Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Mental labour

Nature Human Behaviour volume 2, pages 899–908 (2018)Cite this article

Subjects

Abstract

Mental effort is an elementary notion in our folk psychology and a familiar fixture in everyday introspective experience. However, as an object of scientific study, mental effort has remained rather elusive. Cognitive psychology has provided some tools for understanding how effort impacts performance, by linking effort with cognitive control function. What has remained less clear are the principles that govern the allocation of mental effort. Under what circumstances do people choose to invest mental effort, and when do they decline to do so? And what regulates the intensity of mental effort when it is applied? In new and promising work, these questions are being approached with the tools of behavioural economics. Though still in its infancy, this economic approach to mental effort research has already uncovered important aspects of effort-based decision-making, and points clearly to future lines of inquiry, including some intriguing opportunities presented by recent artificial intelligence research.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Behavioural paradigms that reveal a cost of cognitive control.
Fig. 2: Labour supply theory and mental effort.
Fig. 3: Evidence for individual differences correlations in effort-based decision-making.
Fig. 4: Transition structures, trial event sequences and results for multi-stage decision-making tasks.
Fig. 5: Schematic of the proposed multi-task learning model.

Similar content being viewed by others

References

  1. Belden, T. G. & Belden, M. R. The Lengthening Shadow: The Life of Thomas J. Watson (Little, Brown, Boston, 1962).

    Google Scholar 

  2. Inzlicht, M., Shenhav, A. & Olivola, C. The effort paradox: effort is both costly and valued. Trends Cogn. Sci. 22, 337–349 (2017).

    Google Scholar 

  3. Hull, C. L. Principles of Behavior (Appleton-Century, New York, NY, 1943).

    Google Scholar 

  4. Kagel, J. H., Battalio, R. C. & Green, L. Economic Choice Theory: An Experimental Analysis of Animal Behavior (Cambridge Univ. Press, Cambridge, 1995).

    Google Scholar 

  5. Allport, G. W. The Nature of Prejudice (Addison Wesley, New York, NY, 1954).

    Google Scholar 

  6. McGuire, W. J. in The Handbook of Social Psychology (eds Lindzey, G. & Aronson, E.) 136–314 (Addision-Wesley, Reading, 1969).

    Google Scholar 

  7. Baroody, A. J. & Ginsburg, H. P. in Conceptual and Procedural Knowledge: The Case of Mathematics (ed. Hiebert, J.) 75–112 (Lawrence Erlbaum Associates, Hillsdale, 1986).

    Google Scholar 

  8. Taylor, S. E. in Cognition, Social Behavior, and the Environment (ed. Harvey, J. H.) 189–211 (Erlbaum, Hillsdale, 1981).

    Google Scholar 

  9. Navon, D. & Gopher, D. On the economy of the human-processing system. Psychol. Rev. 86, 214–255 (1979).

    Google Scholar 

  10. Smith, V. L. & Walker, J. M. Monetary rewards and decision cost in experimental economics. Econ. Inq. 31, 245–261 (1993).

    Google Scholar 

  11. Camerer, C. F. & Hogarth, R. M. The effects of financial incentives in experiments: a review and capital-labor-production framework. J. Risk Uncertainty 19, 7–42 (1999).

    Google Scholar 

  12. Shah, A. K. & Oppenheimer, D. M. Heuristics made easy: an effort-reduction framework. Psychol. Bull. 134, 207–222 (2008).

    PubMed  Google Scholar 

  13. Shugan, S. M. The cost of thinking. J. Consum. Res. 7, 99–111 (1980).

    Google Scholar 

  14. Simon, H. A. A behavioral model of rational choice. Q. J. Econ. 69, 99–118 (1955).

    Google Scholar 

  15. Payne, J. W., Bettman, J. R. & Johnson, E. J. Adaptive strategy selection in decision making. J. Exp. Psychol. 14, 534–552 (1988).

