The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Explicit information for category-orthogonal object properties increases along the ventral stream
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
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Distributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
The fusiform face area: a module in human extrastriate cortex specialized for face perception
Kanwisher, N., McDermott, J., & Chun, M. M. (1997) · The Journal of Neuroscience · doi:10.1523/jneurosci.17-11-04302.1997 · Cited by 7,738
Basic objects in natural categories
Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90013-x · Cited by 5,643
McClelland, J. L., Rumelhart, D. E., & AU (1987) · doi:10.7551/mitpress/5237.001.0001 · Cited by 3,315
Object vision and spatial vision: Two cortical pathways
Mishkin, M., Ungerleider, L. G., & Macko, K. A. (1983) · Trends in Neurosciences · doi:10.1016/0166-2236(83)90190-x · Cited by 2,947
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Yamins, D. L. K., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1403112111 · Cited by 1,924
Self-citation‘What’ and ‘where’ in the human brain
Ungerleider, L. G., & Haxby, J. V. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90066-3 · Cited by 1,913
How does the brain solve visual object recognition?
DiCarlo, J. J., Zoccolan, D., & Rust, N. C. (2012) · Neuron · doi:10.1016/j.neuron.2012.01.010 · Cited by 1,790
Self-citation
Cited by
Neuroscience-Inspired Artificial Intelligence
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.06.011 · Cited by 1,557
Cognitive computational neuroscience
Kriegeskorte, N., & Douglas, P. K. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0210-5 · Cited by 535
‘What’ Is Happening in the Dorsal Visual Pathway
Freud, E., Plaut, D. C., & Behrmann, M. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.08.003 · Cited by 286
Understanding Low- and High-Level Contributions to Fixation Prediction
Kummerer, M., Wallis, T. S. A., Gatys, L. A., & Bethge, M. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.513 · Cited by 282
Deep Learning: The Good, the Bad, and the Ugly
Serre, T. (2019) · Annual Review of Vision Science · doi:10.1146/annurev-vision-091718-014951 · Cited by 267
Metadata
- Authors
- Hong, H., Yamins, D. L. K., Majaj, N. J., & DiCarlo, J. J.
- Venue
- Nature Neuroscience
- Published
- 2016-02-22
- Volume
- 19
- Issue
- 4
- Pages
- 613-622
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2286279415
- Cited by
- 337
- References
- 15
- FWCI
- 24.68
- Type
- journal article
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Explicit information for category-orthogonal object properties increases along the ventral stream. https://app.cochat.ai/papers/10.1038/nn.4247
- The paper
- Hong, H., Yamins, D. L. K., Majaj, N. J., & DiCarlo, J. J. (2016). Explicit information for category-orthogonal object properties increases along the ventral stream. Nature Neuroscience. https://doi.org/10.1038/nn.4247
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.
