Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, United States.
Cognition. 2013 Feb;126(2):135-48. doi: 10.1016/j.cognition.2012.08.005. Epub 2012 Oct 25.
We study people's abilities to transfer object category knowledge across visual and haptic domains. If a person learns to categorize objects based on inputs from one sensory modality, can the person categorize these same objects when the objects are perceived through another modality? Can the person categorize novel objects from the same categories when these objects are, again, perceived through another modality? Our work makes three contributions. First, by fabricating Fribbles (3-D, multi-part objects with a categorical structure), we developed visual-haptic stimuli that are highly complex and realistic, and thus more ecologically valid than objects that are typically used in haptic or visual-haptic experiments. Based on these stimuli, we developed the See and Grasp data set, a data set containing both visual and haptic features of the Fribbles, and are making this data set freely available on the world wide web. Second, complementary to previous research such as studies asking if people transfer knowledge of object identity across visual and haptic domains, we conducted an experiment evaluating whether people transfer object category knowledge across these domains. Our data clearly indicate that we do. Third, we developed a computational model that learns multisensory representations of prototypical 3-D shape. Similar to previous work, the model uses shape primitives to represent parts, and spatial relations among primitives to represent multi-part objects. However, it is distinct in its use of a Bayesian inference algorithm allowing it to acquire multisensory representations, and sensory-specific forward models allowing it to predict visual or haptic features from multisensory representations. The model provides an excellent qualitative account of our experimental data, thereby illustrating the potential importance of multisensory representations and sensory-specific forward models to multisensory perception.
我们研究人们在视觉和触觉领域之间转移物体类别知识的能力。如果一个人学会基于一种感觉模式的输入来对物体进行分类,那么当物体通过另一种感觉模式感知时,这个人能否对这些相同的物体进行分类?当这些物体再次通过另一种感觉模式感知时,这个人能否对来自相同类别的新物体进行分类?我们的工作有三个贡献。首先,通过制造 Fribbles(具有类别结构的三维多部分物体),我们开发了视觉-触觉刺激,这些刺激非常复杂和逼真,因此比通常用于触觉或视觉-触觉实验的物体更具生态有效性。基于这些刺激,我们开发了 See and Grasp 数据集,该数据集包含 Fribbles 的视觉和触觉特征,并在万维网上免费提供该数据集。其次,与之前的研究(例如询问人们是否在视觉和触觉领域之间转移物体身份知识的研究)互补,我们进行了一项实验,评估人们是否在这些领域之间转移物体类别知识。我们的数据清楚地表明我们可以做到这一点。第三,我们开发了一种计算模型,该模型可以学习原型 3D 形状的多感觉表示。与之前的工作类似,该模型使用形状基元来表示零件,以及基元之间的空间关系来表示多部分物体。然而,它的独特之处在于它使用贝叶斯推理算法来获取多感觉表示,以及用于从多感觉表示中预测视觉或触觉特征的感觉特异性前向模型。该模型为我们的实验数据提供了极好的定性解释,从而说明了多感觉表示和感觉特异性前向模型对多感觉感知的重要性。