Jiang Xiong, Rosen Ezra, Zeffiro Thomas, Vanmeter John, Blanz Volker, Riesenhuber Maximilian
Department of Neuroscience, Georgetown University Medical Center, Washington, District of Columbia 20007, USA.
Neuron. 2006 Apr 6;50(1):159-72. doi: 10.1016/j.neuron.2006.03.012.
Understanding the neural mechanisms underlying object recognition is one of the fundamental challenges of visual neuroscience. While neurophysiology experiments have provided evidence for a "simple-to-complex" processing model based on a hierarchy of increasingly complex image features, behavioral and fMRI studies of face processing have been interpreted as incompatible with this account. We present a neurophysiologically plausible, feature-based model that quantitatively accounts for face discrimination characteristics, including face inversion and "configural" effects. The model predicts that face discrimination is based on a sparse representation of units selective for face shapes, without the need to postulate additional, "face-specific" mechanisms. We derive and test predictions that quantitatively link model FFA face neuron tuning, neural adaptation measured in an fMRI rapid adaptation paradigm, and face discrimination performance. The experimental data are in excellent agreement with the model prediction that discrimination performance should asymptote as faces become dissimilar enough to activate different neuronal populations.
理解物体识别背后的神经机制是视觉神经科学的基本挑战之一。虽然神经生理学实验为基于日益复杂的图像特征层次结构的“简单到复杂”处理模型提供了证据，但面部处理的行为和功能磁共振成像研究被解释为与该模型不符。我们提出了一个基于神经生理学且合理的、基于特征的模型，该模型定量解释了面部辨别特征，包括面部倒置和“构型”效应。该模型预测，面部辨别基于对面部形状具有选择性的单元的稀疏表示，无需假定额外的“面部特异性”机制。我们推导并测试了一些预测，这些预测定量地将模型梭状回面孔区（FFA）面部神经元调谐、功能磁共振成像快速适应范式中测量的神经适应以及面部辨别性能联系起来。实验数据与模型预测高度一致，即随着面孔变得足够不同以激活不同的神经元群体，辨别性能应趋于平稳。
