CoChat
Sign Up
Cited by 102Visual perception and processing mechanismsVisual Attention and Saliency DetectionFace Recognition and Perception

Multi-level crowding and the paradox of object recognition in clutter

Open access
Manassi, Mauro, Whitney, David·2018·Current Biology
DOI 10.1016/j.cub.2017.12.051
Open this paper in CoChat — free accountOpen PDF

Abstract

In everyday life, we are constantly surrounded by complex and cluttered scenes. In such cluttered environments, visual perception is primarily limited by crowding, the deleterious influence of nearby objects on object recognition. For the past several decades, visual crowding was assumed to occur at a single stage, only between low-level features or object parts, thus dismantling, destroying, or filtering object information. A large and converging body of evidence has demonstrated that this assumption is false: crowding occurs at multiple stages of visual analysis, and information passes through crowding at each of these stages. This converging empirical evidence points to a seeming paradox: crowding happens at multiple levels, which would seem to impair object recognition, and yet visual information at each of those levels is maintained intact and influences subsequent higher-level visual processing. Thus, while crowding impairs the access we have to visual information at many levels, it does not impair the representation of that information. The resolution of this paradox reveals how the visual system strikes a balance between the limits of object selection and the desire to represent multiple levels of visual information throughout cluttered scenes. Understanding crowding is therefore key to resolving the relationship between the richness of object and scene representations and the limits of conscious object recognition.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.cub.2017.12.051

Cites

Statistics

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.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Detection ability degrades on the periphery of the visual field because of reduced visual acuity and crowding.

    “In addition, without us noticing, detection ability degrades on the periphery of the visual field ( Fig. 1 ) because of reduced visual acuity and crowding [19 , 20] .”

    From Enhancing human-AI collaboration: The case of colonoscopy (2024) · cites this paper as [20]

    The Summary describes crowding as limiting object recognition in clutter, and the Introduction discusses crowding in peripheral vision. These passages support the crowding component, but our copy does not attribute degraded peripheral detection to reduced visual acuity; it discusses recognition rather than detection. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

No references could be resolved yet.

Part of this paper’s reference list could not be split into single entries.

