Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, United Kingdom; Perception in Action Research Centre and Department of Cognitive Science Macquarie University, Australia.
Department of Computer Science, School of Mathematics, Statistics, and Computer Science, University of Tehran, Iran.
Neuroimage. 2021 Jun;233:117896. doi: 10.1016/j.neuroimage.2021.117896. Epub 2021 Mar 3.
Humans are fast and accurate when they recognize familiar faces. Previous neurophysiological studies have shown enhanced representations for the dichotomy of familiar vs. unfamiliar faces. As familiarity is a spectrum, however, any neural correlate should reflect graded representations for more vs. less familiar faces along the spectrum. By systematically varying familiarity across stimuli, we show a neural familiarity spectrum using electroencephalography. We then evaluated the spatiotemporal dynamics of familiar face recognition across the brain. Specifically, we developed a novel informational connectivity method to test whether peri-frontal brain areas contribute to familiar face recognition. Results showed that feed-forward flow dominates for the most familiar faces and top-down flow was only dominant when sensory evidence was insufficient to support face recognition. These results demonstrate that perceptual difficulty and the level of familiarity influence the neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition.
当人们识别熟悉的面孔时,速度和准确性都很高。先前的神经生理学研究表明,熟悉面孔与不熟悉面孔的二分法存在增强的表示。然而,由于熟悉是一个连续体,任何神经相关物都应该反映出沿着该连续体对更熟悉和不太熟悉的面孔的分级表示。通过系统地改变刺激的熟悉度,我们使用脑电图显示了一个神经熟悉度谱。然后,我们评估了整个大脑中熟悉面孔识别的时空动态。具体来说,我们开发了一种新颖的信息连接方法来测试前额叶大脑区域是否有助于熟悉面孔识别。结果表明,对于最熟悉的面孔,前馈流占主导地位,而只有在感官证据不足以支持面孔识别时,自上而下的流才占主导地位。这些结果表明,感知难度和熟悉程度会影响熟悉面孔的神经表示,以及前额叶神经网络对熟悉面孔识别的贡献程度。