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Sports Medicine

, Volume 44, Issue 2, pp 147–158 | Cite as

Application of Decision-Making Theory to the Regulation of Muscular Work Rate during Self-Paced Competitive Endurance Activity

  • Andrew Renfree
  • Louise Martin
  • Dominic Micklewright
  • Alan St Clair Gibson
Review Article

Abstract

Successful participation in competitive endurance activities requires continual regulation of muscular work rate in order to maximise physiological performance capacities, meaning that individuals must make numerous decisions with regards to the muscular work rate selected at any point in time. Decisions relating to the setting of appropriate goals and the overall strategic approach to be utilised are made prior to the commencement of an event, whereas tactical decisions are made during the event itself. This review examines current theories of decision-making in an attempt to explain the manner in which regulation of muscular work is achieved during athletic activity. We describe rational and heuristic theories, and relate these to current models of regulatory processes during self-paced exercise in an attempt to explain observations made in both laboratory and competitive environments. Additionally, we use rational and heuristic theories in an attempt to explain the influence of the presence of direct competitors on the quality of the decisions made during these activities. We hypothesise that although both rational and heuristic models can plausibly explain many observed behaviours in competitive endurance activities, the complexity of the environment in which such activities occur would imply that effective rational decision-making is unlikely. However, at present, many proposed models of the regulatory process share similarities with rational models. We suggest enhanced understanding of the decision-making process during self-paced activities is crucial in order to improve the ability to understand regulation of performance and performance outcomes during athletic activity.

Keywords

Work Rate Exercise Bout Behaviour Alternative Pace Strategy Afferent Feedback 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

No sources of funding were used to assist in the preparation of this review. To the knowledge of the authors, there are no conflicts of interest that are directly or indirectly related to the contents of this manuscript.

