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 behavioral studies have documented how object representations emerge
through grouping, amodal completion, proto-objects, and object files. Deep
neural network (DNN) models of visual object recognition, by contrast, remain
largely tethered to the sensory input, despite achieving human-level
performance at labeling 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
development of deep neural network models that will put the object into object
recognition.

本研究回顾了关于人类目标感知和深度神经网络模型在目标识别方面的相关工作，并探讨了这两个领域如何相互促进，提供了发展新实验任务和推动深度神经网络模型中的目标识别的基准的认知文献和实验任务。