IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0267983.html
   My bibliography  Save this article

The impact of visually simulated self-motion on predicting object motion–A registered report protocol

Author

Listed:
  • Björn Jörges
  • Laurence R Harris

Abstract

To interact successfully with moving objects in our environment we need to be able to predict their behavior. Predicting the position of a moving object requires an estimate of its velocity. When flow parsing during self-motion is incomplete–that is, when some of the retinal motion created by self-motion is incorrectly attributed to object motion–object velocity estimates become biased. Further, the process of flow parsing should add noise and lead to object velocity judgements being more variable during self-motion. Biases and lowered precision in velocity estimation should then translate to biases and lowered precision in motion extrapolation. We investigate this relationship between self-motion, velocity estimation and motion extrapolation with two tasks performed in a realistic virtual reality (VR) environment: first, participants are shown a ball moving laterally which disappears after a certain time. They then indicate by button press when they think the ball would have hit a target rectangle positioned in the environment. While the ball is visible, participants sometimes experience simultaneous visual lateral self-motion in either the same or in the opposite direction of the ball. The second task is a two-interval forced choice task in which participants judge which of two motions is faster: in one interval they see the same ball they observed in the first task while in the other they see a ball cloud whose speed is controlled by a PEST staircase. While observing the single ball, they are again moved visually either in the same or opposite direction as the ball or they remain static. We expect participants to overestimate the speed of a ball that moves opposite to their simulated self-motion (speed estimation task), which should then lead them to underestimate the time it takes the ball to reach the target rectangle (prediction task). Seeing the ball during visually simulated self-motion should increase variability in both tasks. We expect to find performance in both tasks to be correlated, both in accuracy and precision.

Suggested Citation

  • Björn Jörges & Laurence R Harris, 2023. "The impact of visually simulated self-motion on predicting object motion–A registered report protocol," PLOS ONE, Public Library of Science, vol. 18(1), pages 1-17, January.
    • Handle: RePEc:plo:pone00:0267983
    DOI: 10.1371/journal.pone.0267983
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0267983
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0267983&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0267983?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. G. N. Wilkinson & C. E. Rogers, 1973. "Symbolic Description of Factorial Models for Analysis of Variance," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(3), pages 392-399, November.
    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. Payne, Roger W., 1998. "Design keys, pseudo-factors and general balance," Computational Statistics & Data Analysis, Elsevier, vol. 29(2), pages 217-229, December.
    2. Bent Nielsen, 2014. "Deviance analysis of age-period-cohort models," Economics Papers 2014-W03, Economics Group, Nuffield College, University of Oxford.
    3. Quirin Gehmacher & Juliane Schubert & Fabian Schmidt & Thomas Hartmann & Patrick Reisinger & Sebastian Rösch & Konrad Schwarz & Tzvetan Popov & Maria Chait & Nathan Weisz, 2024. "Eye movements track prioritized auditory features in selective attention to natural speech," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    4. Riehl, Kevin & Kiesel, Florian & Schiereck, Dirk, 2022. "Political and Socioeconomic Factors That Determine the Financial Outcome of Successful Green Innovation," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132099, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    5. repec:jss:jstsof:23:i07 is not listed on IDEAS
    6. Wei Pan & Xianbin Wang & Wenwei Zhou & Bowen Hang & Liwen Guo, 2023. "Linguistic Analysis for Identifying Depression and Subsequent Suicidal Ideation on Weibo: Machine Learning Approaches," IJERPH, MDPI, vol. 20(3), pages 1-12, February.
    7. Ferran Orga & Andrew Mitchell & Marc Freixes & Francesco Aletta & Rosa Ma Alsina-Pagès & Maria Foraster, 2021. "Multilevel Annoyance Modelling of Short Environmental Sound Recordings," Sustainability, MDPI, vol. 13(11), pages 1-13, May.
    8. Christian Kleiber & Achim Zeileis, 2005. "Validating multiple structural change models-a case study," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(5), pages 685-690.
    9. repec:jss:jstsof:34:i01 is not listed on IDEAS
    10. repec:jss:jstsof:14:i09 is not listed on IDEAS
    11. Zeileis, Achim & Kleiber, Christian & Kramer, Walter & Hornik, Kurt, 2003. "Testing and dating of structural changes in practice," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 109-123, October.
    12. Emil Exenberger & Michaela Kav?áková, 2020. "Evaluation of financial health of companies through data envelopment analysis: Selection of variables for the DEA model in R," Proceedings of Economics and Finance Conferences 10913067, International Institute of Social and Economic Sciences.
    13. Großmann, Heiko, 2014. "Automating the analysis of variance of orthogonal designs," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 1-18.
    14. Emi Tanaka, 2020. "Simple outlier detection for a multi‐environmental field trial," Biometrics, The International Biometric Society, vol. 76(4), pages 1374-1382, December.
    15. Rotem Botvinik-Nezer & Bogdan Petre & Marta Ceko & Martin A. Lindquist & Naomi P. Friedman & Tor D. Wager, 2024. "Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    16. Kevser Cetin & Wolfram Mauser, 2023. "The Role of Recent Climate Change in Explaining the Statistical Yield Increase of Maize in Northern Bavaria—A Model Study," Agriculture, MDPI, vol. 13(7), pages 1-19, July.
    17. repec:jss:jstsof:17:i04 is not listed on IDEAS
    18. Jesper Ryden & Sven Erick Alm, 2010. "The effect of interaction and rounding error in two-way ANOVA: example of impact on testing for normality," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(10), pages 1695-1701.
    19. Nitzan Shahar & Tobias U Hauser & Michael Moutoussis & Rani Moran & Mehdi Keramati & NSPN consortium & Raymond J Dolan, 2019. "Improving the reliability of model-based decision-making estimates in the two-stage decision task with reaction-times and drift-diffusion modeling," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-25, February.
    20. Zardetto Diego, 2015. "ReGenesees: an Advanced R System for Calibration, Estimation and Sampling Error Assessment in Complex Sample Surveys," Journal of Official Statistics, Sciendo, vol. 31(2), pages 177-203, June.
    21. repec:plo:pone00:0218661 is not listed on IDEAS
    22. Kevin Riehl & Florian Kiesel & Dirk Schiereck, 2022. "Political and Socioeconomic Factors That Determine the Financial Outcome of Successful Green Innovation," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    23. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
    24. Artur Araujo & Steven Julious & Stephen Senn, 2016. "Understanding Variation in Sets of N-of-1 Trials," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-24, December.

    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:pone00:0267983. 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.