Wardle Susan G, Baker Chris
Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, 20892, USA.
F1000Res. 2020 Jun 11;9. doi: 10.12688/f1000research.22296.1. eCollection 2020.
Object recognition is the ability to identify an object or category based on the combination of visual features observed. It is a remarkable feat of the human brain, given that the patterns of light received by the eye associated with the properties of a given object vary widely with simple changes in viewing angle, ambient lighting, and distance. Furthermore, different exemplars of a specific object category can vary widely in visual appearance, such that successful categorization requires generalization across disparate visual features. In this review, we discuss recent advances in understanding the neural representations underlying object recognition in the human brain. We highlight three current trends in the approach towards this goal within the field of cognitive neuroscience. Firstly, we consider the influence of deep neural networks both as potential models of object vision and in how their representations relate to those in the human brain. Secondly, we review the contribution that time-series neuroimaging methods have made towards understanding the temporal dynamics of object representations beyond their spatial organization within different brain regions. Finally, we argue that an increasing emphasis on the context (both visual and task) within which object recognition occurs has led to a broader conceptualization of what constitutes an object representation for the brain. We conclude by identifying some current challenges facing the experimental pursuit of understanding object recognition and outline some emerging directions that are likely to yield new insight into this complex cognitive process.
物体识别是基于所观察到的视觉特征组合来识别物体或类别的能力。鉴于眼睛接收到的与给定物体属性相关的光模式会随着视角、环境照明和距离的简单变化而有很大差异，这是人类大脑的一项非凡壮举。此外，特定物体类别的不同示例在视觉外观上可能有很大差异，因此成功分类需要跨越不同视觉特征进行概括。在本综述中，我们讨论了在理解人类大脑中物体识别背后的神经表征方面的最新进展。我们强调了认知神经科学领域内实现这一目标的方法目前的三个趋势。首先，我们考虑深度神经网络作为物体视觉的潜在模型的影响，以及它们的表征与人类大脑中的表征如何相关。其次，我们回顾了时间序列神经成像方法对理解物体表征的时间动态所做出的贡献，这些动态超出了它们在不同脑区的空间组织。最后，我们认为越来越强调物体识别发生的背景（视觉和任务）已经导致对大脑中构成物体表征的内容有了更广泛的概念化。我们通过识别当前在理解物体识别的实验研究中面临的一些挑战来得出结论，并概述一些可能会对这个复杂认知过程产生新见解的新兴方向。