References

  1. 1.
    De Koning JJ, Foster C, Bakkum A, et al. Regulation of pacing strategy during athletic competition. PLoS ONE. 2011;6(1):e15863. doi: 10.1371/journal.pone.0015863.PubMedCentralPubMedCrossRefGoogle Scholar
  2. 2.
    Noakes TD. Fatigue is a brain-derived emotion that regulates the exercise behaviour to ensure the protection of whole body homeostasis. Front Physiol. 2012;3(82):1–13.Google Scholar
  3. 3.
    St Clair Gibson A, DeKoning JJ, Thompson KG, et al. Crawling to the finish line—why do endurance athletes collapse? Implications for understanding mechanisms underlying pacing and fatigue. Sports Med. 2013; 43(6):413–24.Google Scholar
  4. 4.
    St Clair Gibson A, Lambert EV, Rauch LHG, et al. The role of information processing between the brain and peripheral physiological systems in pacing and perception of effort. Sports Med. 2006; 36(8):706–22.Google Scholar
  5. 5.
    Renfree A, St Clair Gibson A. Influence of different performance levels on pacing strategy during the female World Championship marathon race. Int J Sports Physiol Perform. 2013; 8(3):279–85.Google Scholar
  6. 6.
    Bach DR, Dolan RJ. Knowing how much you don’t know: a neural organization of uncertainty estimates. Nat Rev Neurosci. 2012;13:573–86.Google Scholar
  7. 7.
    Bar-Eli M. Judgment and decision-making in sport and exercise: Rediscovery and new visions. Psychol Sport Exerc. 2006;7:519–24.CrossRefGoogle Scholar
  8. 8.
    Abbis CR, Laursen PB. Describing and understanding pacing strategies during athletic competition. Sports Med. 2008;38(3):239–52.CrossRefGoogle Scholar
  9. 9.
    Ulmer H-V. Concept of an extracellular regulation of muscular metabolic rate during heavy exercise in humans by psychophysiological feedback. Experientia. 1996;52:416–20.PubMedCrossRefGoogle Scholar
  10. 10.
    St Clair Gibson A, Goedecke JH, Harley YX, et al. Metabolic setpoint control mechanisms in different physiological systems as rest and during exercise. J Theor Biol. 2005;236:60–72.PubMedCrossRefGoogle Scholar
  11. 11.
    Bar-Eli M, Plesner H, Raab M. Judgment, decision-making and success in sport. Chichester: Wiley; 2011.CrossRefGoogle Scholar
  12. 12.
    Baker J, Cote J, Abernethy B. Sport-specific practice and the development of expert decision-making in team ball sports. J Appl Sport Psychol. 2003;15(1):12–25.CrossRefGoogle Scholar
  13. 13.
    Memmert D, Furley P. “I spy with my little eye!”: breadth of attention, inattentional blindness, and tactical decision-making in team sports. J Sport Exerc Psychol. 2007;29(3):365–81.PubMedGoogle Scholar
  14. 14.
    Johnson JG. Cognitive modelling of decision-making in sports. Psychol Sport Exerc. 2006;7:631–52.CrossRefGoogle Scholar
  15. 15.
    Simon HA. A behavioral model of rational choice. Q J Econ. 1955;69(1):99–118.CrossRefGoogle Scholar
  16. 16.
    Simon HA. Rational decision-making in business organizations, Nobel memorial lecture 1978. http://www.nobelprize.org/nobel_prizes/economics/laureates/1978/simon-lecture.pdf.
  17. 17.
    Savage LJ. The foundations of statistics. 2nd ed. New York: Dover; 1954.Google Scholar
  18. 18.
    Miller EK. The prefrontal cortex and cognitive control. Nat Rev Neurosci. 2000;1:59–65.PubMedCrossRefGoogle Scholar
  19. 19.
    Boksem MAS, Tops M. Mental fatigue: costs and benefits. Brain Res Rev. 2008;59:125–39.PubMedCrossRefGoogle Scholar
  20. 20.
    Knill D, Pouget A. The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 2004;27(12):712–9.PubMedCrossRefGoogle Scholar
  21. 21.
    Berger JO. Statistical decision theory and Bayesian analysis. 2nd ed. New York: Springer; 1985.CrossRefGoogle Scholar
  22. 22.
    Trommershauser J, Maloney LT, Landy MS. Statistical decision theory and the trade-offs in the control of motor response. Spat Vis. 2003;16:255–75.PubMedCrossRefGoogle Scholar
  23. 23.
    Morton RH. Deception by manipulating the clock calibration influences cycle ergometer endurance time in males. J Sci Med Sport. 2009;12(2):332–7.PubMedCrossRefGoogle Scholar
  24. 24.
    Thomas G, Renfree A. The effect of secret clock manipulation on 10 km cycle time trial performance. Int J Arts Sci. 2010;3(9):193–202.Google Scholar
  25. 25.
    Gigerenzer G, Gaissmaier W. Heuristic decision-making. Annu Rev Psychol. 2011;62:451–82.PubMedCrossRefGoogle Scholar
  26. 26.
