Recurrence is required to capture the representational dynamics of the human visual system
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Significance: Understanding the computational principles that underlie human vision is a key challenge for neuroscience and could help improve machine vision. Feedforward neural network models process their input through a deep cascade of computations. These models can recognize objects in images and explain aspects of human rapid recognition. However, the human brain contains recurrent connections within and between stages of the cascade, which are missing from the models that dominate both engineering and neuroscience. Here, we measure and model the dynamics of human brain activity during visual perception. We compare feedforward and recurrent neural network models and find that only recurrent models can account for the dynamic transformations of representations among multiple regions of visual cortex.
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Artificial vision models such as deep convolutional neural networks are already both highly successful and often claimed to be good models of human vision.
“artificial vision models such as deep convolutional neural networks (DCNNs), are already both highly successful and often claimed to be good models of human vision (Kietzmann et al. 2019, Lindsay 2021, Mehrer et al. 2021, Zhuang et al. 2021).”
From Consciousness in Artificial Intelligence: Insights from the Science of Consciousness (2023) · cites this paper as Kietzmann et al. 2019
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Harmonizing the Object Recognition Strategies of Deep Neural Networks with Humans
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Self-citationDirect speech reconstruction from sensorimotor brain activity with optimized deep learning models
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Development of visual object recognition
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High-level visual representations in the human brain are aligned with large language models
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The spatiotemporal neural dynamics of object location representations in the human brain
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Self-citationWaves traveling over a map of visual space can ignite short-term predictions of sensory input
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Diverse deep neural networks all predict human IT well, after training and fittingPreprint
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Disentangling five dimensions of animacy in human brain and behaviour
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- Authors
- Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K. A., Cichy, R. M., Hauk, O., & Kriegeskorte, N.
- Affiliations
- Radboud University Nijmegen (NL)
- MRC Cognition and Brain Sciences Unit (GB)
- University of Amsterdam (NL)
- Freie Universität Berlin (DE)
- Columbia University (US)
- Corresponding author
- Kietzmann, Tim C.
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2019-10-07
- Volume
- 116
- Issue
- 43
- Pages
- 21854-21863
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6815174
- OpenAlex
- W2979357328
- Cited by
- 406
- References
- 68
- Citations by year
- 2026: 44 · 2025: 60 · 2024: 66 · 2023: 57 · 2022: 58 · 2021: 66 · 2020: 50 · 2019: 5
- FWCI
- 26.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion, cc-by-nc-nd)
- UvA-DARE (University of Amsterdam) (submittedVersion, cc-by-nc-nd)
- Radboud Repository (Radboud University) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc-nd)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver
- Apollo
- Funders
- European Commission (604102, 261352)
- Deutsche Forschungsgemeinschaft
- Medical Research Council (MC-A060- 5PR20)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- recurrent neural networks, ventral stream, representational dynamics, object recognition, Granger causality, bidirectional information flow, top-down connections
- MeSH terms
- Deep Learning, Adult, Female, Humans, Models, Neurological, Nerve Net, Visual Pathways, Visual Perception, Magnetoencephalography, Feedback, Sensory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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