    Google Scholar 

  16. Kool, W., McGuire, J. T., Rosen, Z. & Botvinick, M. M. Decision making and the avoidance of cognitive demand. J. Exp. Psychol. 139, 665–682 (2010).

    Google Scholar 

  17. Westbrook, A., Kester, D. & Braver, T. S. What Is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference. PLoS ONE 8, e68210 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Westbrook, A. & Braver, T. S. Cognitive effort: a neuroeconomic approach. Cogn. Affect. Behav. Neurosci. 15, 395–415 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. Westbrook, J. A. & Braver, T. S. The economics of cognitive effort. Behav. Brain Sci. 36, 704–705 (2013).

    PubMed  Google Scholar 

  20. Culbreth, A., Westbrook, A. & Barch, D. Negative symptoms are associated with an increased subjective cost of cognitive effort. J. Abnorm. Psychol. 125, 528–536 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Chong, T. T.-J. et al. Neurocomputational mechanisms underlying subjective valuation of effort costs. PLoS Biol. 15, e1002598 (2017).

    PubMed  PubMed Central  Google Scholar 

  22. Dunn, T. L., Lutes, D. J. & Risko, E. F. Metacognitive evaluation in the avoidance of demand. J. Exp. Psychol. Hum. Percep. Perform. 42, 1372–1387 (2016).

    Google Scholar 

  23. Kool, W. & Botvinick, M. A Labor/leisure tradeoff in cognitive control. J. Exp. Psychol. Gen. 143, 131–141 (2014).

    PubMed  Google Scholar 

  24. Apps, M. A., Grima, L. L., Manohar, S. & Husain, M. The role of cognitive effort in subjective reward devaluation and risky decision-making. Sci. Rep. 5, 16880 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Manohar, S. G. et al. Reward pays the cost of noise reduction in motor and cognitive control. Curr. Biol. 25, 1707–1716 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. McGuire, J. C. & Botvinick, M. Prefrontal cortex, cognitive control, and the registration of decision costs. Proc. Natl Acad. Sci. USA 107, 7922–7926 (2010).

    CAS  PubMed  Google Scholar 

  27. Posner, M. I. & DiGirolamo, G. J. in The Attentive Brain (ed. Parasuraman, R.) 401–423 (MIT Press, Cambridge, MA, 1998).

    Google Scholar 

  28. Shiffrin, R. M. & Schneider, W. Controlled and automatic information processing: II. Perceptual learning, automatic attending, and a general theory. Psychol. Rev. 84, 127–190 (1977).

    Google Scholar 

  29. Botvinick, M. M. & Cohen, J. D. The computational and neural basis of cognitive control: charted territory and new frontiers. Cogn. Sci. 38, 1249–1285 (2014).

    PubMed  Google Scholar 

  30. Schouppe, N., Ridderinkhof, K. R., Verguts, T. & Notebaert, W. Context-specific control and context selection in conflict tasks. Acta Psychol. 146, 63–66 (2014).

    Google Scholar 

  31. Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Shenhav, A., Cohen, J. D. & Botvinick, M. M. Dorsal anterior cingulate cortex and the value of control. Nature Neurosci. 19, 1286–1291 (2016).

    CAS  PubMed  Google Scholar 

  33. Botvinick, M. & Braver, T. Motivation and cognitive control: from behavior to neural mechanism. Annu. Rev. Psychol. 66, 83–113 (2015).

    PubMed  Google Scholar 

  34. Sayalı, C. & Badre, D. Neural systems of cognitive demand avoidance. Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2018.06.016 (2018).

  35. Kahneman, D. Attention and Effort (Prentice-Hall, Englewood Cliffs, 1973).

    Google Scholar 

  36. Posner, M. I. & Snyder, C. R. R. in Information Processing and Cognition: The Loyola Symposium (ed. Solso, R. L.) 55–85 (Erlbaum Associates, Hillsdale, 1975).

  37. Glimcher, P. W., Camerer, C. F., Fehr, E. & Poldrack, R. A. Neuroeconomics: Decision Making and the Brain (Academic, Oxford, 2009).

