Modelling face memory reveals task-generalizable representations

Zhan, J., Garrod, O. G.B., van Rijsbergen, N. and Schyns, P. G. (2019) Modelling face memory reveals task-generalizable representations. Nature Human Behaviour, 3, pp. 817-826. (doi: 10.1038/s41562-019-0625-3) (PMID:31209368)

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Current cognitive theories are cast in terms of information-processing mechanisms that use mental representations. For example, people use their mental representations to identify familiar faces under various conditions of pose, illumination and ageing, or to draw resemblance between family members. Yet, the actual information contents of these representations are rarely characterized, which hinders knowledge of the mechanisms that use them. Here, we modelled the three-dimensional representational contents of 4 faces that were familiar to 14 participants as work colleagues. The representational contents were created by reverse-correlating identity information generated on each trial with judgements of the face’s similarity to the individual participant’s memory of this face. In a second study, testing new participants, we demonstrated the validity of the modelled contents using everyday face tasks that generalize identity judgements to new viewpoints, age and sex. Our work highlights that such models of mental representations are critical to understanding generalization behaviour and its underlying information-processing mechanisms.

Item Type:Articles
Additional Information:Also funded by the Multidisciplinary University Research Initiative/Engineering and Physical Sciences Research Council (USA, UK; 172046-01).
Glasgow Author(s) Enlighten ID:Garrod, Dr Oliver and Schyns, Professor Philippe and Zhan, Dr Jiayu and Van Rijsbergen, Dr Nicola
Authors: Zhan, J., Garrod, O. G.B., van Rijsbergen, N., and Schyns, P. G.
College/School:College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
Journal Name:Nature Human Behaviour
Publisher:Nature Publishing Group
ISSN (Online):2397-3374
Published Online:17 June 2019
Copyright Holders:Copyright © The Authors, under exclusive licence to Springer Nature Limited 2019
First Published:First published in Nature Human Behaviour 3:817-826
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
698281Brain Algorithmics: Reverse Engineering Dynamic Information Processing Networks from MEG time seriesPhilippe SchynsWellcome Trust (WELLCOTR)107802/Z/15/ZINP - CENTRE FOR COGNITIVE NEUROIMAGING