Abstract:

The perception of biological motion is crucial for the survival of human beings. By examining the models of biological motion perception, it is helpful to understand the complex process. The previous models emphasized how visual system encodes biological motion. The kinetic-geometric model for visual vector analysis originally developed in the study of perception of motion combinations of the mechanical type was applied to these biological motion patterns. For the "planarity assumption" in the interpretation of biological motion, the specific problem addressed is how the three dimensional structure and motions of animal limbs may be computed from the two dimensional motions of their projected images. Most recent studies taken the neural mechanism of biological motion perception into account. Hierarchical neural model by Giese and Poggio use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. Template-matching model from configural form cues is addressed by Lange and Lappe. They present a computational model based on neurally plausible assumptions to elucidate the contributions of motion and form signals to biological motion perception and the computations in the underlying brain network. The model simulates receptive fields for images of the static human body, as found by neuroimaging studies, and temporally integrates their responses by leaky integrator neurons. The model reveals a high correlation to data obtained by neurophysiological, neuroimaging, and psychophysical studies. These above models were proposed to explain key experimental results and plan new experiments relating to the recognition of biological movements. The paper also points to issues that cannot be answered by these models and by the available experimental results.

摘要：

人类可以从生物体的各种运动行为中获得丰富的社会信息，以满足社会交往的需求。视觉系统对生物运动信息的加工是一个复杂的过程，不同于对其他普通客体的加工能力。研究者们采用不同的方法，分别从各自的角度来研究这一过程，同时也建立了一系列模型。其中早期模型关注视觉系统加工生物运动信息的过程和方法；近期模型则采用脑成像手段构建生物运动信息加工的神经网络。这些模型包含了很多有价值的研究成果，但是也存在需要进一步完善的地方。