Putting the individual into face recognition: Bridging theory and application

将个人纳入人脸识别：桥接理论与应用

• 批准号: ES/X002063/1
• 负责人: Holger Wiese
• 金额: $ 58.42万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FX002063%2F1
• 关键词: Putting; individual; into; face; recognition

This project will examine the neural basis of a fundamental social skill - the ability to recognise the people we know from their faces. We recognise our relatives, friends, and colleagues dozens of times every day, but surprisingly little is known about how we achieve this. We propose that one reason for this is that previous research has not acknowledged the idiosyncrasy of familiar face recognition. We all have our own unique set of familiar faces, and these tend to overlap only partly with others. Here, we address the problem of familiar face recognition using methods that allow incorporation of individual, idiosyncratic familiarity. Taking idiosyncratic familiarity into account will result in substantially more reliable measures of face recognition and will allow us to develop an innovative theoretical and methodological focus. Previous work has largely established where in the brain and when in time a familiar face is recognised. Here, we will, for the first time, systematically examine how the visual appearance of known faces is stored in the brain. In addition, we will examine a question of practical importance - the reliable detection of familiarity with a face, even when participants are motivated to conceal such knowledge.In a first strand, we will tackle the problem that the same face can look very different in different conditions (e.g. due to changes in lighting, viewing angle, or make-up). How then is it possible for the brain to recognise it as the same familiar face? We have developed two potentially complementary theoretical views to explain this phenomenon. The first suggests that we store the "gist" of a face in our memory, i.e. the information that is commonly observed in all circumstances. The second suggests that, in addition to this abstract representation, we may have many specific memories of a face - similar to "snapshots" taken on different occasions - and thus information about what a face looked like in a particular encounter. Our project will provide decisive evidence for the 'gist' and 'snapshot' views. In a second strand, we will examine how neural representations of different faces are organised to allow for efficient recognition. We know literally thousands of faces. How then is it possible not to constantly mix them up? Maybe because the different faces we know are organised in a way that allows only the "best match" to become activated while other, potentially similar looking faces are inhibited. This idea is based on computer models of face recognition, but has rarely been tested with human viewers and its neural basis is completely unknown. We have now developed a series of novel experimental studies to fill this gap.Finally, while previous cognitive neuroscience research has mostly been constrained to examine face processing in groups, i.e. by collapsing data across a number of participants, it is crucial for any potential real-life application that familiarity can be detected in individual participants. In applied situations, it is not useful to know that a group of 10-20 participants on average shows a certain brain response, as evidence is typically required about an individual witness or suspect. This project will therefore develop a novel neural measure to detect familiarity in individuals. Moreover, to be of practical relevance, such a measure needs to work reliably even when participants are trying to conceal familiarity - for example to avoid implicating conspirators in criminal investigations. We will therefore test for robustness against attempted deceit.Overall, by taking idiosyncratic familiarity into account as well as by shifting the research focus from the "where/when" to "how", and from the group to the individual level, this project will generate innovative findings on how the human brain recognises familiar faces - a question of high theoretical importance. In addition, our results will contribute to solving a problem of substantial practical relevance.

这个项目将研究一项基本社交技能的神经基础——从面孔上识别我们认识的人的能力。我们每天都要认出亲戚、朋友和同事几十次，但令人惊讶的是，我们对如何做到这一点知之甚少。我们认为，其中一个原因是以前的研究没有认识到熟悉的面孔识别的特质。我们都有自己独特的一套熟悉的面孔，这些面孔往往只与他人部分重叠。在这里，我们使用允许结合个人，特质熟悉度的方法来解决熟悉人脸识别的问题。考虑到特殊熟悉度将导致更可靠的面部识别措施，并将使我们能够发展创新的理论和方法重点。先前的工作已经在很大程度上确定了大脑的哪个部位以及何时会识别熟悉的面孔。在这里，我们将首次系统地研究已知面孔的视觉外观是如何在大脑中存储的。此外，我们将研究一个具有实际重要性的问题——即使参与者有意隐瞒这些知识，对面孔熟悉程度的可靠检测。在第一链中，我们将解决相同的脸在不同条件下看起来非常不同的问题（例如，由于照明，视角或化妆的变化）。那么，大脑怎么可能将它识别为同一张熟悉的脸呢？我们发展了两种可能互补的理论观点来解释这一现象。第一种观点认为，我们会将一张脸的“要点”储存在记忆中，即在所有情况下都能看到的信息。第二种观点认为，除了这种抽象的表象之外，我们可能对一张脸有许多具体的记忆——类似于在不同场合拍摄的“快照”——因此也就有了在特定场合下一张脸是什么样子的信息。我们的项目将为“要点”和“快照”视图提供决定性的证据。在第二部分中，我们将研究如何组织不同面孔的神经表征以允许有效识别。我们确实认识成千上万张脸。那么，怎么可能不经常把它们混在一起呢？也许是因为我们所知道的不同面孔的组织方式只允许“最佳匹配”被激活，而其他潜在相似的面孔则被抑制。这个想法是基于人脸识别的计算机模型，但很少在人类观众身上进行测试，其神经基础也完全未知。我们现在已经开发了一系列新颖的实验研究来填补这一空白。最后，虽然之前的认知神经科学研究大多局限于研究群体中的面部处理，即通过在许多参与者中折叠数据，但对于任何潜在的现实应用来说，能够在个体参与者中检测到熟悉度是至关重要的。在实际情况下，知道一组平均10-20名参与者表现出某种特定的大脑反应是没有用的，因为通常需要关于单个证人或嫌疑人的证据。因此，该项目将开发一种新的神经测量方法来检测个体的熟悉度。此外，为了具有实际意义，这种措施需要可靠地发挥作用，即使参与者试图隐瞒熟悉程度——例如，避免在刑事调查中牵连同谋者。因此，我们将测试其对欺诈企图的稳健性。总的来说，通过将特殊熟悉度考虑在内，并将研究重点从“何地/何时”转移到“如何”，从群体层面转移到个人层面，该项目将产生关于人类大脑如何识别熟悉面孔的创新发现——这是一个具有高度理论重要性的问题。此外，我们的结果将有助于解决一个具有重大实际意义的问题。