Object Motion Computation for the Initiation of Smooth Pursuit Eye Movements in Humans

A dynamical model of visual motion processing for arbitrary stimuli including type II plaids

To explore the operating principle of visual motion processing in the brain underlying perception and eye movements, we model the information processing of velocity estimate of the visual stimulus at the algorithmic level using the dynamical system approach. In this study, we formulate the model as an optimization process of an appropriately defined objective function. The model is applicable to arbitrary visual stimuli. We find that our theoretical predictions qualitatively agree with time evolution of eye movement reported by previous works across various types of stimulus. Our results suggest that the brain implements the present framework as the internal model of motion vision. We anticipate our model to be a promising building block for more profound understanding of visual motion processing as well as for the development of robotics.

The perceptual dynamics of the contrast induced speed bias

In this article we present a temporal extension of the slow motion prior model to generate predictions regarding the temporal evolution of the contrast induced speed bias. We further tested these predictions using a novel experimental paradigm that allows us to measure the dynamic perceptual difference between stimuli through a series of manual pursuit open loop tasks. Results show good agreement with our model's predictions. The main findings reveal that hand speed dynamics are affected by stimulus contrast in a way that is consistent with a dynamic model of motion perception that assumes a slow motion prior. The proposed model also confirms observations made in previous studies that suggest that motion bias persisted even at high contrast as a consequence of the dynamics of the slow motion prior.

Reinforcement effects in anticipatory smooth eye movements

When predictive information about target motion is available, anticipatory smooth pursuit eye movements (aSPEM) are consistently generated before target appearance, thereby reducing the typical sensorimotor delay between target motion onset and foveation. By manipulating the probability for target motion direction, we were able to bias the direction and mean velocity of aSPEM. This suggests that motion-direction expectancy has a strong effect on the initiation of anticipatory movements. To further understand the nature of anticipatory smooth eye movements, we investigated different effects of reinforcement on aSPEM. In a first experiment, the reinforcement was contingent to a particular anticipatory behavior. A monetary reward was associated to a criterion-matching anticipatory velocity as estimated online during the gap before target motion onset. Our results showed a small but significant effect of behavior-contingent monetary reward on aSPEM. In a second experiment, the proportion of rewarded trials was manipulated across motion directions (right vs. left) independently from participants' behavior. Our results indicate that a bias in expected reward does not systematically affect anticipatory eye movements. Overall, these findings strengthen the notion that anticipatory eye movements can be considered as an operant behavior (similar to visually guided ones), whereas the expectancy for a noncontingent reward cannot efficiently bias them.

Does environmental exposure to the greenhouse gas, N2O, contribute to etiological factors in neurodevelopmental disorders? A mini-review of the evidence

BACKGROUND

Neurodevelopmental disorders are increasing in prevalence worldwide. Previous work suggests that exposure to the environmental air pollutant and greenhouse gas - nitrous oxide (N₂O) - may be an etiological factor in neurodevelopmental disorders through the targeting of several neural correlates.

METHODOLOGY

While a number of recent systematic reviews have addressed the role of general anesthesia in the surgical setting and neurodevelopmental outcomes, a narrative mini-review was conducted to first define and characterize the relevant variables (i.e., N₂O, attention-deficit hyperactivity disorder [ADHD] and autism spectrum disorders [ASD]) and their potential interactions into a coherent, hypothesis-generating work. The narrative mini-review merges basic principles in environmental science, anesthesiology, and psychiatry to more fully develop the novel hypotheses that neurodevelopmental impairment found in conditions like ADHD and ASD may be due to exposure to the increasing air pollutant, N₂O.

RESULTS

The results of the present mini-review indicate that exposure to N₂O, even at non-toxic doses, may modulate central neurotransmission and target many neural substrates directly implicated in neurodevelopmental disorders, including the glutamatergic, opioidergic, cholinergic, and dopaminergic systems. Epidemiological studies also indicate that early and repeated exposure to general anesthesia, including N₂O, may contribute to later adverse neurodevelopmental outcomes in children.

CONCLUSIONS

The current evidence and subsequent hypotheses suggest that a renewed interest be taken in the toxicological assessment of environmental N₂O exposure using validated biomarkers and psychiatric endpoints. Given the relevance of N₂O as a greenhouse gas, societies may also wish to engage in a more robust monitoring and reporting of N₂O levels in the environment for climactic benefit as well.

