scholarly journals Bayesian Modeling of Motion Perception Using Dynamical Stochastic Textures

2018 ◽  
Vol 30 (12) ◽  
pp. 3355-3392 ◽  
Author(s):  
Jonathan Vacher ◽  
Andrew Isaac Meso ◽  
Laurent U. Perrinet ◽  
Gabriel Peyré
Keyword(s):  
Spatial Frequency ◽  
Motion Perception ◽  
Visual Motion ◽  
Texture Synthesis ◽  
Three Dimensional ◽  
Generative Model ◽  
Perceptual Bias ◽  
Dynamic Texture ◽  
Local Motion ◽  
Texture Model

A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on optimal inference with respect to a generative model. The study presented here details the complete formulation of such a generative model intended to probe visual motion perception with a dynamic texture model. It is derived in a set of axiomatic steps constrained by biological plausibility. We extend previous contributions by detailing three equivalent formulations of this texture model. First, the composite dynamic textures are constructed by the random aggregation of warped patterns, which can be viewed as three-dimensional gaussian fields. Second, these textures are cast as solutions to a stochastic partial differential equation (sPDE). This essential step enables real-time, on-the-fly texture synthesis using time-discretized autoregressive processes. It also allows for the derivation of a local motion-energy model, which corresponds to the log likelihood of the probability density. The log likelihoods are essential for the construction of a Bayesian inference framework. We use the dynamic texture model to psychophysically probe speed perception in humans using zoom-like changes in the spatial frequency content of the stimulus. The human data replicate previous findings showing perceived speed to be positively biased by spatial frequency increments. A Bayesian observer who combines a gaussian likelihood centered at the true speed and a spatial frequency dependent width with a “slow-speed prior” successfully accounts for the perceptual bias. More precisely, the bias arises from a decrease in the observer's likelihood width estimated from the experiments as the spatial frequency increases. Such a trend is compatible with the trend of the dynamic texture likelihood width.

scholarly journals On the Aperture Problem of Binocular 3D Motion Perception

Vision ◽  
2019 ◽  
Vol 3 (4) ◽  
pp. 64
Author(s):  
Martin Lages ◽  
Suzanne Heron
Keyword(s):  
Visual System ◽  
Motion Perception ◽  
Three Dimensional ◽  
Circular Aperture ◽  
Local Motion ◽  
Motion Direction ◽  
3D Motion ◽  
Aperture Problem ◽  
Points Of View ◽  
Velocity Constraints

Like many predators, humans have forward-facing eyes that are set a short distance apart so that an extensive region of the visual field is seen from two different points of view. The human visual system can establish a three-dimensional (3D) percept from the projection of images into the left and right eye. How the visual system integrates local motion and binocular depth in order to accomplish 3D motion perception is still under investigation. Here, we propose a geometric-statistical model that combines noisy velocity constraints with a spherical motion prior to solve the aperture problem in 3D. In two psychophysical experiments, it is shown that instantiations of this model can explain how human observers disambiguate 3D line motion direction behind a circular aperture. We discuss the implications of our results for the processing of motion and dynamic depth in the visual system.

Motion Perception: From Detection to Interpretation

2018 ◽  
Vol 4 (1) ◽  
pp. 501-523 ◽  
Author(s):  
Shin'ya Nishida ◽  
Takahiro Kawabe ◽  
Masataka Sawayama ◽  
Taiki Fukiage
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Specular Reflection ◽  
Motion Vector ◽  
Motion Processing ◽  
Local Motion ◽  
Computational Framework ◽  
Motion Signal ◽  
Visual Motion Processing ◽  
Level Of Processing

Visual motion processing can be conceptually divided into two levels. In the lower level, local motion signals are detected by spatiotemporal-frequency-selective sensors and then integrated into a motion vector flow. Although the model based on V1-MT physiology provides a good computational framework for this level of processing, it needs to be updated to fully explain psychophysical findings about motion perception, including complex motion signal interactions in the spatiotemporal-frequency and space domains. In the higher level, the velocity map is interpreted. Although there are many motion interpretation processes, we highlight the recent progress in research on the perception of material (e.g., specular reflection, liquid viscosity) and on animacy perception. We then consider possible linking mechanisms of the two levels and propose intrinsic flow decomposition as the key problem. To provide insights into computational mechanisms of motion perception, in addition to psychophysics and neurosciences, we review machine vision studies seeking to solve similar problems.

