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

陌生面孔的学习如何受到其与已知面孔的相似性的影响。

• 批准号: 2107715
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2107715
• 关键词: How; learning; unfamiliar; faces; affected

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

背景虽然熟悉和不熟悉的面孔处理有一些共同的特征，如“记忆”处理[例1]，但也有证据表明它们依赖于不同类型的信息[例2]。与不熟悉的面孔识别相比，熟悉面孔识别的上级性能的稳健观察表明，熟悉与可靠的处理益处相关。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，几乎没有人知道陌生的面孔是如何变得熟悉的。目前的理论模型[例如4]表明，面孔不是孤立地编码的，而是相对于现有的面部表征，通常局限于“平均”或“标准”的面孔。然而，我们经常会惊讶于一个新遇到的人与我们已经认识的人是多么相似。我做了新的预测，不熟悉的面孔的编码进行有关预先存在的脸representations.DesignThis项目旨在解决知识的主要缺陷，关于如何学习的不熟悉的面孔是由他们的相似程度影响到已知的面孔。我将使用各种技术，包括变形，来操纵不熟悉的面孔与已知面孔的相似性。外显学习将通过一张脸之前是否见过的反应来衡量。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）的评级、眼动跟踪（包括瞳孔扩张）和事件相关电位（ERP）来测量内隐学习。使用这些方法，我将能够探索熟悉，相似到熟悉和不熟悉的面孔感知之间的处理差异。由于熟悉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操纵熟悉度。分析相关性和回归分析将用于探索个体差异，以及对熟悉度的外显和内隐指标的组差异（即一些参与者熟悉的刺激和其他人不熟悉的刺激）的方差分析。眼动追踪和ERP数据在提交给统计测试之前需要进行预处理。结论我将探讨当一个陌生的面孔变得熟悉时会发生什么。这项工作是理论上的信息。“互动激活和竞争”[例如6]模式假定身份和与身份有关的信息是分开处理的。虽然陌生面孔依赖于视觉表征，但理论上，熟悉面孔也可以通过概念信息进行处理。据推测，如果不熟悉的面孔的编码至少部分地与预先存在的面孔表示有关，这可能对预测编码[例如7]可能如何工作产生有趣的影响。这项工作可能能够测试特别强的现有的理论，表明陌生的面孔不是面孔[3]，并开始发展一个令人满意的帐户的脸learning.ImplicationsA更好地了解知觉，认知和神经机制参与陌生和熟悉的面孔学习将告知现有的理论。从长远来看，这项工作可能会识别出一系列面部熟悉度的前和后指示符。这可能对目击者有重要影响