Real-world size of objects serves as an axis of object space.

ABSTRACT: Our mind can represent various objects from physical world in an abstract and complex high-dimensional object space, with axes encoding critical features to quickly and accurately recognize objects. Among object features identified in previous neurophysiological and fMRI studies that may serve as the axes, objects' real-world size is of particular interest because it provides not only visual information for broad conceptual distinctions between objects but also ecological information for objects' affordance. Here we use deep convolutional neural networks (DCNNs), which enable direct manipulation of visual experience and units' activation, to explore how objects' real-world size is extracted to construct the axis of object space. Like the human brain, the DCNNs pre-trained for object recogn

SUBMITTER: Huang T 

PROVIDER: S-EPMC9329427 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature