A Computational Model of the Eye Movement Effects on Motion Perception

Perception ◽  
1996 ◽  
Vol 25 (1_suppl) ◽  
pp. 166-166
Author(s):  
K A Turano ◽  
S M Heidenreich
Keyword(s):  
Computational Model ◽  
Eye Movement ◽  
Motion Perception ◽  
Opposite Direction ◽  
Retinal Image ◽  
Vision Research ◽  
Opposite Sign ◽  
Motion Information ◽  
Extraretinal Signal ◽  
Moving Stimulus

Accurate speed and direction matches can be made between a moving stimulus viewed with a stationary eye and a moving stimulus viewed with an eye moving in the opposite direction to the stimulus. However, when the eye moves in the same direction as the distal stimulus, at a slower speed, subjects match the retinal-image motions of the stimuli rather than their distal motions (Heidenreich and Turano, 1995 Investigative Ophthalmology and Visual Science36 1675). Furthermore, it is only under this latter condition (and when the eye is stationary) that subjects can precisely discriminate distal stimulus speeds (Turano and Heidenreich Vision Research in press). We present a computational model that can account for the results of all three findings. The model assumes that the extraretinal signal mirrors the eye movement. Motion information from the retinal and extraretinal sources is combined in a subtractive manner when the two signals have opposite sign. When the two signals have the same sign, they do not combine, and motion judgments are based solely on information from the retinal source.

Vestibular Signals in Self-Orientation and Eye Movement Control

Physiology ◽  
2001 ◽  
Vol 16 (5) ◽  
pp. 234-238 ◽  
Author(s):  
Bernhard J. M. Hess
Keyword(s):  
Signal Processing ◽  
Eye Movement ◽  
Vestibular System ◽  
Retinal Image ◽  
Internal Model ◽  
Movement Control ◽  
Head Position ◽  
Head Motion ◽  
Afferent Information ◽  
The Brain

The central vestibular system receives afferent information about head position as well as rotation and translation. This information is used to prevent blurring of the retinal image but also to control self-orientation and motion in space. Vestibular signal processing in the brain stem appears to be linked to an internal model of head motion in space.

scholarly journals Pathway-dependent regulation of sleep dynamics in a network model of the sleep-wake cycle

2019 ◽  
Author(s):  
Charlotte Héricé ◽  
Shuzo Sakata
Keyword(s):  
Computational Model ◽  
Eye Movement ◽  
Network Model ◽  
Rem Sleep ◽  
Synaptic Weight ◽  
Nrem Sleep ◽  
Cell Types ◽  
Brain Areas ◽  
Neuronal Populations ◽  
Neural Populations

AbstractSleep is a fundamental homeostatic process within the animal kingdom. Although various brain areas and cell types are involved in the regulation of the sleep-wake cycle, it is still unclear how different pathways between neural populations contribute to its regulation. Here we address this issue by investigating the behavior of a simplified network model upon synaptic weight manipulations. Our model consists of three neural populations connected by excitatory and inhibitory synapses. Activity in each population is described by a firing-rate model, which determines the state of the network. Namely wakefulness, rapid eye movement (REM) sleep or non-REM (NREM) sleep. By systematically manipulating the synaptic weight of every pathway, we show that even this simplified model exhibits non-trivial behaviors: for example, the wake-promoting population contributes not just to the induction and maintenance of wakefulness, but also to sleep induction. Although a recurrent excitatory connection of the REM-promoting population is essential for REM sleep genesis, this recurrent connection does not necessarily contribute to the maintenance of REM sleep. The duration of NREM sleep can be shortened or extended by changes in the synaptic strength of the pathways from the NREM-promoting population. In some cases, there is an optimal range of synaptic strengths that affect a particular state, implying that the amount of manipulations, not just direction (i.e., activation or inactivation), needs to be taken into account. These results demonstrate pathway-dependent regulation of sleep dynamics and highlight the importance of systems-level quantitative approaches for sleep-wake regulatory circuits.Author SummarySleep is essential and ubiquitous across animal species. Over the past half-century, various brain areas, cell types, neurotransmitters, and neuropeptides have been identified as part of a sleep-wake regulating circuitry in the brain. However, it is less explored how individual neural pathways contribute to the sleep-wake cycle. In the present study, we investigate the behavior of a computational model by altering the strength of connections between neuronal populations. This computational model is comprised of a simple network where three neuronal populations are connected together, and the activity of each population determines the current state of the model, that is, wakefulness, rapid-eye-movement (REM) sleep or non-REM (NREM) sleep. When we alter the connection strength of each pathway, we observe that the effect of such alterations on the sleep-wake cycle is highly pathway-dependent. Our results provide further insights into the mechanisms of sleep-wake regulation, and our computational approach can complement future biological experiments.

