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Is it an animal? Is it a human face? Fast processing in upright and inverted natural scenes
Open accessAbstract
Object categorization can be extremely fast. But among all objects, human faces might hold a special status that could depend on a specialized module. Visual processing could thus be faster for faces than for any other kind of object. Moreover, because face processing might rely on facial configuration, it could be more disrupted by stimulus inversion. Here we report two experiments that compared the rapid categorization of human faces and animals or animal faces in the context of upright and inverted natural scenes. In Experiment 1, the natural scenes contained human faces and animals in a full range of scales from close-up to far views. In Experiment 2, targets were restricted to close-ups of human faces and animal faces. Both experiments revealed the remarkable object processing efficiency of our visual system and further showed (1) virtually no advantage for faces over animals; (2) very little performance impairment with inversion; and (3) greater sensitivity of faces to inversion. These results are interpreted within the framework of a unique system for object processing in the ventral pathway. In this system, evidence would accumulate very quickly and efficiently to categorize visual objects, without involving a face module or a mental rotation mechanism. It is further suggested that rapid object categorization in natural scenes might not rely on high-level features but rather on features of intermediate complexity.
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“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 [34]
In the citing paper
“Recent behavioral studies (34)** suggested that the animal categorization task can be performed very well by human observers on rotated images.”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [34]
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Self-citation
Metadata
- Authors
- Rousselet, G. A., Macé, M. J.-M., & Fabre-Thorpe, M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Corresponding author
- Rousselet, Guillaume A.
- Macé, Marc J.-M.
- Venue
- Journal of Vision
- Published
- 2003-07-31
- Volume
- 3
- Issue
- 6
- Pages
- 5
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2170599782
- Cited by
- 215
- References
- 54
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 8 · 2021: 5 · 2020: 7 · 2019: 15 · 2018: 5 · 2017: 9 · 2016: 10 · 2015: 11 · 2014: 13 · 2013: 11 · 2012: 14
- FWCI
- 3.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Vision (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (submittedVersion)
- Funders
- Centre National de la Recherche Scientifique
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- face perception, object classification, natural images, animal faces, ventral stream, rapid visual processing, mental rotation
- MeSH terms
- Adult, Animals, Cognition, Face, Female, Form Perception, Generalization, Stimulus, Humans, Male, Memory, Middle Aged, Orientation
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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