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  1. Individual differences in eyewitness accuracy across multiple lineups of faces.Andrew Russ, Melanie Sauerland, Charlotte E. Lee & Markus Bindemann - 2018 - Cognitive Research: Principles and Implications 3 (30).
    Theories of face recognition in cognitive psychology stipulate that the hallmark of accurate identification is the ability to recognize a person consistently, across different encounters. In this study, we apply this reasoning to eyewitness identification by assessing the recognition of the same target person repeatedly, over six successive lineups. Such repeat identifications are challenging and can be performed only by a proportion of individuals, both when a target exhibits limited and more substantial variability in appearance across lineups (Experiments 1 and (...)
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  • Dunning–Kruger effects in face perception.Xingchen Zhou & Rob Jenkins - 2020 - Cognition 203 (C):104345.
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  • Personal familiarity of faces, animals, objects, and scenes: Distinct perceptual and overlapping conceptual representations.Holger Wiese, Maya Schipper, Tsvetomila Popova, A. Mike Burton & Andrew W. Young - 2023 - Cognition 241 (C):105625.
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  • Partitioning natural face image variability emphasises within-identity over between-identity representation for understanding accurate recognition.David White, Tanya Wayne & Victor P. L. Varela - 2022 - Cognition 219 (C):104966.
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  • A PCA-Based Active Appearance Model for Characterising Modes of Spatiotemporal Variation in Dynamic Facial Behaviours.David M. Watson & Alan Johnston - 2022 - Frontiers in Psychology 13.
    Faces carry key personal information about individuals, including cues to their identity, social traits, and emotional state. Much research to date has employed static images of faces taken under tightly controlled conditions yet faces in the real world are dynamic and experienced under ambient conditions. A common approach to studying key dimensions of facial variation is the use of facial caricatures. However, such techniques have again typically relied on static images, and the few examples of dynamic caricatures have relied on (...)
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  • 360 Degrees of Facial Perception: Congruence in Perception of Frontal Portrait, Profile, and Rotation Photographs.Vít Třebický, Jitka Fialová, David Stella, Zuzana Štěrbová, Karel Kleisner & Jan Havlíček - 2018 - Frontiers in Psychology 9.
  • Social inferences from faces: Ambient images generate a three-dimensional model.Clare Am Sutherland, Julian A. Oldmeadow, Isabel M. Santos, John Towler, D. Michael Burt & Andrew W. Young - 2013 - Cognition 127 (1):105-118.
  • Personality judgments from everyday images of faces.Clare A. M. Sutherland, Lauren E. Rowley, Unity T. Amoaku, Ella Daguzan, Kate A. Kidd-Rossiter, Ugne Maceviciute & Andrew W. Young - 2015 - Frontiers in Psychology 6.
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  • Integrating social and facial models of person perception: Converging and diverging dimensions.Clare A. M. Sutherland, Julian A. Oldmeadow & Andrew W. Young - 2016 - Cognition 157 (C):257-267.
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  • Independent contribution of perceptual experience and social cognition to face recognition.Linoy Schwartz & Galit Yovel - 2019 - Cognition 183 (C):131-138.
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  • These two are different. Yes, they’re the same: Choice blindness for facial identity.Melanie Sauerland, Anna Sagana, Kathrin Siegmann, Danitsja Heiligers, Harald Merckelbach & Rob Jenkins - 2016 - Consciousness and Cognition 40:93-104.
  • Tolerance for distorted faces: Challenges to a configural processing account of familiar face recognition.Adam Sandford & A. Mike Burton - 2014 - Cognition 132 (3):262-268.
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  • What makes a face photo a ‘good likeness’?Kay L. Ritchie, Robin S. S. Kramer & A. Mike Burton - 2018 - Cognition 170 (C):1-8.
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  • Multiple-image arrays in face matching tasks with and without memory.Kay L. Ritchie, Robin S. S. Kramer, Mila Mileva, Adam Sandford & A. Mike Burton - 2021 - Cognition 211 (C):104632.