    Google Scholar 

  38. Verguts, T., Vassena, E. & Silvetti, M. Adaptive effort investment in cognitive and physical tasks: a neurocomputational model. Front. Behav. Neurosci. 9, 57 (2015).

    PubMed  PubMed Central  Google Scholar 

  39. Musslick, S., Cohen, J. D. & Shenhav, A. Estimating the costs of cognitive control: theoretical validation and potential pitfalls. In Proc. 40th Annu. Meeting Cognitive Science Society (2017).

  40. Nicholson, W. & Snyder, C. M. Microeconomic Theory: Basic Principles and Extension (Cengage Learning, Mason, OH, 2008).

    Google Scholar 

  41. Charness, G. & Kuhn, P. What Can Labor Economists Learn from the Lab? NBER Working Paper No. 15913 (2010).

  42. Dickinson, D. L. An experimental examination of labor supply and work intensities. J. Labor Econ. 17, 638–670 (1999).

    Google Scholar 

  43. Fehr, E., Goette, L. & Zehnder, C. A behavioral account of the labor market: the role of fairness concerns. Annu. Rev. Econ. 1, 355–384 (2009).

    Google Scholar 

  44. Hosking, J. G., Cocker, P. J. & Winstanley, C. A. Prefrontal cortical inactivations decrease willingness to expend cognitive effort on a rodent cost/benefit decision-making task. Cereb. Cortex 26, 1529–1538 (2015).

    PubMed  PubMed Central  Google Scholar 

  45. Cocker, P. J., Hosking, J. G., Benoit, J. & Winstanley, C. A. Sensitivity to cognitive effort mediates psychostimulant effects on a novel rodent cost/benefit decision-making task. Neuropsychopharmacology 37, 1825–1837 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Hosking, J. G., Floresco, S. B. & Winstanley, C. A. Dopamine antagonism decreases willingness to expend physical, but not cognitive, effort: a comparison of two rodent cost/benefit decision-making tasks. Neuropsychopharmacology 40, 1005–1015 (2015).

    CAS  PubMed  Google Scholar 

  47. Sandra, D. A. & Otto, A. R. Cognitive capacity limitations and need for cognition differentially predict reward-induced cognitive effort expenditure. Cognition 172, 101–106 (2018).

    PubMed  Google Scholar 

  48. Chevalier, N. Willing to think hard? The subjective value of cognitive effort in children. Child Dev. 89, 1283–1295 (2018).

    PubMed  Google Scholar 

  49. Culbreth, A. J., Moran, E. K. & Barch, D. M. Effort-based decision-making in schizophrenia. Curr. Opin. Behav. Sci. 22, 1–6 (2018).

    PubMed  Google Scholar 

  50. Eisenberger, R. Learned industriousness. Psychol. Rev. 99, 248–267 (1992).

    CAS  PubMed  Google Scholar 

  51. Olivola, C. Y. & Shafir, E. The martyrdom effect: when pain and effort increase prosocial contributions. J. Behav. Decis. Making 26, 91–105 (2013).

    Google Scholar 

  52. Norton, M. I., Mochon, D. & Ariely, D. The IKEA effect: when labor leads to love. J. Consum. Psychol. 22, 453–460 (2012).

    Google Scholar 

  53. Festinger, L. A Theory of Cognitive Dissonance (Stanford Univ. Press, Stanford, CA, 1962).

    Google Scholar 

  54. Anderson, J. R. The Adaptive Character of Thought (Erlbaum, Hillsdale, 1990).

    Google Scholar 

  55. Shenhav, A. et al. Toward a rational and mechanistic account of mental effort. Ann. Rev. Neurosci. 40, 99–124 (2017).

    CAS  PubMed  Google Scholar 

  56. Lieder, F. & Griffiths, T. L. Strategy selection as rational metareasoning. Psychol. Rev. 124, 762–794 (2017).

    PubMed  Google Scholar 

  57. Kool, W., Gershman, S. J. & Cushman, F. A. Cost-benefit arbitration between multiple reinforcement-learning systems. Psychol. Sci. 28, 1321–1333 (2017).

