eye movements

Reinforcement effects in anticipatory smooth eye movements
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Dynamic modulation of volatility by reward contingencies: effects on anticipatory smooth eye movement
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How the dynamics of human smooth pursuit is influenced by speed uncertainty
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Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
The properties of motion processing for driving smooth eye movements have bee investigated using simple, artificial stimuli such as …
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Operant reinforcement versus reward expectancy: effects on anticipatory eye movements
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Effects of motion predictability on anticipatory and visually-guided eye movements: a common prior for sensory processing and motor control?
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Modeling the effect of dynamic contingencies on anticipatory eye movements
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Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
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Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
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Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit
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Anticipatory smooth eye movements and reinforcement
When an object is moving in the visual field, we are able to accurately track it with a combination of saccades and smooth eye …
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Anticipatory smooth eye movements as operant behavior
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Active inference, eye movements and oculomotor delays
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. …
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Active inference, eye movements and oculomotor delays
The characteristics of microsaccadic eye movements varied with the change of strategy in a match-to-sample task
Under natural viewing conditions, large eye movements are interspace by small eye movements (microsaccade). Recent works have shown …
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Axonal delays and on-time control of eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2014-01-10 112:00
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Axonal delays and on-time control of eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
Last updated on 2021-10-12
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On the nature of anticipatory eye movements and the factors affecting them
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Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception
The visual system does not process information instantaneously, but rather integrates over time. Integration occurs both for stationary …
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Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon …
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The behavioral receptive field underlying motion integration for primate tracking eye movements
Short-latency ocular following are reflexive, tracking eye movements that are observed in human and non-human primates in response to a …
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Grabbing, tracking and sniffing as models for motion detection and eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2012-01-27 112:00
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
2012-01-12 15:00 Malet Place Eng Bldg 1.03 (first floor).
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Effect of image statistics on fixational eye movements
Under natural viewing conditions, small movements of the eyes prevent the maintenance of a steady direction of gaze. It is unclear how …
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Grabbing, tracking and sniffing as models for motion detection and eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
Last updated on 2021-10-12
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Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.
To measure speed and direction of moving objects, the cortical motion system pools information across different spatiotemporal …
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More is not always better: dissociation between perception and action explained by adaptive gain control
Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal …
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More is not always better: dissociation between perception and action explained by adaptive gain control
Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
Last updated on 2021-10-12
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …
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Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
Perceptions as Hypotheses: Saccades as Experiments
If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate …
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Perceptions as Hypotheses: Saccades as Experiments
Pattern discrimination for moving random textures: Richer stimuli are more difficult to recognize
In order to analyze the characteristics of a rich dynamic visual environment, the visual system must integrate information collected at …
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Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2011-07-02 712:00
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Saccadic foveation of a moving visual target in the rhesus monkey
When generating a saccade toward a moving target, the target displacement that occurs during the period spanning from its detection to …
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Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
Last updated on 2021-10-12
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Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2010-12-17 1212:00
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Role of homeostasis in learning sparse representations
Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the …
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Role of homeostasis in learning sparse representations
Models of low-level vision: linking probabilistic models and neural masses
see this more recent talk @ UCL, London
2010-01-08 112:00
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Different pooling of motion information for perceptual speed discrimination and behavioral speed estimation
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Dynamical emergence of a neural solution for motion integration
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Models of low-level vision: linking probabilistic models and neural masses
Last updated on 2021-10-12
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Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
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Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
Last updated on 2021-10-12
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Decoding center-surround interactions in population of neurons for the ocular following response
Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed …
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Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its …
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Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Decoding the population dynamics underlying ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following …
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
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Dynamical Neural Networks: modeling low-level vision at short latencies
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following …
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Modeling spatial integration in the ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following …
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Modeling spatial integration in the ocular following response using a probabilistic framework
Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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