The proportion and latency of smooth pursuit eye movements (slow, continuous eye movements that follow a visual target) were assessed before and after the intervention. Results Overall, the mean proportion of smooth pursuit eye movements increased slightly but significantly in both the experimental OMT group (by 0.78%; 95% CI , 0.24–1.33) and the control group (by 1.2%; 95% CI, 0.64–1.75). Pursuit latencies decreased by 12 ms (95% CI, 1.2–22.9) and 24.1 ms (95% CI, 13.6–34.5), in these same groups, respectively. Importantly, there was no evidence of between-group differences for either outcome ( P = .12 and P = .11). Conclusion The observed enhancements in ocular pursuit quality may be attributable to test-retest benefits. Further clinical trials involving older participants or patients with oculomotor impairments should be conducted to clarify potential clinical implications.

Ocular fixation is a dynamic process that is actively controlled by many of the same brain structures involved in the control of eye movements, including the superior colliculus, cerebellum and reticular formation. In this article, we review several aspects of this active control. First, the decision to move the eyes not only depends on target-related signals from the peripheral visual field, but also on signals from the currently fixated target at the fovea, and involves mechanisms that are shared between saccades and smooth pursuit. Second, eye position during fixation is actively controlled and depends on bilateral activity in the superior colliculi and medio-posterior cerebellum; disruption of activity in these circuits causes systematic deviations in eye position during both fixation and smooth pursuit eye movements. Third, the eyes are not completely still during fixation but make continuous miniature movements, including ocular drift and microsaccades, which are controlled by the same neuronal mechanisms that generate larger saccades. Finally, fixational eye movements have large effects on visual perception. Ocular drift transforms the visual input in ways that increase spatial acuity; microsaccades not only improve vision by relocating the fovea but also cause momentary changes in vision analogous to those caused by larger saccades. This article is part of the themed issue ‘Movement suppression: brain mechanisms for stopping and stillness’.

RationaleThe non-selective nicotinic acetylcholine receptor (nAChR) agonist nicotine has been argued to improve attention via enhanced filtering of irrelevant stimuli. Here, we tested this hypothesis in the context of smooth pursuit eye movements (SPEMs), an oculomotor function previously shown to improve with nicotine in some but not all studies.ObjectivesIn order to test whether nicotine improves performance particularly when the inhibition of distracting stimuli is required, SPEM was elicited in conditions with or without peripheral distractors. Additionally, different target frequencies were employed in order to parametrically vary general processing demands on the SPEM system.MethodsHealthy adult non-smokers (N = 18 females, N = 13 males) completed a horizontal sinusoidal SPEM task at different target frequencies (0.2 Hz, 0.4 Hz, 0.6 Hz) in the presence or absence of peripheral distractors in a double-blind, placebo-controlled, cross-over design using a 2 mg nicotine gum.ResultsNicotine increased peak pursuit gain relative to placebo (p < .001), but an interaction with distractor condition (p = .001) indicated that this effect was most pronounced in the presence of distractors. Catch-up saccade frequency was reduced by nicotine (p = .01), particularly at higher target frequencies (two-way interaction, p = .04). However, a three-way interaction (p = .006) indicated that the reduction with nicotine was strongest at the highest target frequency (0.6 Hz) only without distractors, whereas in the presence of distractors, it was strongest at 0.4-Hz target frequency. There were no effects of nicotine on subjective state measures.ConclusionsTogether, these findings support a role of both distractor inhibition and general processing load in the effects of nicotine on smooth pursuit.

With the growing thirst to investigate determinants of entrepreneurship intentions and pursuit among youth and students at institutions of higher learning in South Africa, the study sought to draw distinct differences in entrepreneurship pursuit stimulus influenced by gender dimensions. It further investigated students\\' views on the most challenging aspect of being an entrepreneur. The study was conducted at the University of Venda, Vhembe district of the Limpopo province. The study participants were final-year students in an entrepreneurship module pursuing various agricultural disciplines. The study used a purposive sampling technique to select its participants, while data was collected using an online survey. The study findings indicated that entrepreneurship stimuli relating to entrepreneurship perception, social affiliation, and academic dynamics shaped entrepreneurship pursuit with variations in gender dimensions. The study recommended championing young, established entrepreneurs for entrepreneurship promotion for enhanced entrepreneurial action among students.

The UAV pursuit-evasion problem focuses on the efficient tracking and capture of evading targets using unmanned aerial vehicles (UAVs), which is pivotal in public safety applications, particularly in scenarios involving intrusion monitoring and interception. To address the challenges of data acquisition, real-world deployment, and the limited intelligence of existing algorithms in UAV pursuit-evasion tasks, we propose an innovative swarm intelligence-based UAV pursuit-evasion control framework, namely “Boids Model-based DRL Approach for Pursuit and Escape” (Boids-PE), which synergizes the strengths of swarm intelligence from bio-inspired algorithms and deep reinforcement learning (DRL). The Boids model, which simulates collective behavior through three fundamental rules, separation, alignment, and cohesion, is adopted in our work. By integrating Boids model with the Apollonian Circles algorithm, significant improvements are achieved in capturing UAVs against simple evasion strategies. To further enhance decision-making precision, we incorporate a DRL algorithm to facilitate more accurate strategic planning. We also leverage self-play training to continuously optimize the performance of pursuit UAVs. During experimental evaluation, we meticulously designed both one-on-one and multi-to-one pursuit-evasion scenarios, customizing the state space, action space, and reward function models for each scenario. Extensive simulations, supported by the PyBullet physics engine, validate the effectiveness of our proposed method. The overall results demonstrate that Boids-PE significantly enhance the efficiency and reliability of UAV pursuit-evasion tasks, providing a practical and robust solution for the real-world application of UAV pursuit-evasion missions.