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Toward a unified model of face and object recognition in the human visual system

机译:建立人类视觉系统中人脸和物体识别的统一模型

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Our understanding of the mechanisms and neural substrates underlying visual recognition has made considerable progress over the past 30 years. During this period, accumulating evidence has led many scientists to conclude that objects and faces are recognised in fundamentally distinct ways, and in fundamentally distinct cortical areas. In the psychological literature, in particular, this dissociation has led to a palpable disconnect between theories of how we process and represent the two classes of object. This paper follows a trend in part of the recognition literature to try to reconcile what we know about these two forms of recognition by considering the effects of learning. Taking a widely accepted, self-organizing model of object recognition, this paper explains how such a system is affected by repeated exposure to specific stimulus classes. In so doing, it explains how many aspects of recognition generally regarded as unusual to faces (holistic processing, configural processing, sensitivity to inversion, the other-race effect, the prototype effect, etc.) are emergent properties of category-specific learning within such a system. Overall, the paper describes how a single model of recognition learning can and does produce the seemingly very different types of representation associated with faces and objects.
机译:在过去30年中,我们对视觉识别基础的机制和神经基质的理解取得了长足的进步。在此期间,越来越多的证据使许多科学家得出结论,即从根本不同的方式以及在根本不同的皮层区域识别物体和面部。特别是在心理学文献中,这种分离导致我们如何处理和表示两种客体的理论之间明显脱节。本文遵循部分识别文献的趋势,试图通过考虑学习的影响来调和我们对这两种形式的识别的了解。本文采用了一种广为接受的,自组织的对象识别模型,解释了这种系统如何受到反复暴露于特定刺激类别的影响。通过这样做,它解释了通常被认为是面孔异常的识别的哪些方面(整体处理,配置处理,对反转的敏感性,其他种族效应,原型效应等)是类别内学习的新兴属性。这样的系统。总体而言,本文描述了一个单一的识别学习模型如何能够并且确实产生与面部和物体相关的看似非常不同的表示类型。

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