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具有局部中心-环绕相互作用和反馈的运动透明性处理模型。

A model of motion transparency processing with local center-surround interactions and feedback.

机构信息

Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215, USA.

出版信息

Neural Comput. 2011 Nov;23(11):2868-914. doi: 10.1162/NECO_a_00193. Epub 2011 Aug 18.

DOI:10.1162/NECO_a_00193
PMID:21851277
Abstract

Motion transparency occurs when multiple coherent motions are perceived in one spatial location. Imagine, for instance, looking out of the window of a bus on a bright day, where the world outside the window is passing by and movements of passengers inside the bus are reflected in the window. The overlay of both motions at the window leads to motion transparency, which is challenging to process. Noisy and ambiguous motion signals can be reduced using a competition mechanism for all encoded motions in one spatial location. Such a competition, however, leads to the suppression of multiple peak responses that encode different motions, as only the strongest response tends to survive. As a solution, we suggest a local center-surround competition for population-encoded motion directions and speeds. Similar motions are supported, and dissimilar ones are separated, by representing them as multiple activations, which occurs in the case of motion transparency. Psychophysical findings, such as motion attraction and repulsion for motion transparency displays, can be explained by this local competition. Besides this local competition mechanism, we show that feedback signals improve the processing of motion transparency. A discrimination task for transparent versus opaque motion is simulated, where motion transparency is generated by superimposing large field motion patterns of either varying size or varying coherence of motion. The model's perceptual thresholds with and without feedback are calculated. We demonstrate that initially weak peak responses can be enhanced and stabilized through modulatory feedback signals from higher stages of processing.

摘要

当在一个空间位置感知到多个相干运动时,就会出现运动透明性。例如,想象在一个阳光明媚的日子里,从公共汽车的窗户望出去,窗外的世界在经过,而公共汽车内的乘客的运动在窗户上反射。这两种运动在窗户上的叠加导致了运动透明性,这是很难处理的。可以使用竞争机制来减少在一个空间位置编码的所有运动的嘈杂和模糊的运动信号。然而,这种竞争导致了对编码不同运动的多个峰值响应的抑制,因为只有最强的响应才会存活。作为一种解决方案,我们建议对群体编码的运动方向和速度进行局部中心-环绕竞争。相似的运动通过表示为多个激活来支持,而不相似的运动则通过表示为多个激活来分离,这在运动透明性的情况下发生。运动吸引力和运动透明性显示的运动排斥等心理物理发现可以用这种局部竞争来解释。除了这种局部竞争机制,我们还表明反馈信号可以改善运动透明性的处理。模拟了一个用于透明与不透明运动的辨别任务,其中透明运动是通过叠加大视野的运动模式来产生的,这些运动模式的大小或运动的连贯性不同。计算了有和没有反馈的模型的感知阈值。我们证明,最初较弱的峰值响应可以通过来自处理的更高阶段的调制反馈信号来增强和稳定。

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