Dynamic integration of information about salience and value for smooth pursuit eye movements

Eye movement behavior can be determined by bottom-up factors like visual salience and by top-down factors like expected value. These different types of signals have to be combined for the control of eye movements. In this study we investigated how smooth pursuit eye movements integrate salience and value information. Observers were asked to track a random-dot kinematogram containing two coherent motion directions. To manipulate salience, the coherence or the density of one of the motion signals was varied. To manipulate value, observers won or lost money in a separate experiment if they were tracking one or the other motion direction. Our results show that pursuit direction was initially determined only by salience. 300-400 ms after target motion onset, pursuit steered towards the rewarded direction and the salience effects disappeared. The time course of this effect depended crucially on the difficulty to segment the two signal directions. These results indicate that salience determines early pursuit responses in the same way as saccades with short latencies. Value information is processed slower and dominates pursuit after several 100 ms.

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 global percept. This is well demonstrated for the detection of motion in the aperture problem: as luminance of an elongated line is symmetrical along its axis, tangential velocity is ambiguous when measured locally. Here, we develop the hypothesis that motion-based predictive coding is sufficient to infer global motion. Our implementation is based on a context-dependent diffusion of a probabilistic representation of motion. We observe in simulations a progressive solution to the aperture problem similar to physiology and behavior. We demonstrate that this solution is the result of two underlying mechanisms. First, we demonstrate the formation of a tracking behavior favoring temporally coherent features independent of their texture. Second, we observe that incoherent features are explained away, while coherent information diffuses progressively to the global scale. Most previous models included ad hoc mechanisms such as end-stopped cells or a selection layer to track specific luminance-based features as necessary conditions to solve the aperture problem. Here, we have proved that motion-based predictive coding, as it is implemented in this functional model, is sufficient to solve the aperture problem. This solution may give insights into the role of prediction underlying a large class of sensory computations.

Eye movements: the past 25 years

This article reviews the past 25 years of research on eye movements (1986-2011). Emphasis is on three oculomotor behaviors: gaze control, smooth pursuit and saccades, and on their interactions with vision. Focus over the past 25 years has remained on the fundamental and classical questions: What are the mechanisms that keep gaze stable with either stationary or moving targets? How does the motion of the image on the retina affect vision? Where do we look - and why - when performing a complex task? How can the world appear clear and stable despite continual movements of the eyes? The past 25 years of investigation of these questions has seen progress and transformations at all levels due to new approaches (behavioral, neural and theoretical) aimed at studying how eye movements cope with real-world visual and cognitive demands. The work has led to a better understanding of how prediction, learning and attention work with sensory signals to contribute to the effective operation of eye movements in visually rich environments.

Does the noise matter? Effects of different kinematogram types on smooth pursuit eye movements and perception

We investigated how the human visual system and the pursuit system react to visual motion noise. We presented three different types of random-dot kinematograms at five different coherence levels. For transparent motion, the signal and noise labels on each dot were preserved throughout each trial, and noise dots moved with the same speed as the signal dots but in fixed random directions. For white noise motion, every 20 ms the signal and noise labels were randomly assigned to each dot and noise dots appeared at random positions. For Brownian motion, signal and noise labels were also randomly assigned, but the noise dots moved at the signal speed in a direction that varied randomly from moment to moment. Neither pursuit latency nor early eye acceleration differed among the different types of kinematograms. Late acceleration, pursuit gain, and perceived speed all depended on kinematogram type, with good agreement between pursuit gain and perceived speed. For transparent motion, pursuit gain and perceived speed were independent of coherence level. For white and Brownian motions, pursuit gain and perceived speed increased with coherence but were higher for white than for Brownian motion. This suggests that under our conditions, the pursuit system integrates across all directions of motion but not across all speeds.

Modelling the dynamics of motion integration with a new luminance-gated diffusion mechanism

The dynamics of motion integration show striking similarities when observed at neuronal, psychophysical, and oculomotor levels. Based on the inter-relation and complementary insights given by those dynamics, our goal was to test how basic mechanisms of dynamical cortical processing can be incorporated in a dynamical model to solve several aspects of 2D motion integration and segmentation. Our model is inspired by the hierarchical processing stages of the primate visual cortex: we describe the interactions between several layers processing local motion and form information through feedforward, feedback, and inhibitive lateral connections. Also, following perceptual studies concerning contour integration and physiological studies of receptive fields, we postulate that motion estimation takes advantage of another low-level cue, which is luminance smoothness along edges or surfaces, in order to gate recurrent motion diffusion. With such a model, we successfully reproduced the temporal dynamics of motion integration on a wide range of simple motion stimuli: line segments, rotating ellipses, plaids, and barber poles. Furthermore, we showed that the proposed computational rule of luminance-gated diffusion of motion information is sufficient to explain a large set of contextual modulations of motion integration and segmentation in more elaborated stimuli such as chopstick illusions, simulated aperture problems, or rotating diamonds. As a whole, in this paper we proposed a new basal luminance-driven motion integration mechanism as an alternative to less parsimonious models, we carefully investigated the dynamics of motion integration, and we established a distinction between simple and complex stimuli according to the kind of information required to solve their ambiguities.