Research works printed but not linked: 49

  1. [1]1. Levi, D.M. Crowding–an essential bottleneck for object recognition: a mini-review Vision. Res. 2008; 48:635-654 Crossref Scopus (739) PubMed Google Scholar
  2. [2]2. Pelli, D.G. ∙ Tillman, K.A. The uncrowded window of object recognition Nat. Neurosci. 2008; 11:1129-1135 Crossref Scopus (507) PubMed Google Scholar
  3. [3]3. Whitney, D. ∙ Levi, D.M. Visual crowding: a fundamental limit on conscious perception and object recognition Trends. Cogn. Sci. 2011; 15:160-168 Full Text Full Text (PDF) Scopus (614) PubMed Google Scholar
  4. [4]4. Faivre, N. ∙ Berthet, V. ∙ Kouider, S. Sustained invisibility through crowding and continuous flash suppression: a comparative review Front. Psychol. 2014; 5:475 Crossref Scopus (32) PubMed Google Scholar
  5. [5]5. Parkes, L. ∙ Lund, J. ∙ Angelucci, A. ... Compulsory averaging of crowded orientation signals in human vision Nat. Neurosci. 2001; 4:739-744 Crossref Scopus (728) PubMed Google Scholar
  6. [6]6. Harrison, W.J. ∙ Bex, P.J. A unifying model of orientation crowding in peripheral vision Curr. Biol. 2015; 25:3213-3219 Full Text Full Text (PDF) Scopus (50) PubMed Google Scholar
  7. [7]7. Balas, B. ∙ Nakano, L. ∙ Rosenholtz, R. A summary-statistic representation in peripheral vision explains visual crowding J. Vis. 2009; 9:1-18 Crossref PubMed Google Scholar
  8. [8]8. Freeman, J. ∙ Simoncelli, E.P. Metamers of the ventral stream Nat. Neurosci. 2011; 14:1195-1201 Crossref Scopus (448) PubMed Google Scholar
  9. [9]9. Ester, E.F. ∙ Klee, D. ∙ Awh, E. Visual crowding cannot be wholly explained by feature pooling J. Exp. Psychol. Hum. Percept. Perform. 2014; 40:1022-1033 Crossref Scopus (46) PubMed Google Scholar
  10. [10]10. Huckauf, A. ∙ Heller, D. What various kinds of errors tell us about lateral masking effects Vis. Cogn. 2002; 9:889-910 Crossref Scopus (47) Google Scholar
  11. [11]11. He, S. ∙ Cavanagh, P. ∙ Intriligator, J. Attentional resolution and the locus of visual awareness Nature. 1996; 383:334-337 Crossref Scopus (783) PubMed Google Scholar
  12. [12]12. Herzog, M.H. ∙ Sayim, B. ∙ Chicherov, V. ... Crowding, grouping, and object recognition: A matter of appearance J. Vis. 2015; 15:1-18 Crossref Scopus (98) Google Scholar
  13. [13]13. Manassi, M. ∙ Sayim, B. ∙ Herzog, M.H. Grouping, pooling, and when bigger is better in visual crowding J. Vis. 2012; 12:1-14 Crossref Scopus (130) Google Scholar
  14. [14]14. Sayim, B. ∙ Westheimer, G. ∙ Herzog, M.H. Gestalt factors modulate basic spatial vision Psychol. Sci. 2010; 21:641-644 Crossref Scopus (75) PubMed Google Scholar
  15. [15]15. Maus, G.W. ∙ Fischer, J. ∙ Whitney, D. Perceived positions determine crowding PLoS One. 2011; 6:e19796 Crossref Scopus (26) PubMed Google Scholar
  16. [16]16. Dakin, S.C. ∙ Greenwood, J.A. ∙ Carlson, T.A. ... Crowding is tuned for perceived (not physical) location J.Vis. 2011; 11:1-13 Crossref Scopus (27) Google Scholar
  17. [17]17. Martelli, M. ∙ Majaj, N.J. ∙ Pelli, D.G. Are faces processed like words? A diagnostic test for recognition by parts J. Vis. 2005; 5:58-70 Crossref Scopus (194) PubMed Google Scholar
  18. [18]18. Sun, H.M. ∙ Balas, B. Face features and face configurations both contribute to visual crowding Atten. Percept. Psychophys. 2015; 77:508-519 Crossref Scopus (13) PubMed Google Scholar
  19. [19]19. Kimchi, R. ∙ Pirkner, Y. Multiple level crowding: crowding at the object parts level and at the object configural level Perception. 2015; 44:1275-1292 Crossref Scopus (20) PubMed Google Scholar
  20. [20]20. Huckauf, A. ∙ Heller, D. ∙ Nazir, T.A. Lateral masking: limitations of the feature interaction account Percept. Psychophys. 1999; 61:177-189 Crossref Scopus (49) PubMed Google Scholar
  21. [21]21. Reuther, J. ∙ Chakravarthi, R. Categorical membership modulates crowding: evidence from characters J. Vis. 2014; 14:1-13 Crossref Scopus (17) Google Scholar
  22. [22]22. Louie, E.G. ∙ Bressler, D.W. ∙ Whitney, D. Holistic crowding: selective interference between configural representations of faces in crowded scenes J. Vis. 2007; 7:1-11 Crossref Scopus (33) PubMed Google Scholar
  23. [23]23. Kalpadakis-Smith, A. ∙ Goffaux, V. ∙ Greenwood, J.A. Crowding is not holistic: Visual similarity determines the effect of clutter on faces PsyArXiv. 2017; Crossref Google Scholar
  24. [24]24. Kanwisher, N. ∙ Yovel, G. The fusiform face area: a cortical region specialized for the perception of faces Philos. Trans. R. Soc. Lond. B. 2006; 361:2109-2128 Crossref Scopus (1195) PubMed Google Scholar
  25. [25]25. Farzin, F. ∙ Rivera, S.M. ∙ Whitney, D. Holistic crowding of Mooney faces J. Vis. 2009; 9:1-15 Crossref Scopus (83) PubMed Google Scholar
  26. [26]26. Ikeda, H. ∙ Watanabe, K. ∙ Cavanagh, P. Crowding of biological motion stimuli J. Vis. 2013; 13:1-6 Crossref Scopus (20) Google Scholar
  27. [27]27. Ikeda, H. ∙ Watanabe, K. Action congruency influences crowding when discriminating biological motion direction Perception. 2016; 45:1046-1059 Crossref Scopus (4) PubMed Google Scholar
