Computational Social Cognition, Agency for Science, Technology and Research, Institute of High Performance Computing, 1 Fusionopolis Way, #16-16, Connexis 138632, Singapore.
Behav Res Methods. 2011 Sep;43(3):643-65. doi: 10.3758/s13428-011-0128-2.
People spontaneously ascribe intentions on the basis of observed behavior, and research shows that they do this even with simple geometric figures moving in a plane. The latter fact suggests that 2-D animations isolate critical information--object movement--that people use to infer the possible intentions (if any) underlying observed behavior. This article describes an approach to using motion information to model the ascription of intentions to simple figures. Incremental chart parsing is a technique developed in natural-language processing that builds up an understanding as text comes in one word at a time. We modified this technique to develop a system that uses spatiotemporal constraints about simple figures and their observed movements in order to propose candidate intentions or nonagentive causes. Candidates are identified via partial parses using a library of rules, and confidence scores are assigned so that candidates can be ranked. As observations come in, the system revises its candidates and updates the confidence scores. We describe a pilot study demonstrating that people generally perceive a simple animation in a manner consistent with the model.
人们会根据观察到的行为自发地归因于意图，研究表明，即使是在简单的几何图形在平面上移动的情况下，他们也会这样做。后一事实表明，2D 动画隔离了关键信息——物体运动——人们可以用这些信息来推断观察到的行为背后可能存在的意图（如果有的话）。本文描述了一种使用运动信息来对简单图形进行意图归因的方法。增量图表解析是一种在自然语言处理中开发的技术，它通过一次输入一个词来逐步建立对文本的理解。我们修改了这个技术来开发一个系统，该系统使用关于简单图形及其观察到的运动的时空约束，以提出候选意图或非代理原因。候选对象是通过使用规则库进行部分解析来识别的，并分配置信分数，以便对候选对象进行排名。随着观察结果的输入，系统会修改其候选对象并更新置信分数。我们描述了一项初步研究，表明人们通常会以与模型一致的方式感知简单的动画。
