The role of shape and texture within the neural representation of faces.

拟议的博士项目将加入一个动态和快速增长的文献机构，关于人类大脑如何计算和编码视觉刺激的解开。长期以来，人们已经确定，在大脑中存在对某些类型的刺激更敏感的区域（参考），例如，位于外侧枕叶皮质（纹外体区）的区域已被证明对身体及其组成成分做出反应，但对物体的反应最小（Downing等人，2001）。同样，神经影像学研究揭示了枕叶和颞叶区域的网络，这些区域形成了面部视觉分析的核心系统（Haxby，霍夫曼和Gobbini，2000; Kanwisher，McDermott和Chun，1997）。这些研究一致发现，在这个网络中，对面部的反应比其他视觉刺激更强烈的区域：梭状面区（FFA），枕面部区（OFA）和后上级颞沟（pSTS）。已经发现这些组成区域处理面部的不同方面，例如; FFA与身份的处理有关，而pSTS与面部的可变方面（如表情或观点）的处理有关（Haxby，霍夫曼和Gobbini，2000）。虽然有证据支持这种普遍的分离，但这些区域的神经代码并不清楚。比如说，是基于诸如身份和表情之类的高级属性的神经代码，或者可以是更基本和一般的神经代码来支持这些面部区域中的表示。（这可以被认为是由于面部特征的形状和位置以及面部内更广泛的反射模式而引起的反射率的突然变化）在用于识别熟悉面孔的不变表示中起着关键作用（Burton，Jenkins，汉考克&白色，2005; Russell & Sinha，2007）。例如，如果纹理呈现在标准化形状上（Burton，Jenkins，汉考克&白色，2005），或者当形状通过拉伸图像而失真时（Hole，乔治，Eaves & Rasek，2002），熟悉的面部识别基本上不受影响。相比之下，缺乏任何表面属性的面部线条画通常不足以识别Davies，Ellis和Shepherd，1978）。总之，这些研究表明，纹理是面部识别的主要线索（Burton，2013）。相比之下，表情的判断通常被认为主要基于关键表达特征的形状和位置，如眉毛，眼睛，鼻子和嘴巴。形状线索在面部表情识别中的重要性的证据来自对比度反转（如照片底片）。在对比度反转图像中，定义特征形状属性的边缘保持在相同的位置，尽管整体表面属性（纹理）发生了巨大变化。尽管众所周知，对比度否定对面部身份识别具有很大的破坏性（布鲁斯& Young，1998），但事实证明，基于面部表情的判断在对比度反转的图像中仍然是可能的（布鲁斯& Young，1998; Harris，Young & Andrews，2014;白色，2001）。目前的建议将确定形状和纹理在人脸区域中的作用。其目的是更好地了解大脑如何代表有关面部的信息。这项研究的主要假设是，面部区域的神经表征，而不是直接与身份或表达等高级概念联系在一起，而是基于更基本的潜在表征，如形状或纹理。这种类型的表示将提供一种更通用的代码，可以用于以更灵活的方式从面部解码信息。

The proposed PhD project will join a dynamic and rapidly growing body of literature concerning the unraveling of how the human brain computes and codes for visual stimuli. It has been long established that within the brain there are areas that are more sensitive for certain types of stimuli (reference) for example an area located in the lateral occipital cortex (the extrastriate body area) has been shown to respond to bodies and their constituent components but minimally for objects (Downing et al, 2001). Likewise neuroimaging studies have revealed a network of regions in the occipital and temporal lobes, which form a core system for the visual analysis of faces (Haxby, Hoffman & Gobbini, 2000; Kanwisher, McDermott & Chun, 1997). These studies have consistently found regions within this network that show stronger responses to faces than other visual stimuli: the fusiform face area (FFA), the occipital face area (OFA) and the posterior superior temporal sulcus (pSTS). These constituent areas have been found to process different aspects of faces, for example; the FFA is linked to the processing of identity and the pSTS is linked to the processing of changeable aspects of faces such as expression or viewpoint (Haxby, Hoffman & Gobbini, 2000). Although there is evidence in support of this general dissociation, the neural code in these regions is not clear. For example, is the neural code based on high-level properties such as identity and expression or can a more basic and general neural code underpin the representation in these face regions.A number of studies have shown that the texture (which can be thought of as abrupt changes in reflectance due to the shapes and positions of facial features and a more broader pattern of reflectance within the face) plays a critical role in the invariant representation that is used for the recognition of familiar faces (Burton, Jenkins, Hancock & White, 2005; Russell & Sinha, 2007). For example, familiar face recognition is not substantially affected if the texture is presented on a standardized shape (Burton, Jenkins, Hancock & White, 2005) or when the shape is distorted by stretching the image (Hole, George, Eaves & Rasek, 2002). In contrast, line drawings of faces, which lack any surface properties, are not usually sufficient for recognition Davies, Ellis, & Shepherd, 1978). Together, these studies suggest that texture is the dominant cue in face recognition (Burton, 2013).In contrast judgements of expression are often thought to be based primarily on the shapes and positions of critical expressive features such as the eyebrows, eyes, nose and mouth. Evidence for the importance of shape cues in facial expression recognition comes from contrast reversal (as in a photo negative). In a contrast-reversed image, the edges that define feature shape properties remain in the same positions, despite the huge change in overall surface properties (texture). Although contrast negation is well-known to be very disruptive of facial identity recognition (Bruce & Young, 1998) it turns out that judgements based on facial expression are still possible in contrast-reversed images (Bruce & Young, 1998; Harris, Young & Andrews, 2014; White, 2001). The current proposal will determine the role of shape and texture in human face regions. The aim is to better understand how the brain represents information about faces. The main hypothesis of this study is that the neural representation in face regions, rather than being directly linked to high-level concepts such as identity or expression, is based on a more basic underlying representation, such as shape or texture. This type of representation would provide a more general code that could be used to decode information from faces in a more flexible way.