The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Chapter 2 Building the gist of a scene: the role of global image features in recognition
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Marr, D., & Hildreth, E. (1980) · Proceedings of the Royal Society of London. Series B. Biological Sciences · doi:10.1098/rspb.1980.0020 · Cited by 6,510
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Forest before trees: The precedence of global features in visual perception
Navon, D. (1977) · Cognitive Psychology · doi:10.1016/0010-0285(77)90012-3 · Cited by 4,005
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538
Hierarchical models of object recognition in cortex
Riesenhuber, M., & Poggio, T. (1999) · Nature Neuroscience · doi:10.1038/14819 · Cited by 3,309
Bar, M. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1476 · Cited by 1,613
From Blobs to Boundary Edges: Evidence for Time- and Spatial-Scale-Dependent Scene Recognition
Schyns, P. G., & Oliva, A. (1994) · Psychological Science · doi:10.1111/j.1467-9280.1994.tb00500.x · Cited by 715
Self-citationVisual features of intermediate complexity and their use in classification
Ullman, S., Vidal-Naquet, M., & Sali, E. (2002) · Nature Neuroscience · doi:10.1038/nn870 · Cited by 600
Preattentive Object Files: Shapeless Bundles of Basic Features
WOLFE, J. M., & BENNETT, S. C. (1997) · Vision Research · doi:10.1016/s0042-6989(96)00111-3 · Cited by 349
Cited by
Context Encoders: Feature Learning by Inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., & Efros, A. A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.278 · Cited by 5,691
Deep supervised, but not unsupervised, models may explain IT cortical representation
Khaligh-Razavi, S.-M., & Kriegeskorte, N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003915 · Cited by 1,204
The role of context in object recognition
Oliva, A., & Torralba, A. (2007) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2007.09.009 · Cited by 1,063
Self-citationVisual Place Recognition: A Survey
Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., & Milford, M. J. (2016) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2496823 · Cited by 1,079
Object Detectors Emerge in Deep Scene CNNs
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., & Torralba, A. (2014) · arXiv · doi:10.48550/arxiv.1412.6856 · Cited by 971
Self-citationSpace in the brain: How the hippocampal formation supports spatial cognition
Hartley, T., Lever, C., Burgess, N., & O'Keefe, J. (2014) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2012.0510 · Cited by 495
GREENE, M., & OLIVA, A. (2009) · Cognitive Psychology · doi:10.1016/j.cogpsych.2008.06.001 · Cited by 467
Self-citationRecurrence is required to capture the representational dynamics of the human visual system
Kietzmann, T. C., Spoerer, C. J., Sörensen, L. K. A., Cichy, R. M., Hauk, O., & Kriegeskorte, N. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1905544116 · Cited by 406
Baum, S. H., Stevenson, R. A., & Wallace, M. T. (2015) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2015.09.007 · Cited by 370
Tsintotas, K. A., Bampis, L., & Gasteratos, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3175656 · Cited by 175
Robust Object Co-Segmentation Using Background Prior
Han, J., Quan, R., Zhang, D., & Nie, F. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2781424 · Cited by 169
Design and Development of a Multimodal Biomedical Information Retrieval System
Demner-Fushman, D., Antani, S., Simpson, M., & Thoma, G. R. (2012) · Journal of Computing Science and Engineering · doi:10.5626/jcse.2012.6.2.168 · Cited by 161
Reconciling predictive coding and biased competition models of cortical function
Spratling, M. W. (2008) · Frontiers in Computational Neuroscience · doi:10.3389/neuro.10.004.2008 · Cited by 153
Multiple kernel learning for emotion recognition in the wild
Sikka, K., Dykstra, K., Sathyanarayana, S., Littlewort, G., & Bartlett, M. (2013) · Proceedings of the 15th ACM on International conference on multimodal interaction · doi:10.1145/2522848.2531741 · Cited by 119
How Inspecting a Picture Affects Processing of Text in Multimedia Learning
Eitel, A., Scheiter, K., & Schüler, A. (2013) · Applied Cognitive Psychology · doi:10.1002/acp.2922 · Cited by 85
Predictive feedback can account for biphasic responses in the lateral geniculate nucleus
Jehee, J. F. M., & Ballard, D. H. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000373 · Cited by 75
Evensmoen, H. R., Ladstein, J., Hansen, T. I., Møller, J. A., Witter, M. P., Nadel, L., & Håberg, A. K. (2015) · Hippocampus · doi:10.1002/hipo.22357 · Cited by 75
Cornelissen, T. H. W., & Võ, M. L.-H. (2017) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-016-1203-7 · Cited by 70
Deane, G. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab024 · Cited by 38
“Another example is gist perception in object recognition— where the ‘gist’ of the scene engages past experience to generate the most likely prediction about the object’s identity (Bar 2003; Oliva and Torralba 2006).”
BackgroundThe fundamentals of eye tracking part 6: working with areas of interest
Hooge, I. T. C., Nyström, M., Niehorster, D. C., Andersson, R., Foulsham, T., Nuthmann, A., & Hessels, R. S. (2026) · Behavior Research Methods · doi:10.3758/s13428-025-02937-3 · Cited by 22
Qianchen, L., Gallagher, R. M., & Tsuchiya, N. (2022) · Royal Society Open Science · doi:10.1098/rsos.210394 · Cited by 21
Cortically Coupled Computing: A New Paradigm for Synergistic Human-Machine Interaction
Saproo, S., Faller, J., Shih, V., Sajda, P., Waytowich, N. R., Bohannon, A., Lawhern, V. J., Lance, B. J., & Jangraw, D. (2016) · Computer · doi:10.1109/mc.2016.294 · Cited by 18
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
“…two approaches: a deep gated Hebbian PC (Dora et al., 2021) successfully learns internal representations of natural images across multiple layers of the visual processing hierarchy, while exhibiting neuronal response properties such as orientation selectivity, object selectivity, and sparseness.”
BackgroundSustained conscious access to incidental memories in RSVP
Matthews, J., Wu, J., Corneille, V., Hohwy, J., van Boxtel, J., & Tsuchiya, N. (2019) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-018-1600-1 · Cited by 9
An embarrassment of richnesses: the PFC isn’t the content NCC
Kozuch, B. (2024) · Neuroscience of Consciousness · doi:10.1093/nc/niae017 · Cited by 9
Metadata
- Authors
- Oliva, A., & Torralba, A.
- Venue
- Progress in Brain Research
- Publisher
- Elsevier
- Published
- 2006
- Volume
- 155
- Pages
- 23-36
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W1532257412
- Cited by
- 1,434
- References
- 9
- FWCI
- 60.01
- Type
- book chapter
- Language
- en
- Open access
- closed
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.