From Unfamiliar Face Processing to Familiar Face Recognition: Behavioural and Neural Effects of Real World Face Learning

从陌生的人脸处理到熟悉的人脸识别：现实世界人脸学习的行为和神经效应

• 批准号: 2280616
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2280616
• 关键词: Unfamiliar; Face; Processing; Familiar; Recognition

There are large differences between familiar and unfamiliar face recognition, however, progress in the field has been slow (Burton, 2013). Familiar face recognition is faster (Clutterbuck & Johnston, 2002), more accurate (Burton, Wilson, Cowan & Bruce, 1999), robust to changes in viewpoint (Hill & Bruce, 1996) and expression (Bruce, 1982). To date, these studies have used traditional methods of face familiarisation in which participants are exposed to different pictures of the target face. The central aim of my proposed PhD project is to establish a new method of face familiarisation based on real-world social interactions in which participants interact with actors whom they have never met. This is important for three reasons; firstly, face recognition happens every day and in every social interaction, my research adds real world validity to lab-based experiments. Secondly, these real-world interactions are likely to drive stronger neural responses that will increase the power of my proposed fMRI studies. Finally, it is important to improve the methods used to tease apart the differences between familiar and unfamiliar face recognition as they have important implications in the use of eye-witness testimony (Brewer & Wells, 2011) and in the use of photo ID in airports and other high-security environments (White, Kemp, Jenkins, Matheson & Burton, 2014).Although the behavioural effects of familiar face recognition are well studied, the neural correlates are less well established. The occipital face area, fusiform face area, and superior temporal sulcus are face-selective regions, they respond more to faces than objects or scenes (Kanwisher, McDermott & Chun, 1997; Pitcher, Dilks, Saxe, Triantafyllou & Kanwisher, 2011). However, neuroimaging and neurostimulation studies examining face familiarity in the brain have produced mixed results. Some fMRI studies find increased FFA activation to familiar faces compared to unfamiliar (Rotshtein, Henson, Treves, Driver & Dolan, 2005), and others find no such modulation (Dubois et al., 1999). Some TMS studies demonstrate the OFA represents face parts in an early processing stage prior to identity recognition (Pitcher, Walsh, Yovel & Duchaine, 2007; Pitcher, Walsh & Duchaine, 2011), whereas other TMS experiments find the OFA plays a role in face recognition (Solomon-Harris, Mullin & Steeves, 2013).Crucially, all these prior studies have used conventional methods of face familiarisation. This is an issue as such studies use 1 photo to represent the face to be learnt. However, faces display within-person variation which cannot be captured by 1 photograph (Jenkins, White, Van Montfort & Burton, 2011). Familiarisation techniques using ambient images instead, natural photographs of an individual under different lighting conditions and angles etc., could be used as they are not blind to within-person variation. Moreover, live interaction with a target face also captures within-person variation but in an ecologically valid way.Furthermore, face learning is not yet fully understood. It is known that longer durations of time observing a face leads to better face recognition (Downes et al., 1997), however, more recent studies have shown that exposure to variation during face learning, e.g. presenting faces from different viewpoints, also leads to better face recognition (Dwyer, Mundy, Vladeanu & Honey, 2009). The precise weightings of variation and length of time have not yet been systematically tested. Therefore, further study is required to tease apart the different factors of face learning. In my master's project (Sliwinska et al., in prep), participants interacted with target identities in person, a new highly ecologically valid familiarisation technique, which lead to 15% increases in recognition accuracy. In my PhD, I aim to refine this new and promising method to clarify the conflicting literature on the OFA and FFA's roles using fMRI and TMS.

熟悉和不熟悉的人脸识别之间存在很大差异，但该领域的进展却很缓慢（Burton，2013）。熟悉的人脸识别速度更快（Clutterbuck＆Johnston，2002），更准确（Burton，Wilson，Cowan＆Bruce，1999），对观点变化（Hill＆Bruce，1996）和表情变化（Bruce，1982）具有鲁棒性。迄今为止，这些研究都使用传统的面部熟悉方法，让参与者接触目标面部的不同图片。我提出的博士项目的中心目标是建立一种基于现实世界社交互动的新面孔熟悉方法，其中参与者与他们从未见过的演员互动。这很重要，原因有三个：首先，人脸识别每天都会在每次社交互动中发生，我的研究为基于实验室的实验增加了现实世界的有效性。其次，这些现实世界的相互作用可能会驱动更强烈的神经反应，从而增强我提出的功能磁共振成像研究的力量。最后，改进用于区分熟悉和陌生面部识别之间差异的方法非常重要，因为它们对于目击者证词的使用（Brewer＆Wells，2011）以及在机场和其他高安全环境中使用照片ID（White，Kemp，Jenkins，Matheson＆Burton，2014）具有重要意义。 相关性还不太确定。枕骨面部区域、梭状面部区域和颞上沟是面部选择性区域，它们对面部的反应比对物体或场景的反应更大(Kanwisher, McDermott & Chun, 1997; Pitcher, Dilks, Saxe, Triantafyllou & Kanwisher, 2011)。然而，检查大脑中面孔熟悉度的神经影像学和神经刺激研究产生了不同的结果。一些功能磁共振成像研究发现，与不熟悉的面孔相比，熟悉的面孔的 FFA 激活有所增加（Rotshtein、Henson、Treves、Driver & Dolan，2005），而其他研究则没有发现这种调节（Dubois 等人，1999）。一些 TMS 研究表明，OFA 在身份识别之前的早期处理阶段代表面部部位（Pitcher, Walsh, Yovel & Duchaine, 2007；Pitcher, Walsh & Duchaine, 2011），而其他 TMS 实验发现 OFA 在面部识别中发挥作用（Solomon-Harris, Mullin & Steeves, 2013）。至关重要的是，所有这些先前的研究都使用了传统的面部识别方法 熟悉。这是一个问题，因为此类研究使用一张照片来代表要学习的面孔。然而，人脸所表现出的个体差异是一张照片无法捕捉到的（Jenkins、White、Van Montfort & Burton，2011）。可以使用使用环境图像、个人在不同照明条件和角度等下的自然照片等的熟悉技术，因为它们不会忽视人体内的变化。此外，与目标面部的实时交互也可以捕捉人体内的变化，但以生态上有效的方式。此外，面部学习尚未完全被理解。众所周知，长时间观察面部会导致更好的面部识别（Downes et al., 1997），然而，最近的研究表明，在面部学习过程中暴露于变化，例如面部识别。从不同的角度呈现面孔，也会带来更好的面孔识别效果（Dwyer、Mundy、Vladeanu 和 Honey，2009）。变化和时间长度的精确权重尚未经过系统测试。因此，需要进一步研究来区分面部学习的不同因素。在我的硕士项目（Sliwinska 等人，准备中）中，参与者亲自与目标身份进行交互，这是一种新的高度生态有效的熟悉技术，可将识别准确度提高 15%。在我的博士学位中，我的目标是完善这种新的、有前途的方法，以使用功能磁共振成像和 TMS 澄清关于 OFA 和 FFA 角色的相互冲突的文献。