Local Constraints for the Perception of Binocular 3D Motion

2012 ◽  
pp. 90-120
Author(s):  
Martin Lages ◽  
Suzanne Heron ◽  
Hongfang Wang
Keyword(s):  
Computer Simulations ◽  
Visual Processing ◽  
Three Dimensional ◽  
Perceptual Bias ◽  
Probabilistic Framework ◽  
Local Motion ◽  
Motion Direction ◽  
Local Constraints ◽  
3D Motion ◽  
Perceived Velocity

The authors discuss local constraints for the perception of three-dimensional (3D) binocular motion in a geometric-probabilistic framework. It is shown that Bayesian models of binocular 3D motion can explain perceptual bias under uncertainty and predict perceived velocity under ambiguity. The models exploit biologically plausible constraints of local motion and disparity processing in a binocular viewing geometry. Results from computer simulations and psychophysical experiments support the idea that local constraints of motion and disparity processing are combined late in the visual processing hierarchy to establish perceived 3D motion direction.

scholarly journals The Roles of Different Spatial Frequency Channels in Real-World Visual Motion Perception

2018 ◽  
Author(s):  
Cong Shi ◽  
Shrinivas Pundlik ◽  
Gang Luo
Keyword(s):  
Spatial Frequency ◽  
Motion Perception ◽  
Visual Motion ◽  
Low Vision ◽  
Estimation Error ◽  
Temporal Frequency ◽  
Speed Estimation ◽  
Cutoff Frequencies ◽  
Vision Condition ◽  
Speed Perception

AbstractSpeed perception is an important task performed by our visual system in various daily life tasks. In various psychophysical tests, relationship between spatial frequency, temporal frequency, and speed has been examined in human subjects. The role of vision impairment in speed perception has also been previously examined. In this work, we examine the inter-relationship between speed, spatial frequency, low vision conditions, and the type of input motion stimuli in motion perception accuracy. For this purpose, we propose a computational model for speed perception and evaluate it in custom generated natural and stochastic sequences by simulating low-vision conditions (low pass filtering at different cutoff frequencies) as well as complementary vision conditions (high pass versions at the same cutoff frequencies). Our results show that low frequency components are critical for accurate speed perception, whereas high frequencies do not play any important role in speed estimation. Since perception of low frequencies may not be impaired in visual acuity loss, speed perception was not found to be impaired in low vision conditions compared to normal vision condition. We also report significant differences between natural and stochastic stimuli, notably an increase in speed estimation error when using stochastic stimuli compared to natural sequences, emphasizing the use of natural stimuli when performing future psychophysical studies for speed perception.

scholarly journals Compound stimuli reveal the structure of visual motion selectivity in macaque MT neurons

2019 ◽  
Author(s):  
Andrew D Zaharia ◽  
Robbe L T Goris ◽  
J Anthony Movshon ◽  
Eero P Simoncelli
Keyword(s):  
Spatial Frequency ◽  
Moving Objects ◽  
Visual Motion ◽  
Temporal Frequency ◽  
Direction Selectivity ◽  
Local Motion ◽  
Area Mt ◽  
Mt Neurons ◽  
Tilted Plane ◽  
Local Edge