Discrimination Between Movements of Eye and Object by Visual Interneurones of Crickets

1969 ◽  
Vol 50 (3) ◽  
pp. 723-732
Author(s):  
JOHN PALKA
Keyword(s):  
Eye Movement ◽  
Voluntary Movement ◽  
Compound Eye ◽  
Visual Fields ◽  
Object Motion ◽  
Visual Environment ◽  
Inhibitory Mechanism ◽  
Object Movement ◽  
Moving Stimulus ◽  
Homogeneous Environment

1. One large neurone on each side of the cervical and thoracic ventral nerve cord of crickets responds to object motion anywhere in the visual field of the ipsilateral compound eye, but not to the forced or voluntary movement of the eye itself. 2. This discrimination between self-movement and object-movement is accomplished by an inhibitory mechanism mediated by the same eye. 3. Inhibition must be present because a potent moving stimulus becomes ineffective if presented during a forced eye movement. 4. Its visual origin is demonstrated in two ways: (a) abolishing all known mechanosensory feedback does not disrupt the mechanism, but (b) alteration of visual conditions does so in a predictable way. Sweeping the eye past a complex visual environment suppresses the neurone's response to a concurrently or subsequently presented moving target, whereas the same movement past a simplified or homogeneous environment produces little or no inhibition. 5. Responses to eye movement itself are greatly enhanced in appropriately simplified visual fields, reinforcing the conclusion that the inhibition preventing response in complex fields is of visual origin. 6. Suggestive evidence for an additional inhibitory mechanism associated with voluntary movement is presented.

Can Illusory Motion Disrupt Tracking Real Motion?

Perception ◽  
1997 ◽  
Vol 26 (3) ◽  
pp. 269-275 ◽  
Author(s):  
Timothy J Andrews ◽  
Allison N McCoy
Keyword(s):  
Motion Perception ◽  
Retinal Image ◽  
Optokinetic Nystagmus ◽  
Neural Representation ◽  
Illusory Motion ◽  
Brain Areas ◽  
Critical Rate ◽  
Real Motion ◽  
Actual Motion ◽  
Perceived Motion

When rotating stripes or other periodic stimuli cross the retina at a critical rate, a reversal in the direction of motion of the stimuli is often seen. This illusion of motion perception was used to explore the roles of retinal and perceived motion in the generation of optokinetic nystagmus. Here we show that optokinetic nystagmus is disrupted during the perception of this illusion. Thus, when perceived and actual motion are in conflict, subjects fail to track the veridical movement. This observation suggests that the perception of motion can directly influence optokinetic nystagmus, even in the presence of a moving retinal image. A conflict in the neural representation of motion in different brain areas may explain these findings.

scholarly journals Accelerating eye movement research via accurate and affordable smartphone eye tracking

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Nachiappan Valliappan ◽  
Na Dai ◽  
Ethan Steinberg ◽  
Junfeng He ◽  
Kantwon Rogers ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Eye Movement ◽  
Vision Research ◽  
State Of The Art ◽  
Movement Behavior ◽  
Natural Image ◽  
Explicit Consent ◽  
Using Data ◽  
Additional Hardware

Abstract Eye tracking has been widely used for decades in vision research, language and usability. However, most prior research has focused on large desktop displays using specialized eye trackers that are expensive and cannot scale. Little is known about eye movement behavior on phones, despite their pervasiveness and large amount of time spent. We leverage machine learning to demonstrate accurate smartphone-based eye tracking without any additional hardware. We show that the accuracy of our method is comparable to state-of-the-art mobile eye trackers that are 100x more expensive. Using data from over 100 opted-in users, we replicate key findings from previous eye movement research on oculomotor tasks and saliency analyses during natural image viewing. In addition, we demonstrate the utility of smartphone-based gaze for detecting reading comprehension difficulty. Our results show the potential for scaling eye movement research by orders-of-magnitude to thousands of participants (with explicit consent), enabling advances in vision research, accessibility and healthcare.

An Illusion of Velocity in Motion Perception

1994 ◽  
Vol 78 (1) ◽  
pp. 112-114
Author(s):  
Kazuhito Noguchi ◽  
Koichi Haishi ◽  
Daisuke Sato
Keyword(s):  
Eye Movement ◽  
Motion Perception ◽  
Turning Point ◽  
Eye Position ◽  
Triangular Wave ◽  
Saccadic Movement ◽  
Ball Bouncing ◽  
The Subject

We report a phenomenon that seems to have potential to elucidate a role of eye movement in motion perception. When tracking a target controlled by a triangular wave, the viewer perceives movement of the target like a ball bouncing in between two walls. We measured eye movement with electrooculograms (EOGs) when the subject was tracking a target controlled by a triangular wave. Eye movement after passing the turning point and rapidly returning to the target with saccadic movement and then smoothly tracking the target was recorded for all 4 adults. It was considered that extraretinal information on eye position during saccade may mainly contribute to this illusion.

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