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  • Seeing through disguise: Getting to know you with a deep convolutional neural network.Eilidh Noyes, Connor J. Parde, Y. Ivette Colón, Matthew Q. Hill, Carlos D. Castillo, Rob Jenkins & Alice J. O'Toole - 2021 - Cognition 211 (C):104611.
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  • Camera-to-subject distance affects face configuration and perceived identity.Eilidh Noyes & Rob Jenkins - 2017 - Cognition 165 (C):97-104.
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  • Understanding facial impressions between and within identities.Mila Mileva, Andrew W. Young, Robin S. S. Kramer & A. Mike Burton - 2019 - Cognition 190 (C):184-198.
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  • Understanding face familiarity.Robin S. S. Kramer, Andrew W. Young & A. Mike Burton - 2018 - Cognition 172 (C):46-58.
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  • Human–Computer Interaction in Face Matching.Matthew C. Fysh & Markus Bindemann - 2018 - Cognitive Science 42 (5):1714-1732.
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  • Digital Detectives: Websleuthing Reduces Eyewitness Identification Accuracy in Police Lineups.Camilla Elphick, Richard Philpot, Min Zhang, Avelie Stuart, Graham Pike, Ailsa Strathie, Catriona Havard, Zoe Walkington, Lara A. Frumkin, Mark Levine, Blaine A. Price, Arosha K. Bandara & Bashar Nuseibeh - 2021 - Frontiers in Psychology 12.
    Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15–20 min delay either viewed a mock social media site including the culprit, viewed a mock social media site including a lookalike, or completed a filler task. A week later, participants (...)
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  • Ensemble coding of facial identity is robust, but may not contribute to face learning.Emily E. Davis, Claire M. Matthews & Catherine J. Mondloch - 2024 - Cognition 243 (C):105668.
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  • Identity From Variation: Representations of Faces Derived From Multiple Instances.A. Mike Burton, Robin S. S. Kramer, Kay L. Ritchie & Rob Jenkins - 2016 - Cognitive Science 40 (1):202-223.
    Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos (...)
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  • Stable individual differences in unfamiliar face identification: Evidence from simultaneous and sequential matching tasks.K. A. Baker, V. J. Stabile & C. J. Mondloch - 2023 - Cognition 232 (C):105333.
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  • How does a newly encountered face become familiar? The effect of within-person variability on adults’ and children’s perception of identity.Kristen A. Baker, Sarah Laurence & Catherine J. Mondloch - 2017 - Cognition 161:19-30.
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  • Critical features for face recognition.Naphtali Abudarham, Lior Shkiller & Galit Yovel - 2019 - Cognition 182 (C):73-83.
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  • Face Recognition Depends on Specialized Mechanisms Tuned to View‐Invariant Facial Features: Insights from Deep Neural Networks Optimized for Face or Object Recognition.Naphtali Abudarham, Idan Grosbard & Galit Yovel - 2021 - Cognitive Science 45 (9):e13031.
    Face recognition is a computationally challenging classification task. Deep convolutional neural networks (DCNNs) are brain‐inspired algorithms that have recently reached human‐level performance in face and object recognition. However, it is not clear to what extent DCNNs generate a human‐like representation of face identity. We have recently revealed a subset of facial features that are used by humans for face recognition. This enables us now to ask whether DCNNs rely on the same facial information and whether this human‐like representation depends on (...)
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  • Seeking safety in knowledge.Jennifer Nagel - 2023 - Proceedings and Addresses of the American Philosophical Association 97:186-214.
    Knowledge demands more than accuracy: epistemologists are broadly agreed that those who know are non-accidentally right, satisfying some kind of safety condition. However, it is hard to formulate any adequate account of safety, and harder still to explain exactly why we care about it. This paper approaches the problem by looking at a concrete human cognitive capacity, face recognition, to see where epistemic safety shows up in it. Drawing on new models in artificial intelligence, and making a case that human (...)
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