Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213–3890, USA.
Cogn Neuropsychol. 2011 May;28(3-4):251-75. doi: 10.1080/02643294.2011.609812.
A key issue that continues to generate controversy concerns the nature of the psychological, computational, and neural mechanisms that support the visual recognition of objects such as faces and words. While some researchers claim that visual recognition is accomplished by category-specific modules dedicated to processing distinct object classes, other researchers have argued for a more distributed system with only partially specialized cortical regions. Considerable evidence from both functional neuroimaging and neuropsychology would seem to favour the modular view, and yet close examination of those data reveals rather graded patterns of specialization that support a more distributed account. This paper explores a theoretical middle ground in which the functional specialization of brain regions arises from general principles and constraints on neural representation and learning that operate throughout cortex but that nonetheless have distinct implications for different classes of stimuli. The account is supported by a computational simulation, in the form of an artificial neural network, that illustrates how cooperative and competitive interactions in the formation of neural representations for faces and words account for both their shared and distinctive properties. We set out a series of empirical predictions, which are also examined, and consider the further implications of this account.
一个持续引发争议的关键问题涉及支持视觉识别物体（如面孔和单词）的心理、计算和神经机制的性质。虽然一些研究人员声称视觉识别是通过专门处理不同物体类别的类别特定模块来完成的，但其他研究人员则认为存在一个更分布式的系统，只有部分专门化的皮质区域。来自功能神经影像学和神经心理学的大量证据似乎支持模块观点，但对这些数据的仔细检查显示出更为渐变的专业化模式，支持更分布式的解释。本文探讨了一个理论上的中间立场，即大脑区域的功能专业化是由神经表示和学习的一般原则和约束产生的，这些原则和约束在整个大脑中起作用，但对不同类别的刺激有不同的影响。该解释得到了一个计算模拟的支持，该模拟以人工神经网络的形式说明了形成面孔和单词的神经表示的合作和竞争相互作用如何解释它们的共同和独特属性。我们提出了一系列经验预测，并对其进行了检验，还考虑了这一解释的进一步影响。