    PubMed  Google Scholar 

  58. Kahneman, D. Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2003).

    Google Scholar 

  59. Dickinson, A. Actions and habits: the development of behavioural autonomy. Phil. Trans. Roy. Soc. Lon. B 308, 67–78 (1985).

    Google Scholar 

  60. Wason, P. C. & Evans, J. S. B. Dual processes in reasoning? Cognition 3, 141–154 (1975).

    Google Scholar 

  61. Sloman, S. A. The empirical case for two systems of reasoning. Psychol. Bull. 119, 3–22 (1996).

    Google Scholar 

  62. Daw, N. D., Niv, Y. & Dayan, P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat. Neurosci. 8, 1704–1711 (2005).

    CAS  PubMed  Google Scholar 

  63. Dolan, R. J. & Dayan, P. Goals and habits in the brain. Neuron 80, 312–325 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Daw, N., Gershman, S. J., Seymour, B., Dayan, P. & Dolan, R. J. Model-based influences on humans’ choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Otto, A. R., Gershman, S. J., Markman, A. B. & Daw, N. D. The curse of planning: dissecting multiple reinforcement-learning systems by taxing the central executive. Psychol. Sci. 24, 751–761 (2013).

    PubMed  Google Scholar 

  66. Otto, A. R., Skatova, A., Madlon-Kay, S. & Daw, N. D. Cognitive control predicts use of model-based reinforcement learning. J. Cogn. Neurosci. 27, 319–333 (2014).

    Google Scholar 

  67. Smittenaar, P., FitzGerald, T. H., Romei, V., Wright, N. D. & Dolan, R. J. Disruption of dorsolateral prefrontal cortex decreases model-based in favor of model-free control in humans. Neuron 80, 914–919 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Kool, W., Cushman, F. A. & Gershman, S. J. When does model-based control pay off? PLoS Comput. Biol. 12, e1005090 (2016).

    PubMed  PubMed Central  Google Scholar 

  69. Kool, W., Gershman, S. & Cushman, F. A. Planning complexity registers as a cost in metacontrol. J. Cogn. Neurosci. https://doi.org/10.1162/jocn_a_01263 (2018).

    Article  PubMed  Google Scholar 

  70. Botvinick, M. M., Huffstetler, S. & McGuire, J. C. Effort discounting in human nucleus accumbens. Cogn. Affect. Behav. Neurosci. 9, 16–27 (2009).

    PubMed  PubMed Central  Google Scholar 

  71. Schmidt, L., Lebreton, M., Cléry-Melin, M.-L., Daunizeau, J. & Pessiglione, M. Neural mechanisms underlying motivation of mental versus physical effort. PLoS Biol. 10, e1001266 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Frobose, M. I. et al. Catecholaminergic modulation of the avoidance of cognitive control. Preprint at bioRxiv https://doi.org/10.1101/191015 (2017).

  73. Gailliot, M. T. et al. Self-control relies on glucose as a limited energy source: willpower is more than a metaphor. J. Pers. Soc. Psychol. 92, 325–336 (2007).

    PubMed  Google Scholar 

  74. Inzlicht, M., Schmeichel, B. J. & Macrae, C. N. Why self-control seems (but may not be) limited. Trends Cogn. Sci. 18, 127–133 (2014).

    PubMed  Google Scholar 

  75. Randles, D., Harlow, I. & Inzlicht, M. A pre-registered naturalistic observation of within domain mental fatigue and domain-general depletion of self-control. PLoS ONE 12, e0182980 (2017).

    PubMed  PubMed Central  Google Scholar 

  76. Hagger, M. S. et al. A multilab preregistered replication of the ego-depletion effect. Perspect. Psychol. Sci. 11, 546–573 (2016).

    PubMed  Google Scholar 

  77. Braun, D. A. & Arrington, C. M. Assessing the role of reward in task selection using a reward-based voluntary task switching paradigm. Psychol. Res. 82, 54–64 (2017).

