10 found
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  1.  22
    Critical features for face recognition.Naphtali Abudarham, Lior Shkiller & Galit Yovel - 2019 - Cognition 182 (C):73-83.
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  2.  78
    A unified coding strategy for processing faces and voices.Galit Yovel & Pascal Belin - 2013 - Trends in Cognitive Sciences 17 (6):263-271.
  3.  6
    From concepts to percepts in human and machine face recognition: A reply to Blauch, Behrmann & Plaut.Galit Yovel & Naphtali Abudarham - 2021 - Cognition 208 (C):104424.
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  4.  57
    A face inversion effect without a face.Talia Brandman & Galit Yovel - 2012 - Cognition 125 (3):365-372.
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  5.  22
    What can individual differences reveal about face processing?Galit Yovel, Jeremy B. Wilmer & Brad Duchaine - 2014 - Frontiers in Human Neuroscience 8.
  6.  6
    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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  7.  90
    Face perception is category-specific: Evidence from normal body perception in acquired prosopagnosia.Tirta Susilo, Galit Yovel, Jason Js Barton & Bradley Duchaine - 2013 - Cognition 129 (1):88-94.
  8.  19
    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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  9.  13
    Dissociating gait from static appearance: A virtual reality study of the role of dynamic identity signatures in person recognition.Noa Simhi & Galit Yovel - 2020 - Cognition 205 (C):104445.
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  10.  4
    Why psychologists should embrace rather than abandon DNNs.Galit Yovel & Naphtali Abudarham - 2023 - Behavioral and Brain Sciences 46:e414.
    Deep neural networks (DNNs) are powerful computational models, which generate complex, high-level representations that were missing in previous models of human cognition. By studying these high-level representations, psychologists can now gain new insights into the nature and origin of human high-level vision, which was not possible with traditional handcrafted models. Abandoning DNNs would be a huge oversight for psychological sciences.
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