A unified theory for perception of physical and social dynamics

人类自发地赋予我们所看到的世界以物理和社会意义：我们不仅看到了无生命物体之间的物理互动，而且还看到了相互作用的人类的有意行为。这个项目将调查人类如何从物体和人的运动中感知和理解物理和社会动态。更大的目标是解释感知和推理是如何在动态情况下共同指导行为的。该项目的发现将有助于装备下一代智能系统，使其具备在新情况下运行所需的视觉推理能力。这样的系统将需要执行对人类来说具有挑战性和耗时的复杂任务。该项目将把行为实验与计算建模相结合，以开发和测试统一的理论。该理论规定了如何从图像直接构造力表示，以及如何使用所发现的力结构来推断动态刺激中涉及的因果关系。具体地说，贝叶斯模型将被开发为两个基本的计算组件：(1)生成模型，用于学习控制物理和社会场景中无生命物体和人类代理运动的潜力结构的表示；(2)推理模型，用于基于力结构的时空配置来识别因果关系和意图的隐藏状态。计算模型将被用来合成动态刺激，以实例化大范围的物理和社会情况。然后，这些刺激将被用于大规模分析人类行为。一系列心理物理实验和模型比较将检验该理论的关键计算部分。该项目还将为对认知科学和人工智能之间的跨学科研究感兴趣的学生提供极好的培训机会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Humans spontaneously endow the world we see with physical and social meaning: we “see” not only physical interactions among inanimate objects, but also the intentional behaviors of interacting humans. This project will investigate how humans perceive and understand physical and social dynamics from the movements of objects and people. The larger goal is to explain how perception and reasoning operate together to guide behavior in dynamic situations. Findings from this project will help equip the next generation of intelligent systems with the visual reasoning ability required to operate in novel situations. Such systems will need to perform complex tasks that are challenging and time-consuming for humans.The project will integrate behavioral experiments with computational modeling to develop and test a unified theory. The theory specifies how force representations can be constructed directly from images and how the discovered force structures can be used to infer causal relations involved in dynamic stimuli. Specifically, Bayesian models will be developed with two basic computational components: (1) generative models to learn representations of latent force structures governing movements of inanimate objects and human agents in physical and social scenes, and (2) reasoning models to identify causal relations and hidden states of intentions based on spatial-temporal configurations of force structures. Computational models will be used to synthesize dynamic stimuli that instantiate a large range of physical and social situations. These stimuli will then be used to analyze human behaviors at a large scale. A series of psychophysical experiments and model comparisons will test the key computational components of the theory. The project will also provide excellent training opportunities for students who are interested in interdisciplinary research at the interface between cognitive science and artificial intelligence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.