变数知觉
感知
对象(语法)
计算机科学
视力
人工智能
认知
分类
对比度(视觉)
视觉感受
视觉对象识别的认知神经科学
感觉系统
人工神经网络
认知科学
计算机视觉
心理学
认知心理学
神经科学
物理
天文
作者
Benjamin Peters,Nikolaus Kriegeskorte
标识
DOI:10.1038/s41562-021-01194-6
摘要
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.
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