Bowns Linda
Nottingham Visual Neuroscience, School of Psychology, The University of Nottingham, University Park, Nottingham NG7 2RD, United Kingdom.
Vision Res. 2011 Dec 8;51(23-24):2425-30. doi: 10.1016/j.visres.2011.09.014. Epub 2011 Oct 8.
Standard biologically inspired spatio-temporal energy models of how humans perceive moving two-dimensional patterns often have two critical stages. In the first stage, suitable filters are convolved with the pattern over time to extract information at the "component" level. Motion energy is then computed for each component. The second stage typically computes pattern velocity using the intersection of constraints rule (IOC). This paper describes a new implementation of the Component Level Feature Model (Bowns, 2002) that computes motion direction that is similar to these two stages except that it does not compute motion energy. Here the model computes direction for 200 randomly generated plaids. The output linearly matched that predicted by the IOC. The model was also able to predict the perceived direction even when it deviated from the IOC due to the following variables - speed ratio (Bowns, 1996); duration (Yo & Wilson, 1992); adaptation (Bowns & Alais, 2006). The model provides a novel explanation for each of the above and for why multiple directions can be represented for the same stimuli (Bowns & Alais, 2006); and why some second-order information attributed to non-linearities (Derrington, Badcock, & Holroyd, 1992) reverses perceived motion direction. Finally, CLFM is invariant to contrast and phase.
关于人类如何感知二维运动模式的标准生物启发式时空能量模型通常有两个关键阶段。在第一阶段，合适的滤波器随时间与模式进行卷积，以在“组件”级别提取信息。然后为每个组件计算运动能量。第二阶段通常使用约束交叉规则（IOC）计算模式速度。本文描述了组件级特征模型（Bowns，2002）的一种新实现，该模型计算运动方向，与这两个阶段类似，但不计算运动能量。在此，该模型为200个随机生成的格子计算方向。输出与IOC预测的结果呈线性匹配。即使由于以下变量——速度比（Bowns，1996）；持续时间（Yo和Wilson，1992）；适应（Bowns和Alais，2006）——导致与IOC有所偏差，该模型也能够预测感知到的方向。该模型为上述每种情况以及为何相同刺激可以呈现多个方向（Bowns和Alais，2006）；以及为何一些归因于非线性的二阶信息（Derrington、Badcock和Holroyd，1992）会使感知到的运动方向反转，提供了一种新颖的解释。最后，组件级特征模型对对比度和相位具有不变性。