  28. [28]28. Rajimehr, R. ∙ Montaser-Kouhsari, L. ∙ Afraz, S.R. Orientation-selective adaptation to crowded illusory lines Perception. 2003; 32:1199-1210 Crossref Scopus (15) PubMed Google Scholar
  29. [29]29. Faivre, N. ∙ Kouider, S. Increased sensory evidence reverses nonconscious priming during crowding J. Vis. 2011; 11:1-13 Crossref Scopus (13) Google Scholar
  30. [30]30. Aghdaee, S.M. Adaptation to spiral motion in crowding condition Perception. 2005; 34:155-162 Crossref Scopus (31) PubMed Google Scholar
  31. [31]31. Rajimehr, R. ∙ Vaziri-Pashkam, M. ∙ Afraz, S.R. ... Adaptation to apparent motion in crowding condition Vis. Res. 2004; 44:925-931 Crossref Scopus (26) PubMed Google Scholar
  32. [32]32. Whitney, D. Motion distorts perceived position without awareness of motion Curr. Biol. 2005; 15:R324-326 Full Text Full Text (PDF) Scopus (36) PubMed Google Scholar
  33. [33]33. Lau, J.S. ∙ Cheung, S.H. Illusory contour formation survives crowding J. Vis. 2012; 12:1-12 Crossref Scopus (16) Google Scholar
  34. [34]34. Boucart, M. ∙ Lenoble, Q. ∙ Quettelart, J. ... Finding faces, animals, and vehicles in far peripheral vision J. Vis. 2016; 16:1-13 Crossref Scopus (41) Google Scholar
  35. [35]35. Bayle, D.J. ∙ Schoendorff, B. ∙ Henaff, M.A. ... Emotional facial expression detection in the peripheral visual field PLoS One. 2011; 6:e21584 Crossref Scopus (57) PubMed Google Scholar
  36. [36]36. McKone, E. Isolating the special component of face recognition: peripheral identification and a Mooney face J. Exp. Psychol. Learn. Mem. Cogn. 2004; 30:181-197 Crossref Scopus (98) PubMed Google Scholar
  37. [37]37. Kouider, S. ∙ Berthet, V. ∙ Faivre, N. Preference is biased by crowded facial expressions Psychol. Sci. 2011; 22:184-189 Crossref Scopus (50) PubMed Google Scholar
  38. [38]38. Fischer, J. ∙ Whitney, D. Object-level visual information gets through the bottleneck of crowding J. Neurophysiol. 2011; 106:1389-1398 Crossref Scopus (69) PubMed Google Scholar
  39. [39]39. Yeh, S.L. ∙ He, S. ∙ Cavanagh, P. Semantic priming from crowded words Psychol. Sci. 2012; 23:608-616 Crossref Scopus (51) PubMed Google Scholar
  40. [40]40. Zhou, J. ∙ Lee, C.-L. ∙ Yeh, S.-L. Word meanings survive visual crowding: evidence from ERPs Lang. Cogn. Neurosci. 2016; 31:1167-1177 Crossref Scopus (9) Google Scholar
  41. [41]41. Chen, J. ∙ Sperandio, I. ∙ Goodale, M.A. Differences in the effects of crowding on size perception and grip scaling in densely cluttered 3-D scenes Psychol. Sci. 2015; 26:58-69 Crossref Scopus (25) PubMed Google Scholar
  42. [42]42. Huckauf, A. Task set determines the amount of crowding Psychol. Res. 2007; 71:646-652 Crossref Scopus (8) PubMed Google Scholar
  43. [43]43. Huckauf, A. ∙ Knops, A. ∙ Nuerk, H.C. ... Semantic processing of crowded stimuli? Psychol. Res. 2008; 72:648-656 Crossref Scopus (0) PubMed Google Scholar
  44. [44]44. Yeshurun, Y. ∙ Rashal, E. Precueing attention to the target location diminishes crowding and reduces the critical distance J. Vis. 2010; 10:1-12 Crossref Scopus (150) Google Scholar
  45. [45]45. Shin, K. ∙ Chung, S.T.L. ∙ Tjan, B.S. Crowding, visual awareness, and their respective neural loci J. Vis. 2017; 17:1-18 Crossref Scopus (8) Google Scholar
  46. [46]46. Chen, J. ∙ He, Y. ∙ Zhu, Z. ... Attention-dependent early cortical suppression contributes to crowding J. Neurosci. 2014; 34:10465-10474 Crossref Scopus (67) PubMed Google Scholar
  47. [47]47. Millin, R. ∙ Arman, A.C. ∙ Chung, S.T. ... Visual crowding in V1 Cereb. Cortex. 2014; 24:3107-3115 Crossref Scopus (61) PubMed Google Scholar
  48. [48]48. Anderson, E.J. ∙ Dakin, S.C. ∙ Schwarzkopf, D.S. ... The neural correlates of crowding-induced changes in appearance Curr. Biol. 2012; 22:1199-1206 Full Text Full Text (PDF) Scopus (78) PubMed Google Scholar
  49. [49]49. Freeman, J. ∙ Donner, T.H. ∙ Heeger, D.J. Inter-area correlations in the ventral visual pathway reflect feature integration J. Vis. 2011; 11:1-23 Crossref Google Scholar

Cited by

Sorted by citations

Authors
Manassi, M., & Whitney, D.
Affiliations
University of California, Berkeley (US)
Corresponding author
Manassi, Mauro
ORCID
Venue
Current Biology
Publisher
Elsevier BV
Published
2018-02
Volume
28
Issue
3
Pages
R127-R133
ISSN
0960-9822 (ISSN-L)
DOI
10.1016/j.cub.2017.12.051
OpenAlex
W2786995129
Cited by
102
References
0
Citations by year
2026: 1 · 2025: 8 · 2024: 11 · 2023: 14 · 2022: 17 · 2021: 16 · 2020: 16 · 2019: 13 · 2018: 6
FWCI
1.72
Type
journal article
Language
en
Open access
Licence
Locations
  • Current Biology (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
Keywords
visual crowding, object recognition, cluttered scenes, visual perception, visual representation, conscious perception, object selection
MeSH terms
Attention, Crowding, Humans, Visual Fields, Visual Perception

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