A neural model of border-ownership from kinetic occlusion

Layton, Oliver W.; Yazdanbakhsh, Arash

doi:10.1016/j.visres.2014.11.002

Cited by 19 publications

(14 citation statements)

References 57 publications

(84 reference statements)

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“…The primate visual system most likely uses several redundant and complementary mechanisms for all manner of computations, including DOFM. Thus models that use BF cues, such as that of Layton and Yazdanbakhsh (2015), could possibly be combined with the FMO model in order to take full advantage of all the available cues. For example, stimuli that are not random-dot based, where there are BF cues but no AD cues-such as the demos of Brooks and Palmer (2003)-cannot be explained by the FMO model.…”

Section: Discussionmentioning

confidence: 99%

Computational modeling of depth ordering in occlusion through accretion or deletion of texture

Ruda¹,

Livitz²,

Riesen³

et al. 2015

Journal of Vision

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Understanding the depth ordering of surfaces in the natural world is one of the most fundamental operations of the primate visual system. Surfaces that undergo accretion or deletion (AD) of texture are always perceived to behind an adjacent surface. An updated ForMotionOcclusion model (Barnes & Mingolla, 2013) includes two streams for computing motion signals and boundary signals. The two streams generate depth percepts such that AD signals together with boundary signals generate a farther depth on the occluded side of the boundary. The model fits the classical data (Kaplan, 1969) as well as the observation that moving surfaces tend to appear closer in depth (Royden, Baker, & Allman, 1988), for both binary and grayscale stimuli. The recent "Moonwalk illusion" described by Kromrey, Bart, and Hegdé (2011) upends the classical view that the surface undergoing AD always becomes the background. Here the surface that undergoes AD appears to be in front of the surrounding surface-a result of the random flickering noise in the surround. As an additional challenge, we developed an AD display with dynamic depth ordering. A new texture version of the Michotte rabbit hole phenomenon (Michotte, Thinès, & Crabbé, 1964/1991) generates depth that changes in part of the display area. Because the ForMotionOcclusion model separates the computation of boundaries from the computation of AD signals, it is able to explain the counterintuitive Moonwalk stimulus. We show simulations that explain the workings of the model and how the model explains the Moonwalk and textured Michotte phenomena.

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Section: Discussionmentioning

confidence: 99%

Computational modeling of depth ordering in occlusion through accretion or deletion of texture

Ruda¹,

Livitz²,

Riesen³

et al. 2015

Journal of Vision

View full text Add to dashboard Cite

show abstract

“…The heuristic used here assumes that an object is usually observed by a closed contour, and the center of this contour might be used to represent the location of this object. Since the focus of this work is on sensorimotor coordination rather than complex object recognition, the more complex cases of occlusion and boundary ownership are not taken into account (Heydt et al, 2003 ; Layton and Yazdanbakhsh, 2015 ; Dresp-Langley and Grossberg, 2016 ). To achieve our goal, we first applied a center-surround filter and convolved CPM's luminance profile regardless of depth, as shown in the grid in Figure 2D , where the filled pattern is an example and corresponds to what we actually used in the experiments.…”

Section: Description Of the Modelmentioning

confidence: 99%

Sensorimotor Self-organization via Circular-Reactions

Öğmen

2021

Front. Neurorobot.

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Newborns demonstrate innate abilities in coordinating their sensory and motor systems through reflexes. One notable characteristic is circular reactions consisting of self-generated motor actions that lead to correlated sensory and motor activities. This paper describes a model for goal-directed reaching based on circular reactions and exocentric reference-frames. The model is built using physiologically plausible visual processing modules and arm-control neural networks. The model incorporates map representations with ego- and exo-centric reference frames for sensory inputs, vector representations for motor systems, as well as local associative learning that result from arm explorations. The integration of these modules is simulated and tested in a three-dimensional spatial environment using Unity3D. The results show that, through self-generated activities, the model self-organizes to generate accurate arm movements that are tolerant with respect to various sources of noise.

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“…This temporal difference indicates that the KB‐orientation‐selective response is computed by using feedback inputs from some areas (Marcar et al., ). A model study also has suggested that V4 and MT neurons, which feed signals back to V2 neurons, can play a crucial role in determining figure surfaces distinct from the background using motion cues (Layton & Yazdanbakhsh, ) (Figure ).…”

Section: Early Cortical Areas Likely Provide a Key Function To Share mentioning

confidence: 99%

Neuronal correlates of motion‐defined shape perception in primate dorsal and ventral streams

Handa

Mikami

2018

Eur J of Neuroscience

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Human and non-human primates can readily perceive the shape of objects using visual motion. Classically, shape, and motion are considered to be separately processed via ventral and dorsal cortical pathways, respectively. However, many lines of anatomical and physiological evidence have indicated that these two pathways are likely to be interconnected at some stage. For motion-defined shape perception, these two pathways should interact with each other because the ventral pathway must utilize motion, which the dorsal pathway processes, to extract shape signal. However, it is unknown how interactions between cortical pathways are involved in neural mechanisms underlying motion-defined shape perception. We review evidence from psychophysical, lesion, neuroimaging and physiological research on motion-defined shape perception and then discuss the effects of behavioral demands on neural activity in ventral and dorsal cortical areas. Further, we discuss functions of two candidate sets of levels: early and higher-order cortical areas. The extrastriate area V4 and middle temporal (MT) area, which are reciprocally connected, at the early level are plausible areas for extracting the shape and/or constituent parts of shape from motion cues because neural dynamics are different from those during luminance-defined shape perception. On the other hand, among other higher-order visual areas, the anterior superior temporal sulcus likely contributes to the processing of cue-invariant shape recognition rather than cue-dependent shape processing. We suggest that sharing information about motion and shape between the early visual areas in the dorsal and ventral pathways is dependent on visual cues and behavioral requirements, indicating the interplay between the pathways.

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A neural model of border-ownership from kinetic occlusion

Cited by 19 publications

References 57 publications

Computational modeling of depth ordering in occlusion through accretion or deletion of texture

Computational modeling of depth ordering in occlusion through accretion or deletion of texture

Sensorimotor Self-organization via Circular-Reactions

Neuronal correlates of motion‐defined shape perception in primate dorsal and ventral streams

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