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

虽然熟悉和不熟悉的人脸处理有一些共同的特征，比如“构形”处理[例1]，但也有证据表明它们依赖于定性不同的信息类型[例2]。与不熟悉的人脸识别相比，熟悉的人脸识别表现优异，这一强有力的观察表明，熟悉与可靠的处理效益有关。一些作者提出，不熟悉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，对于陌生的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4]表明，人脸不是孤立地编码的，而是相对于现有的面部表征，通常局限于“平均”或“规范”的面部。然而，我们常常会惊讶于一个新认识的人和我们已经认识的人是多么的相似。我做了一个新颖的预测，即不熟悉的面孔的编码是在与预先存在的面孔表征相关的情况下进行的。该项目旨在解决关于陌生面孔的学习如何受到其与已知面孔相似程度的影响的主要知识缺陷。我将使用各种技术来操纵陌生面孔与已知面孔的相似性，包括变形。外显学习将通过对之前是否见过一张脸的反应来衡量。至关重要的是，我还将使用适应设计、参与者对各种特征（即可信度）的评分、眼动追踪（包括瞳孔扩张）和事件相关电位（erp）来衡量内隐学习。使用这些方法，我将能够探索熟悉，相似熟悉和不熟悉的面孔感知之间的加工差异。由于熟悉面孔处理很可能与概念信息[例5]和天花板效应相混淆，我将使用训练范式来操纵熟悉度。将使用关系分析和回归分析来探索个体差异，并使用方差分析来研究群体差异（即一些参与者熟悉的刺激和另一些参与者不熟悉的刺激）在显性和隐性熟悉度指标上的差异。眼球追踪和ERP数据在提交统计测试之前需要进行预处理。我将探索当一张陌生的脸变得熟悉时会发生什么。这项工作在理论上是有益的。“互动激活和竞争”[例6]模型假设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来处理。推测地说，如果对不熟悉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的工作方式产生有趣的影响[例7]。这项工作可能能够测试特别强大的现有理论，即不熟悉的面孔不是面孔，并开始发展令人满意的面部学习。更好地理解不熟悉和熟悉面孔学习的知觉、认知和神经机制将为现有理论提供信息。从长远来看，这项工作可能会确定一系列面部熟悉度的前后指标。这可能对目击环境有重要影响

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