Peters Benjamin, Kriegeskorte Nikolaus
Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
Department of Psychology, Columbia University, New York, NY, USA.
Nat Hum Behav. 2021 Sep;5(9):1127-1144. doi: 10.1038/s41562-021-01194-6. Epub 2021 Sep 20.
Human visual perception carves a scene at its physical joints, decomposing the world into objects, which are selectively attended, tracked and predicted as we engage our surroundings. Object representations emancipate perception from the sensory input, enabling us to keep in mind that which is out of sight and to use perceptual content as a basis for action and symbolic cognition. Human behavioural studies have documented how object representations emerge through grouping, amodal completion, proto-objects and object files. By contrast, deep neural network models of visual object recognition remain largely tethered to sensory input, despite achieving human-level performance at labelling objects. Here, we review related work in both fields and examine how these fields can help each other. The cognitive literature provides a starting point for the development of new experimental tasks that reveal mechanisms of human object perception and serve as benchmarks driving the development of deep neural network models that will put the object into object recognition.
人类视觉感知在物理节点处剖析场景，将世界分解为物体，在我们与周围环境互动时，这些物体被有选择地关注、追踪和预测。物体表征使感知从感官输入中解放出来，使我们能够记住不在视线范围内的事物，并将感知内容作为行动和符号认知的基础。人类行为研究记录了物体表征是如何通过分组、非模态完成、原型物体和物体档案而出现的。相比之下，视觉物体识别的深度神经网络模型尽管在物体标注方面达到了人类水平的表现，但在很大程度上仍与感官输入相关联。在这里，我们回顾这两个领域的相关工作，并研究这些领域如何相互帮助。认知文献为开发新的实验任务提供了一个起点，这些任务揭示了人类物体感知的机制，并作为推动深度神经网络模型发展的基准，这些模型将使物体识别名副其实。
