Department of Brain and Cognitive Sciences, University of Rochester.
Psychol Rev. 2017 Nov;124(6):740-761. doi: 10.1037/rev0000086. Epub 2017 Sep 14.
Despite decades of research, little is known about how people visually perceive object shape. We hypothesize that a promising approach to shape perception is provided by a "visual perception as Bayesian inference" framework which augments an emphasis on visual representation with an emphasis on the idea that shape perception is a form of statistical inference. Our hypothesis claims that shape perception of unfamiliar objects can be characterized as statistical inference of 3D shape in an object-centered coordinate system. We describe a computational model based on our theoretical framework, and provide evidence for the model along two lines. First, we show that, counterintuitively, the model accounts for viewpoint-dependency of object recognition, traditionally regarded as evidence against people's use of 3D object-centered shape representations. Second, we report the results of an experiment using a shape similarity task, and present an extensive evaluation of existing models' abilities to account for the experimental data. We find that our shape inference model captures subjects' behaviors better than competing models. Taken as a whole, our experimental and computational results illustrate the promise of our approach and suggest that people's shape representations of unfamiliar objects are probabilistic, 3D, and object-centered. (PsycINFO Database Record
尽管已经进行了几十年的研究，但人们对物体形状的视觉感知方式仍知之甚少。我们假设，“视觉感知作为贝叶斯推断”框架为形状感知提供了一种很有前途的方法，该框架不仅强调视觉表现，还强调形状感知是一种统计推断的想法。我们的假设声称，对不熟悉物体的形状感知可以被描述为在以物体为中心的坐标系中对 3D 形状进行统计推断。我们描述了一个基于我们理论框架的计算模型，并从两个方面提供了证据来支持该模型。首先，我们表明，与直观的想法相反，该模型解释了物体识别的视角依赖性，这传统上被认为是反对人们使用 3D 以物体为中心的形状表示的证据。其次，我们报告了使用形状相似性任务的实验结果，并对现有模型解释实验数据的能力进行了广泛评估。我们发现，我们的形状推断模型比竞争模型更能捕捉到被试的行为。总的来说，我们的实验和计算结果说明了我们方法的前景，并表明人们对不熟悉物体的形状表示是概率性的、3D 的和以物体为中心的。
