Coexistence of prosopagnosia and object agnosia - a novel approach to understanding how faces are represented in the brain

神经心理学文献中长期存在的争议之一涉及高级视觉的功能组织，以及不同类别视觉刺激的识别在多大程度上涉及模块化或分布式表征。物体识别的传统观点是，高级视觉区域包含许多功能专门化的模块（Kanwisher，2010）。例如，神经影像学研究一直在梭状回中发现一个区域-梭状回面部区域（FFA），对面部的反应比对非面部物体的反应更大（Kanwisher，2010）。与大脑这一区域的局灶性脑损伤一致，导致一种被称为面孔失认症的综合征，其中对面部的感知和识别有障碍，但对其他物体没有障碍（Damasio，Damasio &货车Hoesen，1982）。例如，尽管许多患有面孔失认症的个体具有面部选择性缺陷，但最近的研究已经显示出对一些非面部物体的损伤（Behrmann等人，2018年）。然而，目前还不清楚为什么面部识别的障碍对特定物体具有选择性。对这些发现的一种解释是，高级视觉区域对对象的类别没有选择性，而是具有基于刺激的更基本属性的分布式组织（Andrews等人，2015）。从这个角度来看，区域对面部做出反应的原因是因为这些区域中的神经表征对面部中通常发现的这些属性具有选择性。支持这一点的证据是，刺激的低水平视觉特性（如方向和空间频率信息的分布）与高水平视觉区域的神经反应模式之间存在正相关性（Rice，沃森，Hartley & Andrews，2014）。这些结果表明，类别选择性区域的出现可以解释为系统收敛的低级别功能，每个类别的特点的反应。这个建议的假设是，在面孔失认症中受影响的物体将具有与面孔相似的属性。解决这个问题的一种方法是测试患有发展性面孔失认症（DP）的患者识别和区分具有与面孔相似和不相似的低级别属性的物体的能力。例如，对面容失认症患者进行行为测试的一种方法是使用剑桥面部记忆测试，这是一种延迟匹配样本（DMS）任务。在这项任务中，面孔失认症患者将被显示面孔以及与面孔具有相似和不相似的低级别视觉特性的非面孔图像。在一定的延迟之后，参与者将完成一个强制选择识别阶段，在这个阶段，他们必须在一系列图像中识别出与样本匹配的图像。准确度和反应时间将作为因变量进行测量。预测是，面孔失认症患者将表现出选择性缺陷的视觉刺激，具有类似的低级别属性的面孔，但他们的表现将是正常的图像与不同的属性的面孔。第二种方法是使用fMRI来测量FFA的选择性是否可以通过对低级别图像属性的敏感性来解释。为了解决这个问题，我们将比较神经反应模式和这种反应的大小，与健康参与者中具有与面部相似基本属性的物体相比，具有与面部不同属性的物体。我们的预测是，对于具有相似低级属性的对象，响应的幅度和神经响应模式可能更类似于面部，反映了对图像属性而不是图像类别的选择性。

One of the longstanding controversies in the neuropsychology literature concerns the functional organization of high-level vision, and the extent to which the recognition of different classes of visual stimuli engage modular or distributed representations. The conventional view of object recognition is that high-level visual areas contain a number of functionally specialized modules (Kanwisher, 2010). For example, neuroimaging studies have consistently found a region in the fusiform gyrus - the fusiform face area (FFA) that responds to faces more than to non-face objects (Kanwisher, 2010). Consistent with focal brain damage to this region of the brain leads to a syndrome known as prosopagnosia in which there is an impairment in the perception and recognition of faces, but not other objects (Damasio, Damasio & Van Hoesen, 1982).The modular view of high-level vision been challenged, recently, by studies that suggest a more distributed representation. For example, although many individuals with prosopagnosia have face-selective deficits, more recent studies have shown an impairment for some non-face objects (Behrmann et al., 2018). It remains unclear, however, why the impairment in face recognition is selective for particular objects. One explanation for these findings is that higher-level visual areas are not selective for categories of objects but rather have a distributed organization that is based on more basic properties of the stimulus (Andrews et al., 2015). From this perspective, the reason that regions respond to faces is because the neural representation in these regions is selective to these properties that are typically found in faces. Support for this has been demonstrated by the positive correlation that is found between low-level visual properties (such as the distribution of orientation and spatial frequency information) of the stimulus and patterns of neural response in the high-level visual areas (Rice, Watson, Hartley & Andrews, 2014). These results suggest that the appearance of category-selective regions may be explained by the systematic convergence of responses to low-level features that are characteristic of each category. The hypothesis of this proposal is that the objects affected in prosopagnosia will have similar properties to those found in faces. One way to address this issue is to test patients with developmental prosopagnosia (DP) on their ability to recognise and discriminate objects with similar and dissimilar low-level properties to faces. For example, one way to behaviourally test prosopagnosic patients is to use the Cambridge Face Memory Test which is a Delayed Match to Sample (DMS) task. In this task prosopagnosic patients will be shown faces as well as non-face images with similar and dissimilar low-level visual properties to faces. After a certain delay, participants will complete a forced-choice recognition phase where they will have to identify the image that matches the sample one among an array of images. Accuracy and reaction time will be measured as dependent variables. The prediction is that prosopagnosia patients will exhibit selective deficits for visual stimuli that have similar low-level properties to faces but their performance will be normal for images with dissimilar properties to faces. A second approach is to use fMRI to measure whether the selectivity of the FFA can be explained by the sensitivity to low-level image properties. To address this question, we will compare both the neural pattern of response and the magnitude of this response to objects that have similar basic properties to faces compared to objects that have dissimilar properties to faces in healthy participants. Our prediction is that both the magnitude of response and the neural pattern of response are likely to be more similar to faces for objects that are have similar low-level properties, reflecting selectivity to image properties rather than image category.