IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004110.html
   My bibliography  Save this article

Embodied Choice: How Action Influences Perceptual Decision Making

Author

Listed:
  • Nathan F Lepora
  • Giovanni Pezzulo

Abstract

Embodied Choice considers action performance as a proper part of the decision making process rather than merely as a means to report the decision. The central statement of embodied choice is the existence of bidirectional influences between action and decisions. This implies that for a decision expressed by an action, the action dynamics and its constraints (e.g. current trajectory and kinematics) influence the decision making process. Here we use a perceptual decision making task to compare three types of model: a serial decision-then-action model, a parallel decision-and-action model, and an embodied choice model where the action feeds back into the decision making. The embodied model incorporates two key mechanisms that together are lacking in the other models: action preparation and commitment. First, action preparation strategies alleviate delays in enacting a choice but also modify decision termination. Second, action dynamics change the prospects and create a commitment effect to the initially preferred choice. Our results show that these two mechanisms make embodied choice models better suited to combine decision and action appropriately to achieve suitably fast and accurate responses, as usually required in ecologically valid situations. Moreover, embodied choice models with these mechanisms give a better account of trajectory tracking experiments during decision making. In conclusion, the embodied choice framework offers a combined theory of decision and action that gives a clear case that embodied phenomena such as the dynamics of actions can have a causal influence on central cognition.Author Summary: The modern view of how we make perceptual decisions is of a process of accumulating sensory evidence until reaching a threshold level of certainty. However, this evidence accumulation model neglects the contribution of action and motor processes to the choice that is made. Recent novel studies that track the changing dynamics of actions during perceptual decisions are increasingly revealing the contribution of the actions we make to our perceptual choices. Thus, the action dynamics of our bodies causally influences our central cognition, which is a core assumption of embodied theories of mind. This paper presents an explanation of how actions, encompassing behavioral strategies such as preparation and commitment, can bias decision making processes in ways that optimize the ecological choices of animals behaving in natural environments. It thus combines two disconnected research streams, decision-making and action control in a manner consistent with theoretical and psychological arguments for embodied cognition.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1004110
    DOI: 10.1371/journal.pcbi.1004110
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004110
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004110&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004110?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. 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.
    2. Diana Burk & James N Ingram & David W Franklin & Michael N Shadlen & Daniel M Wolpert, 2014. "Motor Effort Alters Changes of Mind in Sensorimotor Decision Making," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    3. Arbora Resulaj & Roozbeh Kiani & Daniel M. Wolpert & Michael N. Shadlen, 2009. "Changes of mind in decision-making," Nature, Nature, vol. 461(7261), pages 263-266, September.
    4. 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)

    Citations

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


    Cited by:

    1. Francesco Donnarumma & Domenico Maisto & Giovanni Pezzulo, 2016. "Problem Solving as Probabilistic Inference with Subgoaling: Explaining Human Successes and Pitfalls in the Tower of Hanoi," PLOS Computational Biology, Public Library of Science, vol. 12(4), pages 1-30, April.
    2. Arkady Zgonnikov & Nadim A. A. Atiya & Denis O'Hora & Iñaki Rañò & KongFatt Wong-Lin, 2019. "Beyond reach: Do symmetric changes in motor costs affect decision making? A registered report," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 455-469, July.
    3. Grant Soosalu & Suzanne Henwood & Arun Deo, 2019. "Head, Heart, and Gut in Decision Making: Development of a Multiple Brain Preference Questionnaire," SAGE Open, , vol. 9(1), pages 21582440198, March.
    4. repec:cup:judgdm:v:14:y:2019:i:4:p:455-469 is not listed 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. Shih-Wei Wu & Maria F Dal Martello & Laurence T Maloney, 2009. "Sub-Optimal Allocation of Time in Sequential Movements," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-13, December.
    2. Zohar Z Bronfman & Noam Brezis & Marius Usher, 2016. "Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-21, February.
    3. Leopold Zizlsperger & Thomas Sauvigny & Thomas Haarmeier, 2012. "Selective Attention Increases Choice Certainty in Human Decision Making," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    4. Manuel Rausch & Michael Zehetleitner, 2019. "The folded X-pattern is not necessarily a statistical signature of decision confidence," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-18, October.
    5. Geonhui Lee & Woong Choi & Hanjin Jo & Wookhyun Park & Jaehyo Kim, 2020. "Analysis of motor control strategy for frontal and sagittal planes of circular tracking movements using visual feedback noise from velocity change and depth information," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    6. 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.
    7. 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.
    8. Adrian M Haith & David M Huberdeau & John W Krakauer, 2015. "Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-21, March.
    9. Arkady Zgonnikov & Nadim A. A. Atiya & Denis O'Hora & Iñaki Rañò & KongFatt Wong-Lin, 2019. "Beyond reach: Do symmetric changes in motor costs affect decision making? A registered report," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 14(4), pages 455-469, July.
    10. 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.
    11. Andrea Insabato & Mario Pannunzi & Gustavo Deco, 2017. "Multiple Choice Neurodynamical Model of the Uncertain Option Task," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-29, January.
    12. 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.
    13. Megan K O’Brien & Alaa A Ahmed, 2019. "Asymmetric valuation of gains and losses in effort-based decision making," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-21, October.
    14. Ofri Raviv & Merav Ahissar & Yonatan Loewenstein, 2012. "How Recent History Affects Perception: The Normative Approach and Its Heuristic Approximation," PLOS Computational Biology, Public Library of Science, vol. 8(10), pages 1-10, October.
    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. 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.
    17. Carrillo, Juan & Brocas, Isabelle, 2007. "Reason, Emotion and Information Processing in the Brain," CEPR Discussion Papers 6535, C.E.P.R. Discussion Papers.
    18. 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.
    19. Luigi Acerbi & Sethu Vijayakumar & Daniel M Wolpert, 2014. "On the Origins of Suboptimality in Human Probabilistic Inference," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-23, June.
    20. 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.

    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:pcbi00:1004110. 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: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    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.