The DOI matches its Crossref record.
Speed of processing in the human visual system
Abstract
How long does it take for the human visual system to process a complex natural image? Subjectively, recognition of familiar objects and scenes appears to be virtually instantaneous, but measuring this processing time experimentally has proved difficult. Behavioural measures such as reaction times can be used, but these include not only visual processing but also the time required for response execution. However, event-related potentials (ERPs) can sometimes reveal signs of neural processing well before the motor output. Here we use a go/no-go categorization task in which subjects have to decide whether a previously unseen photograph, flashed on for just 20 ms, contains an animal. ERP analysis revealed a frontal negativity specific to no-go trials that develops roughly 150 ms after stimulus onset. We conclude that the visual processing needed to perform this highly demanding task can be achieved in under 150 ms.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Thus, a natural paradigm for comparing the performance of human observers in an object-recognition task to that of a feedforward model of visual processing is ultra rapid categorization, a task for which back projections are likely to be inactive (30, 31).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [30]
In the citing paper
“The accuracy of the human observers is well within the range of data previously obtained with go/no-go tasks on similar tasks (30, 31, 33).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [30]
In the citing paper
“A well established experiment is an animal- vs. non-animal-recognition task (30-34).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [30]
Citations
Cited by
Sorted by citations
Items 1,001–1,010 of 1,010
Training improves visual processing speed and generalizes to untrained functions
Lev, M., Ludwig, K., Gilaie-Dotan, S., Voss, S., Sterzer, P., Hesselmann, G., & Polat, U. (2014) · Scientific Reports · doi:10.1038/srep07251 · Cited by 40
Increased Attention Regulation from Emotion Regulation Therapy for Generalized Anxiety Disorder
Renna, M. E., Seeley, S. H., Heimberg, R. G., Etkin, A., Fresco, D. M., & Mennin, D. S. (2018) · Cognitive Therapy and Research · doi:10.1007/s10608-017-9872-7 · Cited by 36
Reinforcement learning of linking and tracing contours in recurrent neural networks
Brosch, T., Neumann, H., & Roelfsema, P. R. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004489 · Cited by 33
Rapid image analysis using neural signals
Mathan, S., Erdogmus, D., Huang, Y., Pavel, M., Ververs, P., Carciofini, J., Dorneich, M., & Whitlow, S. (2008) · CHI '08 Extended Abstracts on Human Factors in Computing Systems · doi:10.1145/1358628.1358849 · Cited by 28
Cortico-spinal modularity in the parieto-frontal system: A new perspective on action control
Bufacchi, R. J., Battaglia-Mayer, A., Iannetti, G. D., & Caminiti, R. (2023) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2023.102537 · Cited by 22
Predictive coding with spiking neurons and feedforward gist signaling
Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., & Pennartz, C. M. A. (2024) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2024.1338280 · Cited by 14
Learning beyond sensations: How dreams organize neuronal representations
Deperrois, N., Petrovici, M. A., Senn, W., & Jordan, J. (2024) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105508 · Cited by 9
Crowdsourcing Thumbnail Captions: Data Collection and Validation
Aguirre, C., Cao, S., Mahmood, A., & Huang, C.-M. (2023) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3589346 · Cited by 6
Evaluating the Feasibility of Artificial Intelligence in Generating Visual Abstracts: A Pilot Study.
Akhund, R., Mainali, B., Izhar, A., Muñoz Wilson, D. D., Gillis, A., Fazendin, J. M., Chen, H., & Lindeman, B. (2026) · Journal of Surgical Research · doi:10.1016/j.jss.2026.05.087
2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Self-citation
Metadata
- Authors
- Thorpe, S., Fize, D., & Marlot, C.
- Affiliations
- Centre de recherche cerveau et cognition (FR)
- ORCID
- Thorpe, Simon
- Venue
- Nature
- Published
- 1996-06
- Volume
- 381
- Issue
- 6582
- Pages
- 520-522
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2091845343
- Cited by
- 3,538
- References
- 0
- Citations by year
- 2026: 97 · 2025: 121 · 2024: 127 · 2023: 116 · 2022: 117 · 2021: 149 · 2020: 147 · 2019: 151 · 2018: 172 · 2017: 165 · 2016: 143 · 2015: 155 · 2014: 169 · 2013: 157 · 2012: 174
- FWCI
- 20.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Keywords
- visual processing speed, event-related potentials, go/no-go task, frontal negativity, object classification, natural scene recognition, reaction time
- MeSH terms
- Adult, Evoked Potentials, Visual, Female, Humans, Male, Middle Aged, Reaction Time, Vision, Ocular
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 ›
Are you an author, or do you see a problem with this page? Request a review
