IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0082122.html
   My bibliography  Save this article

Evidence Accumulation in the Magnitude System

Author

Listed:
  • Anna Lambrechts
  • Vincent Walsh
  • Virginie van Wassenhove

Abstract

Perceptual interferences in the estimation of quantities (time, space and numbers) have been interpreted as evidence for a common magnitude system. However, if duration estimation has appears sensitive to spatial and numerical interferences, space and number estimation tend to be resilient to temporal manipulations. These observations question the relative contribution of each quantity in the elaboration of a representation in a common mental metric. Here, we elaborated a task in which perceptual evidence accumulated over time for all tested quantities (space, time and number) in order to match the natural requirement for building a duration percept. For this, we used a bisection task. Experimental trials consisted of dynamic dots of different sizes appearing progressively on the screen. Participants were asked to judge the duration, the cumulative surface or the number of dots in the display while the two non-target dimensions varied independently. In a prospective experiment, participants were informed before the trial which dimension was the target; in a retrospective experiment, participants had to attend to all dimensions and were informed only after a given trial which dimension was the target. Surprisingly, we found that duration was resilient to spatial and numerical interferences whereas space and number estimation were affected by time. Specifically, and counter-intuitively, results revealed that longer durations lead to smaller number and space estimates whether participants knew before (prospectively) or after (retrospectively) a given trial which quantity they had to estimate. Altogether, our results support a magnitude system in which perceptual evidence for time, space and numbers integrate following Bayesian cue-combination rules.

Suggested Citation

  • Anna Lambrechts & Vincent Walsh & Virginie van Wassenhove, 2013. "Evidence Accumulation in the Magnitude System," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-10, December.
    • Handle: RePEc:plo:pone00:0082122
    DOI: 10.1371/journal.pone.0082122
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0082122
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0082122&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0082122?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. Amir Homayoun Javadi & Clarisse Aichelburg, 2013. "Training Enhances the Interference of Numerosity on Duration Judgement," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.
    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. Amir Homayoun Javadi & Clarisse Aichelburg, 2012. "When Time and Numerosity Interfere: The Longer the More, and the More the Longer," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Nadine Schlichting & Ritske de Jong & Hedderik van Rijn, 2018. "Robustness of individual differences in temporal interference effects," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-19, August.
    2. Andreas Wutz & Anuj Shukla & Raju S Bapi & David Melcher, 2015. "Expansion and Compression of Time Correlate with Information Processing in an Enumeration Task," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-20, August.

    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. Catarina Mendonça & Pietro Mandelli & Ville Pulkki, 2016. "Modeling the Perception of Audiovisual Distance: Bayesian Causal Inference and Other Models," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-18, December.
    2. Jacques Pesnot Lerousseau & Cesare V. Parise & Marc O. Ernst & Virginie Wassenhove, 2022. "Multisensory correlation computations in the human brain identified by a time-resolved encoding model," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. 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.
    4. Marine Hainguerlot & Thibault Gajdos & Jean-Christophe Vergnaud & Vincent de Gardelle, 2023. "How Overconfidence Bias Influences Suboptimality in Perceptual Decision Making," PSE-Ecole d'économie de Paris (Postprint) hal-04197403, HAL.
    5. 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.
    6. 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.
    7. Anthony Renard & Evan R. Harrell & Brice Bathellier, 2022. "Olfactory modulation of barrel cortex activity during active whisking and passive whisker stimulation," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. 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.
    9. 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.
    10. 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.
    11. Xiaochen Zhang & Lingling Jin & Jie Zhao & Jiazhen Li & Ding-Bang Luh & Tiansheng Xia, 2022. "The Influences of Different Sensory Modalities and Cognitive Loads on Walking Navigation: A Preliminary Study," Sustainability, MDPI, vol. 14(24), pages 1-14, December.
    12. Johannes Burge & Priyank Jaini, 2017. "Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-32, February.
    13. Yingjie Lai & Chaemoon Yoo & Xiaomin Zhou & Younghwan Pan, 2023. "Elements of Food Service Design for Low-Carbon Tourism-Based on Dine-In Tourist Behavior and Attitudes in China," Sustainability, MDPI, vol. 15(9), pages 1-21, May.
    14. Florent Meyniel & Maxime Maheu & Stanislas Dehaene, 2016. "Human Inferences about Sequences: A Minimal Transition Probability Model," PLOS Computational Biology, Public Library of Science, vol. 12(12), pages 1-26, December.
    15. 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.
    16. Jean-François Patri & Pascal Perrier & Jean-Luc Schwartz & Julien Diard, 2018. "What drives the perceptual change resulting from speech motor adaptation? Evaluation of hypotheses in a Bayesian modeling framework," PLOS Computational Biology, Public Library of Science, vol. 14(1), pages 1-38, January.
    17. Florent Meyniel, 2020. "Brain dynamics for confidence-weighted learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    18. Carrillo, Juan & Brocas, Isabelle, 2007. "Reason, Emotion and Information Processing in the Brain," CEPR Discussion Papers 6535, C.E.P.R. Discussion Papers.
    19. 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.
    20. Amir Homayoun Javadi & Clarisse Aichelburg, 2013. "Training Enhances the Interference of Numerosity on Duration Judgement," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-8, January.

    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:plo:pone00:0082122. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.