ArticleCognition2021
Computational insights into human perceptual expertise for familiar and unfamiliar face recognition.
Article in Cognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
30 citing papers in PubMed.
- The state of modelling face processing in humans with deep learning.British journal of psychology (London, England : 1953) · 2026Review
- A dual limb attention based deep learning network for multipath classification of millimetre wave signals in intelligent transportation system.Scientific reports · 2026Article
- Frequency-tagged EEG reveals category-selective responses to face race.Social cognitive and affective neuroscience · 2026Article
- Characterization of the spatiotemporal representations of visual, semantic, and memorability features in the human brain.PLoS biology · 2026Article
- Experience Alone Can Generate Human Face Specialization: Evidence From Deep Learning Models.Open mind : discoveries in cognitive science · 2026Article
- Concurrent emergence of view invariance, sensitivity to critical features, and identity face classification through visual experience: Insights from deep learning algorithms.Journal of vision · 2025Article
- Human-like face pareidolia emerges in deep neural networks optimized for face and object recognition.PLoS computational biology · 2025Article
- Human-like dissociations between confidence and accuracy in convolutional neural networks.PLoS computational biology · 2024Article
- Contrastive learning explains the emergence and function of visual category-selective regions.Science advances · 2024Article
- Decoding face recognition abilities in the human brain.PNAS nexus · 2024Article
- Holistic processing and face expertise after pediatric resection of occipitotemporal cortex.Neuropsychologia · 2024Article
- Human-like dissociations between confidence and accuracy in convolutional neural networks.bioRxiv : the preprint server for biology · 2024Article
- Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.Cell reports · 2024Article
- A neural marker of the human face identity familiarity effect.Scientific reports · 2023Article
- Behavioral signatures of face perception emerge in deep neural networks optimized for face recognition.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Diverse types of expertise in facial recognition.Scientific reports · 2023Article
- Twin Identification over Viewpoint Change: A Deep Convolutional Neural Network Surpasses Humans.ACM transactions on applied perception · 2023Article
- Unfamiliar face matching ability predicts the slope of face learning.Scientific reports · 2023Article
- Individual differences in classification images of Mooney faces.Journal of vision · 2022Article
- Face learning via brief real-world social interactions induces changes in face-selective brain areas and hippocampus.Perception · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Humans are generally thought to be experts at face recognition, and yet identity perception for unfamiliar faces is surprisingly poor compared to that for familiar faces. Prior theoretical work has argued that unfamiliar face identity perception suffers because the majority of identity-invariant visual variability is idiosyncratic to each identity, and thus, each face identity must be learned essentially from scratch. Using a high-performing deep convolutional neural network, we evaluate this claim by examining the effects of visual experience in untrained, object-expert and face-expert networks. We found that only face training led to substantial generalization in an identity verification task of novel unfamiliar identities. Moreover, generalization increased with the number of previously learned identities, highlighting the generality of identity-invariant information in face images. To better understand how familiarity builds upon generic face representations, we simulated familiarization with face identities by fine-tuning the network on images of the previously unfamiliar identities. Familiarization produced a sharp boost in verification, but only approached ceiling performance in the networks that were highly trained on faces. Moreover, in these face-expert networks, the sharp familiarity benefit was seen only at the identity-based output probability layer, and did not depend on changes to perceptual representations; rather, familiarity effects required learning only at the level of identity readout from a fixed expert representation. Our results thus reconcile the existence of a large familiar face advantage with claims that both familiar and unfamiliar face identity processing depend on shared expert perceptual representations.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.