IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-33508-1.html
   My bibliography  Save this article

Crowding results from optimal integration of visual targets with contextual information

Author

Listed:
  • Guido Marco Cicchini

    (CNR)

  • Giovanni D’Errico

    (CNR)

  • David Charles Burr

    (CNR
    University of Florence)

Abstract

Crowding is the inability to recognize an object in clutter, usually considered a fundamental low-level bottleneck to object recognition. Here we advance and test an alternative idea, that crowding, like predictive phenomena such as serial dependence, results from optimizing strategies that exploit redundancies in natural scenes. This notion leads to several testable predictions: crowding should be greatest for unreliable targets and reliable flankers; crowding-induced biases should be maximal when target and flankers have similar orientations, falling off for differences around 20°; flanker interference should be associated with higher precision in orientation judgements, leading to lower overall error rate; effects should be maximal when the orientation of the target is near that of the average of the flankers, rather than to that of individual flankers. Each of these predictions were supported, and could be simulated with ideal-observer models that maximize performance. The results suggest that while crowding can affect object recognition, it may be better understood not as a processing bottleneck, but as a consequence of efficient exploitation of the spatial redundancies of the natural world.

Suggested Citation

  • Guido Marco Cicchini & Giovanni D’Errico & David Charles Burr, 2022. "Crowding results from optimal integration of visual targets with contextual information," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33508-1
    DOI: 10.1038/s41467-022-33508-1
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-33508-1
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-33508-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Konrad P Körding & Ulrik Beierholm & Wei Ji Ma & Steven Quartz & Joshua B Tenenbaum & Ladan Shams, 2007. "Causal Inference in Multisensory Perception," PLOS ONE, Public Library of Science, vol. 2(9), pages 1-10, September.
    2. Marc O. Ernst & Martin S. Banks, 2002. "Humans integrate visual and haptic information in a statistically optimal fashion," Nature, Nature, vol. 415(6870), pages 429-433, January.
    3. Konrad P. Körding & Daniel M. Wolpert, 2004. "Bayesian integration in sensorimotor learning," Nature, Nature, vol. 427(6971), pages 244-247, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Adam N Sanborn & Ulrik R Beierholm, 2016. "Fast and Accurate Learning When Making Discrete Numerical Estimates," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-28, April.
    2. Tim Genewein & Eduard Hez & Zeynab Razzaghpanah & Daniel A Braun, 2015. "Structure Learning in Bayesian Sensorimotor Integration," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-27, August.
    3. Jannes Jegminat & Maya A Jastrzębowska & Matthew V Pachai & Michael H Herzog & Jean-Pascal Pfister, 2020. "Bayesian regression explains how human participants handle parameter uncertainty," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-23, May.
    4. Wen-Hao Zhang & Si Wu & Krešimir Josić & Brent Doiron, 2023. "Sampling-based Bayesian inference in recurrent circuits of stochastic spiking neurons," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    5. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Brocas, Isabelle & Carrillo, Juan D., 2012. "From perception to action: An economic model of brain processes," Games and Economic Behavior, Elsevier, vol. 75(1), pages 81-103.
    7. Philipp Schustek & Rubén Moreno-Bote, 2018. "Instance-based generalization for human judgments about uncertainty," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-27, June.
    8. Peter W Battaglia & Daniel Kersten & Paul R Schrater, 2011. "How Haptic Size Sensations Improve Distance Perception," PLOS Computational Biology, Public Library of Science, vol. 7(6), pages 1-13, June.
    9. Luigi Acerbi & Kalpana Dokka & Dora E Angelaki & Wei Ji Ma, 2018. "Bayesian comparison of explicit and implicit causal inference strategies in multisensory heading perception," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-38, July.
    10. Ksander N de Winkel & Mikhail Katliar & Heinrich H Bülthoff, 2017. "Causal Inference in Multisensory Heading Estimation," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-20, January.
    11. Jingwei Sun & Jian Li & Hang Zhang, 2019. "Human representation of multimodal distributions as clusters of samples," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-29, May.
    12. Christoph Kayser & Ladan Shams, 2015. "Multisensory Causal Inference in the Brain," PLOS Biology, Public Library of Science, vol. 13(2), pages 1-7, February.
    13. David R Wozny & Ulrik R Beierholm & Ladan Shams, 2010. "Probability Matching as a Computational Strategy Used in Perception," PLOS Computational Biology, Public Library of Science, vol. 6(8), pages 1-7, August.
    14. Pedram Daee & Maryam S Mirian & Majid Nili Ahmadabadi, 2014. "Reward Maximization Justifies the Transition from Sensory Selection at Childhood to Sensory Integration at Adulthood," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    15. Luigi Acerbi & Daniel M Wolpert & Sethu Vijayakumar, 2012. "Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing," PLOS Computational Biology, Public Library of Science, vol. 8(11), pages 1-19, November.
    16. Patricia Besson & Christophe Bourdin & Lionel Bringoux, 2011. "A Comprehensive Model of Audiovisual Perception: Both Percept and Temporal Dynamics," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-11, August.
    17. Wendy J Adams, 2016. "The Development of Audio-Visual Integration for Temporal Judgements," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-17, April.
    18. Max Berniker & Martin Voss & Konrad Kording, 2010. "Learning Priors for Bayesian Computations in the Nervous System," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
    19. Carrillo, Juan & Brocas, Isabelle, 2007. "Reason, Emotion and Information Processing in the Brain," CEPR Discussion Papers 6535, C.E.P.R. Discussion Papers.
    20. Udo A Ernst & Sunita Mandon & Nadja Schinkel–Bielefeld & Simon D Neitzel & Andreas K Kreiter & Klaus R Pawelzik, 2012. "Optimality of Human Contour Integration," PLOS Computational Biology, Public Library of Science, vol. 8(5), pages 1-17, May.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33508-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.