Duyck Stefanie, Costantino Andrea I, Bracci Stefania, Op de Beeck Hans
Brain and Cognition, Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy.
Commun Biol. 2024 Dec 31;7(1):1718. doi: 10.1038/s42003-024-07415-8.
The functional organization of the human object vision pathway distinguishes between animate and inanimate objects. To understand animacy perception, we explore the case of zoomorphic objects resembling animals. While the perception of these objects as animal-like seems obvious to humans, such "Animal bias" is a striking discrepancy between the human brain and deep neural networks (DNNs). We computationally investigated the potential origins of this bias. We successfully induced this bias in DNNs trained explicitly with zoomorphic objects. Alternative training schedules failed to cause an Animal bias. We considered the superordinate distinction between animate and inanimate classes, the sensitivity for faces and bodies, the bias for shape over texture, the role of ecologically valid categories, recurrent connections, and language-informed visual processing. These findings provide computational support that the Animal bias for zoomorphic objects is a unique property of human perception yet can be explained by human learning history.
人类客体视觉通路的功能组织能够区分有生命和无生命的物体。为了理解对有生命的感知，我们探讨了类似动物的兽形物体的情况。虽然人类将这些物体视为类似动物的感知似乎很明显，但这种“动物偏好”是人类大脑与深度神经网络（DNN）之间的显著差异。我们通过计算研究了这种偏好的潜在来源。我们成功地在明确用兽形物体训练的DNN中诱导出了这种偏好。其他训练方案未能导致动物偏好。我们考虑了有生命和无生命类别之间的上级区分、对面部和身体的敏感度、形状优于纹理的偏好、生态有效类别的作用、循环连接以及语言引导的视觉处理。这些发现提供了计算支持，即对兽形物体的动物偏好是人类感知的独特属性，但可以通过人类学习历史来解释。
