Lewicki Michael S, Olshausen Bruno A, Surlykke Annemarie, Moss Cynthia F
Department of Electrical Engineering and Computer Science, Case Western Reserve University Cleveland, OH, USA.
Helen Wills Neuroscience Institute, School of Optometry, Redwood Center for Theoretical Neuroscience, University of California at Berkeley Berkeley, CA, USA.
Front Psychol. 2014 Apr 1;5:199. doi: 10.3389/fpsyg.2014.00199. eCollection 2014.
The problem of scene analysis has been studied in a number of different fields over the past decades. These studies have led to important insights into problems of scene analysis, but not all of these insights are widely appreciated, and there remain critical shortcomings in current approaches that hinder further progress. Here we take the view that scene analysis is a universal problem solved by all animals, and that we can gain new insight by studying the problems that animals face in complex natural environments. In particular, the jumping spider, songbird, echolocating bat, and electric fish, all exhibit behaviors that require robust solutions to scene analysis problems encountered in the natural environment. By examining the behaviors of these seemingly disparate animals, we emerge with a framework for studying scene analysis comprising four essential properties: (1) the ability to solve ill-posed problems, (2) the ability to integrate and store information across time and modality, (3) efficient recovery and representation of 3D scene structure, and (4) the use of optimal motor actions for acquiring information to progress toward behavioral goals.
在过去几十年里，场景分析问题在许多不同领域都得到了研究。这些研究为场景分析问题带来了重要的见解，但并非所有这些见解都得到了广泛认可，而且当前方法仍存在严重缺陷，阻碍了进一步的进展。在这里，我们认为场景分析是所有动物都要解决的一个普遍问题，并且我们可以通过研究动物在复杂自然环境中面临的问题来获得新的见解。特别是，跳蛛、鸣禽、回声定位蝙蝠和电鱼，都表现出一些行为，这些行为需要对在自然环境中遇到的场景分析问题有稳健的解决方案。通过研究这些看似不同的动物的行为，我们得出了一个用于研究场景分析的框架，该框架包含四个基本属性：（1）解决不适定问题的能力，（2）跨时间和模态整合与存储信息的能力，（3）高效恢复和表示三维场景结构的能力，以及（4）使用最优运动动作获取信息以朝着行为目标前进的能力。
