Exploring mechanisms underlying face learning in infants and adults

探索婴儿和成人面部学习的机制

• 批准号: RGPIN-2019-05548
• 负责人: Moulson, Margaret
• 金额: $ 2.04万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764095
• 关键词: Exploring; mechanisms; underlying; face; learning

Faces provide a wealth of information about other people, including their identity, gender, age, ethnicity, and emotional state. The ability to perceive this information is critical for social interactions. Adults are considered face "experts" - we remember thousands of individual faces, we use specialized processing mechanisms that apply to faces but not other visual stimuli, and we have neural systems that are preferentially tuned to faces compared to other objects. However, in some ways we may not be face experts. Most face perception studies examine the ability to distinguish between two identities, using tightly controlled stimuli - for example, pictures of faces where hair is removed, faces are presented against a uniform background, and lighting is the same. In these studies, adults do look like experts. However, when studies of face recognition are more similar to tasks that adults must perform everyday (e.g., recognizing your new colleague when you see her at the grocery store), adults do not perform like experts. Specifically, when adults see unfamiliar faces that vary naturally - e.g., in lighting, viewpoint, facial expression, hairstyle, background context - they have difficulty determining that two different images represent the same person. However, we do not have the same problem when we have to recognize familiar faces - we are able to recognize people we know well across this kind of natural variability. The key question motivating the proposed studies is: How do unfamiliar faces become familiar? That is, upon encountering a new face, how do we form a robust and stable representation of that face that allows us to recognize the face across natural variability? The proposed research will combine behavioural, eye-tracking, and electrophysiological techniques to address these questions in adults and infants. Eye-tracking will allow us to determine how infants' and adults' patterns of attention change as they are learning a face, and how those patterns of attention predict how well we are able to recognize novel images of that face. EEG will allow us to examine how neural representations of familiar and unfamiliar faces differ in both adults and infants. This program of research will make important theoretical contributions to our understanding of face recognition and its development. It will also make important practical contributions. There are numerous professions where face recognition - in particular, the ability to recognize faces across variability - is critical (e.g., border officials who have to determine if the picture on an ID card is the same person standing in front of them). Understanding the factors that lead to better face recognition performance under real-world conditions can inform training in these professions. Additionally, findings from the proposed studies may lay the foundation for applied research with developing populations who display face recognition deficits (e.g., autism spectrum disorders).

面孔提供了关于其他人的大量信息，包括他们的身份，性别，年龄，种族和情绪状态。感知这些信息的能力对于社交互动至关重要。成年人被认为是面孔“专家”--我们记住成千上万的面孔，我们使用专门的处理机制来处理面孔，而不是其他视觉刺激，我们的神经系统优先调整面孔而不是其他物体。 然而，在某些方面，我们可能不是面部专家。大多数面部知觉研究都是使用严格控制的刺激来检查区分两种身份的能力-例如，面部照片中的头发被去除，面部在统一的背景下呈现，并且照明是相同的。在这些研究中，成年人确实看起来像专家。然而，当面部识别的研究更类似于成年人每天必须执行的任务时（例如，当你在杂货店看到你的新同事时认出她），成年人的表现并不像专家。具体来说，当成年人看到自然变化的陌生面孔时，在照明、视角、面部表情、发型、背景中-他们难以确定两个不同的图像代表同一个人。然而，当我们必须识别熟悉的面孔时，我们没有同样的问题-我们能够识别我们熟悉的人，跨越这种自然变化。激发这些研究的关键问题是：不熟悉的面孔是如何变得熟悉的？也就是说，在遇到一张新面孔时，我们如何形成一个强大而稳定的面孔表征，使我们能够在自然变化中识别面孔？拟议中的研究将结合联合收割机行为，眼动跟踪和电生理技术，以解决成人和婴儿的这些问题。眼动追踪将使我们能够确定婴儿和成人在学习面部时的注意力模式如何变化，以及这些注意力模式如何预测我们识别该面部新图像的能力。脑电图将使我们能够研究熟悉和不熟悉的面孔在成人和婴儿中的神经表征是如何不同的。这项研究计划将为我们理解人脸识别及其发展做出重要的理论贡献。它还将作出重要的实际贡献。在许多职业中，人脸识别-特别是识别人脸的能力-是至关重要的（例如，边境官员必须确定身份证上的照片是否是站在他们面前的同一个人）。了解在现实世界条件下导致更好的人脸识别性能的因素可以为这些专业的培训提供信息。此外，拟议研究的结果可能为显示人脸识别缺陷的发展中人群（例如，自闭症谱系障碍）。