How the learning of unfamiliar faces is affected by their similarity to already known faces.

背景虽然熟悉的和不熟悉的人脸加工有一些共同的特征，如“结构”加工[例如1]，但也有证据表明它们依赖于不同类型的信息[例如2]。与陌生面孔识别相比，熟悉面孔识别的优异性能的可靠观察表明，熟悉与可靠的加工优势相关。一些作者提出，陌生的人脸匹配应该被概念化为图像匹配，而不是涉及任何特定于人脸的过程[例如3]。然而，对于陌生面孔是如何变得熟悉的，我们几乎一无所知。目前的理论模型[例如，4]表明，面部编码不是孤立的，而是相对于现有的面部表征进行编码，通常局限于“普通”或“正常”面部。然而，我们经常感到惊讶的是，一个新遇到的人与我们已经认识的人是如此相似。我做出了一个新的预测，即对陌生面孔的编码是与先前存在的面孔表征有关的。设计这个项目旨在解决关于陌生面孔的学习如何受到他们与已知面孔的相似程度的影响的主要知识缺陷。我将使用包括变形在内的各种技术来处理陌生面孔与已知面孔的相似性。外显学习将通过对之前是否见过一张脸的反应来衡量。关键的是，我还将使用适应性设计、参与者对各种特征(即可信度)、眼球跟踪(包括瞳孔扩大)和事件相关电位(ERPs)的评分来衡量内隐学习。使用这些方法，我将能够探索熟悉的、相似的和不熟悉的面孔知觉之间的加工差异。由于熟悉的面孔加工可能会与概念信息[例如5]和天花板效应混淆，我将使用训练范式来处理熟悉程度。分析相关分析和回归分析将用于探索个体差异，而方差分析将用于群体差异(即一些参与者熟悉的刺激和其他人不熟悉的刺激)关于熟悉程度的外显和内隐指标。眼球跟踪和企业资源规划数据将需要在提交答复进行统计测试之前进行预处理。结论我将探索当陌生面孔变得熟悉时会发生什么。这项工作在理论上是有价值的。“互动激活和竞争”[例如6]模型假定身份和身份相关信息是分开处理的。虽然不熟悉的面孔依赖于视觉表征，但理论上，熟悉的面孔也可以通过概念信息进行处理。推测，如果对陌生面孔的编码至少部分地与预先存在的面孔表征有关，这可能会对预测编码如何工作产生有趣的影响[例如7]。这项工作可能能够检验现有的特别强大的理论，这些理论认为陌生的面孔不是面孔[3]，并开始建立一个令人满意的面孔学习解释。从长远来看，这项工作可能会确定面部熟悉度的前置和后置指标的集合。这可能会对目击证人的设置产生重要影响

Background While familiar and unfamiliar face processing share some characteristics, such as 'configural' processing [e.g. 1], there is also evidence that they rely on qualitatively different types of information [e.g. 2]. The robust observation of superior performance for familiar, compared to unfamiliar face recognition, suggests that familiarity is associated with a reliable processing benefit. Some authors propose that unfamiliar face matching should be conceptualised as image-matching, rather than involving any face-specific processes [e.g. 3]. Yet, virtually nothing is known about how unfamiliar faces become familiar. Current theoretical models [e.g. 4] suggest that faces are not encoded in isolation, but with respect to existing facial representations, often confined to an 'average' or 'norm' face. However, we are often struck by how similar a newly-encountered person is to someone whom we already know. I make the novel prediction that the encoding of unfamiliar faces is performed in relation to pre-existing face representations.DesignThis project aims to address major deficits in knowledge regarding how the learning of unfamiliar faces is affected by their degree of resemblance to already-known faces. I will manipulate unfamiliar faces' similarity to already known faces using various techniques, including morphing. Explicit learning will be measured by responses as to whether or not a face has been seen before. Critically, I will also measure implicit learning using adaptation designs, participant ratings for various characteristics (i.e. trustworthiness), eye-tracking (including pupil dilation), and event-related potentials (ERPs). Using these methods, I will be able to explore processing differences between familiar, similar-to-familiar, and unfamiliar face perception. As familiar face processing is likely to be confounded with conceptual information [e.g. 5] and ceiling effects, I will using training paradigms to manipulate familiarity.AnalysesCorrelational and regression analyses will be used to explore individual differences, and ANOVAs for group differences (i.e. stimuli that are familiar to some participants and stimuli that are unfamiliar to others) on explicit and implicit indexes of familiarity. Eye-tracking and ERP data will require pre-processing before responses are submitted to statistical testing. ConclusionsI will explore what happens as an unfamiliar face becomes familiar. This work is theoretically informative. The 'Interactive Activation and Competition' [e.g. 6] model posits that identity and identity-related information are processed separately. While unfamiliar faces rely on visual representations, theoretically, familiar faces could also be processed through conceptual information. Speculatively, if the encoding of unfamiliar faces is performed at least partly in relation to pre-existing face representations, this could have interesting implications for how predictive coding [e.g. 7] might work. This work may be able to test particularly strong existing theories that suggest unfamiliar faces are not faces [3], and begin to develop a satisfactory account of face learning.ImplicationsA better understanding of the perceptual, cognitive, and neural mechanisms involved in unfamiliar and familiar face learning will inform existing theories. In the longer term, this work may identify a collection of pre- and post-dictors of facial familiarity. This could have important implications in eye-witness settings