IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/2002885.html
   My bibliography  Save this article

Perceived effort for motor control and decision-making

Author

Listed:
  • Ignasi Cos

Abstract

How effort is internally quantified and how it influences both movement generation and decisions between potential movements are 2 difficult questions to answer. Physical costs are known to influence motor control and decision-making, yet we lack a general, principled characterization of how the perception of effort operates across tasks and conditions. Morel and colleagues introduce an insightful approach to that end, assessing effort indifference points and presenting a quadratic law between perceived effort and force production.

Suggested Citation

  • Ignasi Cos, 2017. "Perceived effort for motor control and decision-making," PLOS Biology, Public Library of Science, vol. 15(8), pages 1-6, August.
    • Handle: RePEc:plo:pbio00:2002885
    DOI: 10.1371/journal.pbio.2002885
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002885
    Download Restriction: no
    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.2002885&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pbio.2002885?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. Kang He & You Liang & Farnaz Abdollahi & Moria Fisher Bittmann & Konrad Kording & Kunlin Wei, 2016. "The Statistical Determinants of the Speed of Motor Learning," PLOS Computational Biology, Public Library of Science, vol. 12(9), pages 1-20, September.
    2. Lionel Rigoux & Emmanuel Guigon, 2012. "A Model of Reward- and Effort-Based Optimal Decision Making and Motor Control," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-13, October.
    3. Konrad P Körding & Izumi Fukunaga & Ian S Howard & James N Ingram & Daniel M Wolpert, 2004. "A Neuroeconomics Approach to Inferring Utility Functions in Sensorimotor Control," PLOS Biology, Public Library of Science, vol. 2(10), pages 1-1, September.
    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. Armando Cocca & Nellie Veulliet & Martin Niedermeier & Clemens Drenowatz & Michaela Cocca & Klaus Greier & Gerhard Ruedl, 2022. "Psychometric Parameters of the Intrinsic Motivation Inventory Adapted to Physical Education in a Sample of Active Adults from Austria," Sustainability, MDPI, vol. 14(20), pages 1-12, October.

    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. Pierre Morel & Philipp Ulbrich & Alexander Gail, 2017. "What makes a reach movement effortful? Physical effort discounting supports common minimization principles in decision making and motor control," PLOS Biology, Public Library of Science, vol. 15(6), pages 1-23, June.
    2. Max Berniker & Megan K O’Brien & Konrad P Kording & Alaa A Ahmed, 2013. "An Examination of the Generalizability of Motor Costs," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-11, January.
    3. Nicoletti, Giuseppe & von Rueden, Christina & Andrews, Dan, 2020. "Digital technology diffusion: A matter of capabilities, incentives or both?," European Economic Review, Elsevier, vol. 128(C).
    4. Bastien Berret & Adrien Conessa & Nicolas Schweighofer & Etienne Burdet, 2021. "Stochastic optimal feedforward-feedback control determines timing and variability of arm movements with or without vision," PLOS Computational Biology, Public Library of Science, vol. 17(6), pages 1-24, June.
    5. Nathan F Lepora & Giovanni Pezzulo, 2015. "Embodied Choice: How Action Influences Perceptual Decision Making," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-22, April.
    6. Joshua G A Cashaback & Christopher K Lao & Dimitrios J Palidis & Susan K Coltman & Heather R McGregor & Paul L Gribble, 2019. "The gradient of the reinforcement landscape influences sensorimotor learning," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-27, March.
    7. Wojciech Białaszek & Przemysław Marcowski & Paweł Ostaszewski, 2017. "Physical and cognitive effort discounting across different reward magnitudes: Tests of discounting models," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-25, July.
    8. Taghizadeh-Hesary, Farhad & Yoshino, Naoyuki & Fukuda, Lisa & Rasoulinezhad, Ehsan, 2021. "A model for calculating optimal credit guarantee fee for small and medium-sized enterprises," Economic Modelling, Elsevier, vol. 95(C), pages 361-373.
    9. Da Silva, Sergio, 2014. "The Mutual Gains from Trade Moderate the Parent-Offspring Conflict - Updated," MPRA Paper 60561, University Library of Munich, Germany.
    10. Xiuli Chen & Kieran Mohr & Joseph M Galea, 2017. "Predicting explorative motor learning using decision-making and motor noise," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    11. Da Silva, Sergio, 2013. "The mutual gains from trade moderate the parent-offspring conflict," MPRA Paper 46627, University Library of Munich, Germany.
    12. Nina M van Mastrigt & Jeroen B J Smeets & Katinka van der Kooij, 2020. "Quantifying exploration in reward-based motor learning," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-14, April.
    13. Daniel Blustein & Ahmed Shehata & Kevin Englehart & Jonathon Sensinger, 2018. "Conventional analysis of trial-by-trial adaptation is biased: Empirical and theoretical support using a Bayesian estimator," PLOS Computational Biology, Public Library of Science, vol. 14(12), pages 1-15, December.
    14. Alizée Lopez-Persem & Lionel Rigoux & Sacha Bourgeois-Gironde & Jean Daunizeau & Mathias Pessiglione, 2017. "Choose, rate or squeeze: Comparison of economic value functions elicited by different behavioral tasks," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-18, November.

    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:pbio00:2002885. 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: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    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.