The DOI matches its Crossref record.
Suppressive and facilitatory spatial interactions in peripheral vision: Peripheral crowding is neither size invariant nor simple contrast masking
Open accessAbstract
Peripheral vision is characterized by reduced spatial resolution and inhibitory spatial interactions that extend over long distances. This work had three goals. (1) We considered whether the extensive crowding in peripheral vision is a consequence of a shift in the spatial scale of analysis. To test this, using a large range of target sizes and spatial frequencies, we measured the extent of crowding for targets that were limited in their spatial frequency content. (2) We considered whether crowding in peripheral vision can be explained on the basis of contrast masking by remote flanks. To test this hypothesis, we measured and compared crowding in a direction-identification experiment with masking by remote flanks in a detection experiment. In each of the experiments, our targets and flanks were composed of Gabor features, thus allowing us to control the feature contrast, spatial frequency, and orientation. (3) We examined the relationship between the suppressive and facilitatory interactions in peripheral contrast detection and crowding. Our results show that unlike the normal fovea (Levi, Klein, & Hariharan, 2002), peripheral crowding is not scale invariant nor is it attributable to simple contrast masking. Rather, our results suggest that inhibitory spatial interactions in peripheral crowding extend over larger distances than in the fovea for targets of the same size. In peripheral vision, the critical distance for crowding is approximately 0.1 times the target eccentricity. Observers can easily detect the features that compose our targets (Gabor patches) under conditions where crowding is strong. Thus, we speculate that peripheral crowding occurs because the target and flanks are combined or pooled at a second stage, following the stage of feature extraction. In peripheral vision, this pooling takes place over a large distance.
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
Cited by
Visual crowding: a fundamental limit on conscious perception and object recognition
Whitney, D., & Levi, D. M. (2011) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.02.005 · Cited by 849
Self-citationCrowding is unlike ordinary masking: Distinguishing feature integration from detection
Pelli, D. G., Palomares, M., & Majaj, N. J. (2004) · Journal of Vision · doi:10.1167/4.12.12 · Cited by 832
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
Metadata
- Authors
- Levi, D. M., Hariharan, S., & Klein, S. A.
- Venue
- Journal of Vision
- Published
- 2002-04-01
- Volume
- 2
- Issue
- 2
- Pages
- 3-3
- ISSN
- 1534-7362 (ISSN-L)
- OpenAlex
- W2172002033
- Cited by
- 240
- References
- 0
- FWCI
- 3.39
- Type
- journal article
- Language
- en
- Open access
- gold
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.