Face Adaptation and Face Priming as Tools for Getting Insights Into the Quality of Face Space.

Ronja Mueller,Claus-Christian Carbon,Sandra Utz,Tilo Strobach

doi:10.3389/fpsyg.2020.00166

Ronja Mueller, Claus-Christian Carbon + Show 2 more

Open Access

https://doi.org/10.3389/fpsyg.2020.00166

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Abstract

During the recognition of faces, the incoming perceptual information is matched against mental representations of familiar faces stored in memory. Face space models describe an abstract concept of face representations and their mental organization, in which facial representations are located on various characteristic dimensions, depending on their specific facial characteristics. However, these models are defined just as incompletely as the general understanding of face recognition. We took two phenomena from face processing to better understand face recognition, and so the nature of face space: face adaptation and face priming. The face literature has mainly focused on face adaptation, largely neglecting face priming when trying to integrate outcomes regarding face recognition into the face space framework. Consequently, the present paper aims to review both phenomena and their contributions to face recognition, representation, and face space.

Highlights

It is a common assumption that familiar faces are encoded and recognized by matching the incoming perceptual information against facial representations stored in memory (Bruce and Young, 1986)
an automatized mechanism to avoid a confusion with other faces presented
priming effect could also be that of an adaptation process

Summary

Introduction

It is a common assumption that familiar faces are encoded and recognized by matching the incoming perceptual information against facial representations stored in memory (Bruce and Young, 1986). To discriminate faces from each other, these stored representations must contain a large variety of characteristics, continuously differing from each other along many dimensions (Lee et al, 2000). Valentine proposes a multidimensional space, in which each facial representation is located, depending on its characteristic value on each dimension. According to this model, face representations that are located close to each other are similar, whereas representations that are further apart, share fewer similarities. Face representations that are located close to each other are similar, whereas representations that are further apart, share fewer similarities The facial information these dimensions contain and on which the representations vary is not further specified. Through computational modeling, Lewis (2004) was able to narrow down the number of dimensions to between 15 and 22 (other authors, estimate the number of dimensions to be much higher; see, Meytlis and Sirovich, 2007)

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