    Shah AK, Oppenheimer DM. Heuristics made easy: an effort-reduction framework. Psychol Bull. 2008;137:207–22.CrossRefGoogle Scholar
  27. 27.
    Gilovich T, Griffin DW, Kahneman D, editors. Heuristics and biases: the psychology of intuitive judgment. New York: Cambridge University Press; 2002.Google Scholar
  28. 28.
    Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science. 1981;211(4481):453–8.PubMedCrossRefGoogle Scholar
  29. 29.
    De Martino B, Kumaran D, Seymour B, et al. Frames, biases, and rational decision-making in the human brain. Science. 2009;313(5787):684–7.CrossRefGoogle Scholar
  30. 30.
    Ariely D, Carmon Z. Gestalt characteristics of experiences: the defining features of summarized events. J Behav Decis Mak. 2000;13:191–201.CrossRefGoogle Scholar
  31. 31.
    Borg GAV. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377–81.PubMedCrossRefGoogle Scholar
  32. 32.
    Tucker R. The anticipatory regulation of performance: the physiological basis for pacing strategies and the development of a perception-based model for exercise performance. Br J Sports Med. 2009;43:392–400.PubMedCrossRefGoogle Scholar
  33. 33.
    Baden DA, Warwick-Evans LA, Lakomy J. Am I nearly there? The effect of anticipated running distance on perceived exertion and attentional focus. J Sport Exercise Psychol. 2004;27:215–31.Google Scholar
  34. 34.
    Hall EE, Ekkekakis P, Petruzzello SJ. Is the relationship of RPE to psychological factors intensity dependent? Med Sci Sports Exerc. 2005;37(8):1365–73.PubMedCrossRefGoogle Scholar
  35. 35.
    Noakes TD. Lore of running. Cape Town: Oxford University Press; 1992.Google Scholar
  36. 36.
    Hutchinson JC, Tenenbaum G. Perceived effort—can it be considered gestalt? Psychol Sport Exerc. 2006;7:463–76.CrossRefGoogle Scholar
  37. 37.
    Watson D. Mood and temperament. New York: The Guilford Press; 2000.Google Scholar
  38. 38.
    Baron B, Moullan F, Deruelle F, et al. The role of emotions on pacing strategies and performance in middle and long duration sport events. Br J Sports Med. 2009;45:511–7.PubMedCrossRefGoogle Scholar
  39. 39.
    Renfree A, West J, Corbett M, et al. Complex interplay between the determinants of pacing and performance during 20 km cycle time trials. Int J Sports Physiol Perform. 2012;7(2):121–9.PubMedGoogle Scholar
  40. 40.
    Isen AM. An influence of positive affect on decision-making in complex situations: Theoretical issues with practical implications. J Consum Psychol. 2001;11(2):75–85.CrossRefGoogle Scholar
  41. 41.
    Slovic P, Peters E, Finucane ML, et al. Affect, risk, and decision-making. Health Psychol. 2005;24(4):S35–40.PubMedCrossRefGoogle Scholar
  42. 42.
    Epstein S. Integration of the cognitive and psychodynamic unconscious. Am Psychologist. 1994;49:709–24.CrossRefGoogle Scholar
  43. 43.
    Epstein S. Intuition from the perspective of cognitive-experiential self-theory. In: Plessner H, Betsch C, Betsch T, editors. Intuition in judgement and decision-making. New York: Taylor and Francis; 2010.Google Scholar
  44. 44.
    Denes-Raj V, Epstein S. Conflict between intuitive and rationale processing: when people behave against their better judgement. J Pers Soc Psychol. 1994;66:819–29.PubMedCrossRefGoogle Scholar
  45. 45.
    Finucane ML, Alhakami A, Slovic P, Johnson SM. The affect heuristic in judgements of risks and benefits. J Behav Decis Mak. 2000;13(1):1–17.CrossRefGoogle Scholar
  46. 46.
    Bannerjee AV. A simple model of herd behaviour. Q J Econ. 1992;107(3):797–817.CrossRefGoogle Scholar
  47. 47.
    Skorski S, Faude O, Rausch K, et al. Reproducibility of pacing profiles in competitive swimmers. Int J Sports Med. 2012;33:1–6.CrossRefGoogle Scholar
  48. 48.
    Appelt KC, Milch KF, Handgraaf MJJ, et al. The decision-making individual differences inventory and guidelines for the study of individual differences in judgement and decision-making research. Judgm Decis Mak. 2011;6(3):252–62.Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Andrew Renfree
    • 1
  • Louise Martin
    • 1
  • Dominic Micklewright
    • 2
  • Alan St Clair Gibson
    • 3
  1. 1.Institute of Sport and Exercise ScienceUniversity of WorcesterWorcesterUK
  2. 2.School of Biological SciencesUniversity of EssexColchesterUK
  3. 3.School of Psychology and Sport SciencesNorthumbria UniversityNewcastle Upon TyneUK

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