A model of neural mechanisms in monocular transparent motion perception

Abstract

Transparent motion is perceived when multiple motions are presented in the same part of visual space which moves in different directions. Several psychophysical as well as physiological experiments have studied the conditions under which motion transparency occurs. Few model mechanisms have been proposed to segregate multiple motions. We present a novel neural model which investigates the necessary mechanisms underlying initial motion detection, the required representations for velocity coding, and the integration and segregation of motion stimuli. The model extends a previously developed architecture for dorsal pathway computations, particularly, in cortical areas V1, MT, and MST, emphasizing the role of feedforward cascade processing and feedback from higher to earlier stages for selective feature enhancement and tuning. Our results demonstrate that the model reproduces several psychophysical findings using random dot stimuli. Moreover, the model is able to process transparent motion in real-world sequences of 3D scenes.

Hierarchical representation of shapes in visual cortex—from localized features to figural shape segregation

“…We have demonstrated that such separate but temporally overlapping phases can be accounted for by a recurrent network of mutually interacting neuronal sites. The network model has been composed of the same components like the present model architecture (Raudies and Neumann, 2010). It would thus be interesting to reveal whether similar temporal phases can be identified for model V4 cells that may give rise to identify different signatures indicative of contributions from delayed neuronal mechanisms that are involved in the computation of figural shape information.…”

“…This model architecture has previously been used in various approaches touching different domains, such as the disambiguation of local motion (Bayerl and Neumann, 2004; Beck and Neumann, 2011), the processing of transparent motion (Raudies and Neumann, 2010) the detection of texture boundaries (Thielscher and Neumann, 2003), the extraction of object boundaries using texture compression (Weidenbacher and Neumann, 2009), and the analysis and representation of biological motion sequences (Layher et al, 2014). …”

Neural Mechanisms of Motion Detection, Integration, and Segregation: From Biology to Artificial Image Processing Systems

“…Transparent and semitransparent motion occurs whenever multiple motions are presented in the same part of visual space moving in different directions or with different speeds. The model of Raudies and Neumann [17] investigates the necessary mechanisms underlying initial motion detection, the required representations for velocity coding, and the integration and segregation of motion stimuli to account for the perception of transparent motion.…”

“…Despite its simplicity, the model is able to explain experimental data and, without parameter changes, to successfully process realworld data used for model benchmarking [12,13]. In all, the paper summarizes some previous work of the authors, namely, work of [14][15][16][17] by using a common framework of model description. Most importantly, the framework has been extended such that different neural interaction schemes can be utilized in different variants of the model.…”

Modeling Binocular and Motion Transparency Processing by Local Center-Surround Interactions

“…In the second step of the three-stage processing cascade, FB from other model areas or an attention signal (Raudies & Neumann, 2010) could be included in principle. However, in the current model, areas V2 or MT are the highest areas in the model and do not receive any modulating input, nor do they incorporate any attention signal.…”