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The time course of visual processing: Backward masking and natural scene categorisation
Open accessAbstract
Human observers are very good at deciding whether briefly flashed novel images contain an animal and previous work has shown that the underlying visual processing can be performed in under 150 ms. Here we used a masking paradigm to determine how information accumulates over time during such high-level categorisation tasks. As the delay between test image and mask is increased, both behavioural accuracy and differential ERP amplitude rapidly increase to reach asymptotic levels around 40-60 ms. Such results imply that processing at each stage in the visual system is remarkably rapid, with information accumulating almost continuously following the onset of activation.
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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 [31]
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 [31]
In the citing paper
“It was found (31) that increasing the SOA on a similar animalvs. non-animalcategorization task above 44 ms only has a minor effect on performance (accuracy scores for longer SOA conditions were not significantly different).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [31]
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 [31]
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Metadata
- Authors
- Bacon-Macé, N., Macé, M. J.-M., Fabre-Thorpe, M., & Thorpe, S. J.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Bacon-Macé, Nadège
- Venue
- Vision Research
- Publisher
- Elsevier BV
- Published
- 2005-05
- Volume
- 45
- Issue
- 11
- Pages
- 1459-1469
- ISSN
- 0042-6989 (ISSN-L)
- OpenAlex
- W2135566527
- Cited by
- 236
- References
- 54
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 11 · 2023: 7 · 2022: 6 · 2021: 10 · 2020: 8 · 2019: 19 · 2018: 6 · 2017: 7 · 2016: 8 · 2015: 18 · 2014: 8 · 2013: 15 · 2012: 14
- FWCI
- 6.59
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Vision Research (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Centre National de la Recherche Scientifique
- 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
- backward masking, event-related potentials, information accumulation, rapid visual processing, animal recognition
- MeSH terms
- Adult, Discrimination, Psychological, Electroencephalography, Female, Humans, Male, Middle Aged, Perceptual Masking, Psychophysics, Reaction Time, Sensitivity and Specificity, Visual Pathways, Visual Perception, Chi-Square Distribution, Case-Control Studies, Statistics, Nonparametric
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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