• Are we ready to tackle percept...

Treffer: Are we ready to tackle perceptual segmentation of natural scenes?

Title:
Are we ready to tackle perceptual segmentation of natural scenes?
Authors:
Coen-Cagli R; Department of Systems Computational Biology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 Bronx, NY, United States; Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 Bronx, NY, United States; Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, 1300 Morris Park Avenue, 10461 Bronx, NY, United States. Electronic address: ruben.coen-cagli@einsteinmed.edu., Mamassian P; Laboratoire des systèmes perceptifs, Département d'études cognitives, École normale supérieure, PSL University, CNRS, Paris, France.
Source:
Vision research [Vision Res] 2026 Mar; Vol. 240, pp. 108749. Date of Electronic Publication: 2025 Dec 22.
Publication Type:
Journal Article; Review
Language:
English
Journal Info:
Publisher: Elsevier Science Ltd Country of Publication: England NLM ID: 0417402 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-5646 (Electronic) Linking ISSN: 00426989 NLM ISO Abbreviation: Vision Res Subsets: MEDLINE
Imprint Name(s):
Publication: Kidlington, Oxford : Elsevier Science Ltd.
Original Publication: Oxford [etc.]
MeSH Terms:
Visual Perception*/physiology , Pattern Recognition, Visual*/physiology, Humans ; Psychophysics ; Machine Learning
References:
Vision Res. 1983;23(2):191-8. (PMID: 6868394)
Elife. 2016 Jun 13;5:. (PMID: 27291188)
Nat Neurosci. 1999 Nov;2(11):1019-25. (PMID: 10526343)
Neuron. 2017 Jul 5;95(1):209-220.e3. (PMID: 28625487)
Nat Neurosci. 2016 Mar;19(3):366-74. (PMID: 26906503)
Nat Neurosci. 2015 Nov;18(11):1648-55. (PMID: 26436902)
IEEE Trans Pattern Anal Mach Intell. 2004 May;26(5):530-49. (PMID: 15460277)
Neuron. 2020 Nov 11;108(3):413-423. (PMID: 32918861)
Nat Neurosci. 2011 Aug 14;14(9):1195-201. (PMID: 21841776)
Trends Cogn Sci. 2011 Apr;15(4):160-8. (PMID: 21420894)
Trends Cogn Sci. 2012 Oct;16(10):504-10. (PMID: 22959875)
PLoS Comput Biol. 2020 Nov 30;16(11):e1006308. (PMID: 33253195)
Psychol Sci. 2003 Jan;14(1):26-30. (PMID: 12564750)
Trends Cogn Sci. 2007 Feb;11(2):58-64. (PMID: 17188555)
PLoS Biol. 2017 Aug 21;15(8):e1002611. (PMID: 28827801)
Invest Ophthalmol Vis Sci. 1979 Sep;18(9):893-912. (PMID: 478780)
Vision Res. 2006 Sep;46(18):2934-40. (PMID: 16707146)
Perception. 1993;22(12):1483-501. (PMID: 8090623)
Neuroscience. 2015 Jun 18;296:101-9. (PMID: 25280789)
Vision Res. 2021 Oct;187:55-65. (PMID: 34217005)
PLoS Comput Biol. 2020 Jul 21;16(7):e1008017. (PMID: 32692780)
Biol Cybern. 2023 Oct;117(4-5):299-329. (PMID: 37306782)
Neuron. 2015 Oct 7;88(1):78-92. (PMID: 26447574)
PLoS Comput Biol. 2019 May 10;15(5):e1006580. (PMID: 31075131)
Psychol Rev. 2002 Jul;109(3):492-519. (PMID: 12088242)
Nat Rev Neurosci. 2007 Jul;8(7):522-35. (PMID: 17585305)
Nat Neurosci. 2006 Apr;9(4):578-85. (PMID: 16547513)
Neuron. 2025 Aug 6;113(15):2508-2520.e5. (PMID: 40460828)
Curr Opin Neurobiol. 2020 Dec;65:176-193. (PMID: 33279795)
Psychol Sci. 2011 Jun;22(6):812-20. (PMID: 21597102)
Neuron. 2023 Apr 5;111(7):1003-1019. (PMID: 37023707)
Trends Cogn Sci. 2010 Mar;14(3):119-30. (PMID: 20153683)
Vision Res. 1999;39(25):4244-57. (PMID: 10755161)
Cogn Psychol. 1998 Feb;35(1):71-98. (PMID: 9520318)
Neuron. 2006 Jun 15;50(6):951-62. (PMID: 16772175)
Vis Neurosci. 2009 Jan-Feb;26(1):109-21. (PMID: 19216819)
Neural Netw. 2022 May;149:107-123. (PMID: 35228148)
Neuron. 2015 Feb 4;85(3):512-8. (PMID: 25654257)
Neuron. 2005 Jul 7;47(1):155-66. (PMID: 15996555)
PLoS Comput Biol. 2024 Apr 29;20(4):e1012030. (PMID: 38683837)
Psychol Bull. 2012 Nov;138(6):1172-217. (PMID: 22845751)
Psychol Rev. 1987 Apr;94(2):115-147. (PMID: 3575582)
Nature. 2002 Jan 24;415(6870):433-6. (PMID: 11807555)
PLoS Comput Biol. 2023 Sep 25;19(9):e1011483. (PMID: 37747914)
Prog Brain Res. 2006;154:93-120. (PMID: 17010705)
J Exp Psychol Hum Percept Perform. 2010 Dec;36(6):1443-1459. (PMID: 20731520)
Psychol Sci. 2012 Dec;23(12):1482-9. (PMID: 23137967)