Relating spatial and temporal orientation pooling to population decoding solutions in human vision

Spatial pooling is often considered synonymous with averaging (or other statistical combinations) of local information contained within a complex visual image. We have recently shown, however, that spatial pooling of motion signals is better characterized in terms of optimal decoding of neuronal populations rather than image statistics (Webb et al., 2007). Here we ask which computations guide the spatial and temporal pooling of local orientation signals in human vision. The observers’ task was to discriminate which of two texture patterns had a more clockwise global orientation. Standard textures had a common orientation; comparison textures were chosen independently from a skewed (asymmetrical) probability distribution with distinct spatial or temporal statistics. We simulated observers’ performance using different estimators (vector average, winner-takes-all and maximum likelihood) to decode the orientation-tuned activity of a population of model neurons. Our results revealed that the perceived global orientation of texture patterns coincided with the mean (or vector average read-out) of orientation signals accumulated over both space and time. To reconcile these results with our previous work on direction pooling, we varied stimulus duration. Perceived global orientation was accurately predicted by a vector average read-out of orientation signals at relatively short stimulus durations and maximum likelihood read-out at longer durations. Moreover, decreasing the luminance contrast of texture patterns increased the duration of the transition from a vector average to maximum likelihood read-out. Our results suggest that direction and orientation pooling use similar probabilistic read-out strategies when sufficient time is available.

A neurally plausible model of the dynamics of motion integration in smooth eye pursuit based on recursive Bayesian estimation

In this study, we describe a model of motion integration in smooth eye pursuit based on a recursive Bayesian estimation process, which displays a dynamic behaviour qualitatively similar to the dynamics of the motion integration process observed experimentally, both psychophysically in humans and monkeys, and physiologically in monkeys. By formulating the model as an approximate version of a Kalman filter algorithm, we have been able to show that it can be put into a neurally plausible, distributed recurrent form which coarsely corresponds to the recurrent circuitry of visual cortical areas V1 and MT. The model thus provides further support for the notion that the motion integration process is based on a form of Bayesian estimation, as has been suggested by many psychophysical studies, and moreover suggests that the observed dynamic properties of this process are the result of the recursive nature of the motion estimation.

Bayesian modeling of dynamic motion integration

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 ambiguity due to the spatial limitation of the visual motion analyzers (aperture problem). Perceptual and oculomotor data demonstrate that motion processing of extended objects is initially dominated by the local 1D motion cues, related to the object's edges and orthogonal to them, whereas 2D information, related to terminators (or edge-endings), takes progressively over and leads to the final correct representation of global motion. A Bayesian framework accounting for the sensory noise and general expectancies for object velocities has proven successful in explaining several experimental findings concerning early motion processing [Weiss, Y., Adelson, E., 1998. Slow and smooth: a Bayesian theory for the combination of local motion signals in human vision. MIT Technical report, A.I. Memo 1624]. In particular, these models provide a qualitative account for the initial bias induced by the 1D motion cue. However, a complete functional model, encompassing the dynamical evolution of object motion perception, including the integration of different motion cues, is still lacking. Here we outline several experimental observations concerning human smooth pursuit of moving objects and more particularly the time course of its initiation phase, which reflects the ongoing motion integration process. In addition, we propose a recursive extension of the Bayesian model, motivated and constrained by our oculomotor data, to describe the dynamical integration of 1D and 2D motion information. We compare the model predictions for object motion tracking with human oculomotor recordings.

Predicting 2D Target Velocity Cannot Help 2D Motion Integration for Smooth Pursuit Initiation

Smooth pursuit eye movements reflect the temporal dynamics of bidimensional (2D) visual motion integration. When tracking a single, tilted line, initial pursuit direction is biased toward unidimensional (1D) edge motion signals, which are orthogonal to the line orientation. Over 200 ms, tracking direction is slowly corrected to finally match the 2D object motion during steady-state pursuit. We now show that repetition of line orientation and/or motion direction does not eliminate the transient tracking direction error nor change the time course of pursuit correction. Nonetheless, multiple successive presentations of a single orientation/direction condition elicit robust anticipatory pursuit eye movements that always go in the 2D object motion direction not the 1D edge motion direction. These results demonstrate that predictive signals about target motion cannot be used for an efficient integration of ambiguous velocity signals at pursuit initiation.