ReviewInterface focus2018
The functional neuroanatomy of face perception: from brain measurements to deep neural networks.
Review in Interface focus, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- Article
- Hidden Rhythms of a Developing Brain: Multimetric rs-fMRI Insights Into Typical Youth Maturation.Human brain mapping · 2025Article
- Developmental prosopagnosics have normal spatial integration in posterior ventral face-selective regions.bioRxiv : the preprint server for biology · 2025Article
- A pharmacological and brain imaging study of human vasopressin AVP1BR receptor functional polymorphisms.BMC neuroscience · 2025Article
- Article
- Making sense of transformer success.Frontiers in artificial intelligence · 2025Article
- Neuronal Mechanisms Underlying Face Recognition in Non-human Primates.The Japanese psychological research · 2024Article
- A preference to look closer to the eyes is associated with a position-invariant face neural code.Psychonomic bulletin & review · 2024Article
- A critical period for developing face recognition.Patterns (New York, N.Y.) · 2024Article
- Equivalent processing of facial expression and identity by macaque visual system and task-optimized neural network.NeuroImage · 2023Article
- White matter microstructure in face and body networks predicts facial expression and body posture perception across development.Human brain mapping · 2023Article
- White matter connections of high-level visual areas predict cytoarchitecture better than category-selectivity in childhood, but not adulthood.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Face identity coding in the deep neural network and primate brain.Communications biology · 2022Article
- Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning.Neural networks : the official journal of the International Neural Network Society · 2022Article
- Brain functional connectivities that mediate the association between childhood traumatic events, and adult mental health and cognition.EBioMedicine · 2022Article
- Toward Reflective Spiking Neural Networks Exploiting Memristive Devices.Frontiers in computational neuroscience · 2022Review
- Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.Annual review of vision science · 2021Review
- Inferior Occipital Gyrus Is Organized along Common Gradients of Spatial and Face-Part Selectivity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Odor-driven face-like categorization in the human infant brain.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
A central goal in neuroscience is to understand how processing within the ventral visual stream enables rapid and robust perception and recognition. Recent neuroscientific discoveries have significantly advanced understanding of the function, structure and computations along the ventral visual stream that serve as the infrastructure supporting this behaviour. In parallel, significant advances in computational models, such as hierarchical deep neural networks (DNNs), have brought machine performance to a level that is commensurate with human performance. Here, we propose a new framework using the ventral face network as a model system to illustrate how increasing the neural accuracy of present DNNs may allow researchers to test the computational benefits of the functional architecture of the human brain. Thus, the review (i) considers specific neural implementational features of the ventral face network, (ii) describes similarities and differences between the functional architecture of the brain and DNNs, and (iii) provides a hypothesis for the computational value of implementational features within the brain that may improve DNN performance. Importantly, this new framework promotes the incorporation of neuroscientific findings into DNNs in order to test the computational benefits of fundamental organizational features of the visual system.
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Registered trials
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.