J Exp Psychol Hum Percept Perform. 1988 Nov;14(4):716-28. (PMID: 2974878)
Ann N Y Acad Sci. 2020 Mar;1464(1):222-241. (PMID: 32112444)
J Exp Psychol Hum Percept Perform. 1998 Apr;24(2):441-62. (PMID: 9554093)
Percept Psychophys. 2000 Oct;62(7):1321-31. (PMID: 11143444)
Neuron. 2012 Feb 9;73(3):415-34. (PMID: 22325196)
IEEE Trans Image Process. 1998;7(7):1014-28. (PMID: 18276317)
PLoS Comput Biol. 2020 Oct 2;16(10):e1008215. (PMID: 33006992)
Neuron. 2020 Dec 23;108(6):1181-1193.e8. (PMID: 33301712)
Trends Neurosci. 1993 Jul;16(7):263-8. (PMID: 7689769)
J Vis. 2009 Jul 22;9(7):13. (PMID: 19761328)
J Autism Dev Disord. 2006 Jan;36(1):5-25. (PMID: 16450045)
IEEE Trans Image Process. 2007 Apr;16(4):1121-30. (PMID: 17405442)
Curr Opin Neurobiol. 2008 Aug;18(4):445-51. (PMID: 18835355)
Nat Neurosci. 2015 Oct;18(10):1509-17. (PMID: 26343249)
J Exp Psychol Hum Percept Perform. 2009 Jun;35(3):661-687. (PMID: 19485684)
J Autism Dev Disord. 2006 Jan;36(1):27-43. (PMID: 16453071)
Curr Opin Behav Sci. 2021 Apr;38:150-162. (PMID: 34026948)
Nat Neurosci. 2013 Sep;16(9):1170-8. (PMID: 23955561)
Trends Cogn Sci. 2005 Jan;9(1):16-20. (PMID: 15639436)
Vision Res. 2012 Apr;58:59-67. (PMID: 22387319)
J Neurosci. 2011 Mar 16;31(11):4012-24. (PMID: 21411644)
Vision Res. 2008 Jun;48(12):1374-82. (PMID: 18452968)
Nat Neurosci. 2019 Jun;22(6):974-983. (PMID: 31036945)
Vision Res. 2001 Mar;41(6):711-24. (PMID: 11248261)
Psychol Bull. 2015 May;141(3):549-73. (PMID: 25420221)
Nature. 1998 Sep 24;395(6700):376-81. (PMID: 9759726)
Trends Cogn Sci. 1997 Nov;1(8):296-304. (PMID: 21223932)
Cereb Cortex. 2023 Jul 5;33(14):9186-9211. (PMID: 37317036)
Neural Netw. 2019 Nov;119:113-138. (PMID: 31404805)
J Vis. 2009 Jul 09;9(7):5. (PMID: 19761320)
IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2481-2495. (PMID: 28060704)
Vision Res. 1993 Jan;33(2):173-93. (PMID: 8447091)
Neural Comput. 2004 Mar;16(3):445-76. (PMID: 15006089)
Vision Res. 2020 Sep;174:22-40. (PMID: 32521340)
Grant Information:
R01 EY030578 United States EY NEI NIH HHS; R01 EY031166 United States EY NEI NIH HHS
Contributed Indexing:
Keywords: Algorithms; Grouping; Integration; Natural scenes; Segmentation; Uncertainty
Entry Date(s):
Date Created: 20251223 Date Completed: 20260201 Latest Revision: 20260202
Update Code:
20260202
PubMed Central ID:
PMC12832203
DOI:
10.1016/j.visres.2025.108749
PMID:
41435693
Database:
MEDLINE

Weitere Informationen

Processes of perceptual segmentation and integration (PSI) are fundamental to perceptual organization. Although PSI of visual stimuli has been studied for over a century, we have only a rudimentary understanding of PSI of natural visual stimuli. This is due to limitations of traditional experimental methods in visual psychophysics of PSI; to the exclusive focus of computer-vision research for image segmentation on performance benchmarks; and to the scarcity of meaningful interactions between those two communities. The recent literature discussed in this paper presents a compelling argument that the field is starting to overcome those barriers. One important example of such an interaction between visual psychophysics and machine learning is given by the literature on the crowding phenomenon, which calls for revised models of summary statistics to explain some uncrowding results. Other examples reviewed here include studies of the perceptual uncertainty and dynamics of segmentation of natural stimuli, which call for computational models with probabilistic representations and dynamic computations. Conversely, contemporary machine learning algorithms produce impressive segmentation maps that still need to be aligned with human maps as measured with objective tasks such as the same/different segment paradigm reviewed here. Therefore, the time is ripe to move vision science forward by bridging new computational and experimental paradigms for PSI of natural stimuli.
(Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.