AbstractMotion selectivity in primary visual cortex (V1) is approximately separable in orientation, spatial frequency, and temporal frequency (“frequency-separable”). Models for area MT neurons posit that their selectivity arises by combining direction-selective V1 afferents whose tuning is organized around a tilted plane in the frequency domain, specifying a particular direction and speed (“velocity-separable”). This construction explains “pattern direction selective” MT neurons, which are velocity-selective but relatively invariant to spatial structure, including spatial frequency, texture and shape. Surprisingly, when tested with single drifting gratings, most MT neurons’ responses are fit equally well by models with either form of separability. However, responses to plaids (sums of two moving gratings) tend to be better described as velocity-separable, especially for pattern neurons. We conclude that direction selectivity in MT is primarily computed by summing V1 afferents, but pattern-invariant velocity tuning for complex stimuli may arise from local, recurrent interactions.Significance StatementHow do sensory systems build representations of complex features from simpler ones? Visual motion representation in cortex is a well-studied example: the direction and speed of moving objects, regardless of shape or texture, is computed from the local motion of oriented edges. Here we quantify tuning properties based on single-unit recordings in primate area MT, then fit a novel, generalized model of motion computation. The model reveals two core properties of MT neurons — speed tuning and invariance to local edge orientation — result from a single organizing principle: each MT neuron combines afferents that represent edge motions consistent with a common velocity, much as V1 simple cells combine thalamic inputs consistent with a common orientation.

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

Role of the Vermal Cerebellum in Visually Guided Eye Movements and Visual Motion Perception

2019 ◽  
Vol 5 (1) ◽  
pp. 247-268 ◽  
Author(s):  
Peter Thier ◽  
Akshay Markanday
Keyword(s):  
Eye Movements ◽  
Motion Perception ◽  
Visual Motion ◽  
Cerebellar Nuclei ◽  
Visually Guided ◽  
Past Performance ◽  
Visual Motion Perception ◽  
Oculomotor Vermis ◽  
Retinal Information

The cerebellar cortex is a crystal-like structure consisting of an almost endless repetition of a canonical microcircuit that applies the same computational principle to different inputs. The output of this transformation is broadcasted to extracerebellar structures by way of the deep cerebellar nuclei. Visually guided eye movements are accommodated by different parts of the cerebellum. This review primarily discusses the role of the oculomotor part of the vermal cerebellum [the oculomotor vermis (OMV)] in the control of visually guided saccades and smooth-pursuit eye movements. Both types of eye movements require the mapping of retinal information onto motor vectors, a transformation that is optimized by the OMV, considering information on past performance. Unlike the role of the OMV in the guidance of eye movements, the contribution of the adjoining vermal cortex to visual motion perception is nonmotor and involves a cerebellar influence on information processing in the cerebral cortex.

scholarly journals Human visual motion perception shows hallmarks of Bayesian structural inference

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sichao Yang ◽  
Johannes Bill ◽  
Jan Drugowitsch ◽  
Samuel J. Gershman
Keyword(s):  
Motion Perception ◽  
Visual Motion ◽  
Probabilistic Reasoning ◽  
Choice Model ◽  
Human Motion ◽  
Object Motion ◽  
Ideal Observer ◽  
Structure Identification ◽  
Structural Inference ◽  
Visual Motion Perception

AbstractMotion relations in visual scenes carry an abundance of behaviorally relevant information, but little is known about how humans identify the structure underlying a scene’s motion in the first place. We studied the computations governing human motion structure identification in two psychophysics experiments and found that perception of motion relations showed hallmarks of Bayesian structural inference. At the heart of our research lies a tractable task design that enabled us to reveal the signatures of probabilistic reasoning about latent structure. We found that a choice model based on the task’s Bayesian ideal observer accurately matched many facets of human structural inference, including task performance, perceptual error patterns, single-trial responses, participant-specific differences, and subjective decision confidence—especially, when motion scenes were ambiguous and when object motion was hierarchically nested within other moving reference frames. Our work can guide future neuroscience experiments to reveal the neural mechanisms underlying higher-level visual motion perception.

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