    PubMed  Google Scholar 

  78. Blain, B., Hollard, G. & Pessiglione, M. Neural mechanisms underlying the impact of daylong cognitive work on economic decisions. Proc. Natl Acad. Sci. USA 113, 6967–6972 (2016).

    CAS  PubMed  Google Scholar 

  79. Brewer, G. A., Lau, K. K., Wingert, K. M., Ball, B. H. & Blais, C. Examining depletion theories under conditions of within-task transfer. J. Exp. Psychol. 146, 988–1008 (2017).

    Google Scholar 

  80. Inzlicht, M. & Schmeichel, B. J. in The Handbook of Self-Regulation (eds Vohs, K. D. & Baumeister, R. H.) 165–181 (Guilford, New York, NY, 2016).

    Google Scholar 

  81. Kurzban, R. The sense of effort. Curr. Opin. Psychol. 7, 67–70 (2016).

    Google Scholar 

  82. Kurzban, R., Duckworth, A., Kable, J. W. & Myers, J. An opportunity cost model of subjective effort and task performance. Behav. Brain Sci. 36, 661–679 (2013).

    PubMed  Google Scholar 

  83. Otto, A. R. & Daw, N. The opportunity cost of time modulates cognitive effort. Neuropsychologia https://doi.org/10.1016/j.neuropsychologia.2018.05.006 (2017).

  84. Wilson, R. C., Geana, A., White, J. M., Ludvig, E. A. & Cohen, J. D. Humans use directed and random exploration to solve the explore–exploit dilemma. J. Exp. Psychol. 143, 2074–2081 (2014).

    Google Scholar 

  85. Feng, S. F., Schwemmer, M., Gershman, S. J. & Cohen, J. D. Multitasking versus multiplexing: Toward a normative account of limitations in the simultaneous execution of control-demanding behaviors. Cogn. Affect. Behav. Neurosci. 14, 129–146 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Musslick, S. et al. Controlled vs. automatic processing: a graph-theoretic approach to the analysis of serial vs. parallel processing in neural network architectures. In Proc. 38th Annu. Conf. Cognitive Science Society 1547–1552 (2016).

  87. Taatgen, N. A. in Integrated Models of Cognitive Systems (ed. Gray, W. D.) 368–379 (Oxford Univ. Press, New York, NY, 2007).

  88. Yeung, N. & Monsell, S. Switching between tasks of unequal familiarity: the role of stimulus-attribute and response-set selection. J. Exp. Psychol. Hum. Percep. Perform. 29, 455–469 (2003).

    Google Scholar 

  89. Gray, W. D. The nature and processing of errors in interactive behavior. Cogn. Sci. 24, 205–248 (2000).

    Google Scholar 

  90. Kool, W. & Botvinick, M. The intrinsic cost of cognitive control. Behav. Brain Sci. 36, 697–698 (2013).

    PubMed  Google Scholar 

  91. Teh, Y. et al. Distral: Robust multitask reinforcement learning. Adv. Neural Inf. Process. Syst. 30, 4497–4507 (2017).

    Google Scholar 

  92. Genewein, T., Leibfried, F., Grau-Moya, J. & Braun, D. A. Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle. Front. Robot. AI 2, 27 (2015).

    Google Scholar 

  93. Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. Neuroscience-inspired artificial intelligence. Neuron 95, 245–258 (2017).

    CAS  PubMed  Google Scholar 

  94. Miller, E. K. & Cohen, J. D. An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24, 167–202 (2001).

    CAS  PubMed  Google Scholar 

  95. Białaszek, W., Marcowski, P. & Ostaszewski, P. Physical and cognitive effort discounting across different reward magnitudes: tests of discounting models. PLoS ONE 12, e0182353 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, Harvard University, Cambridge, MA, USA

    Wouter Kool

  2. DeepMind, London, UK

    Matthew Botvinick

  3. University College London, London, UK

    Matthew Botvinick

Authors
  1. Wouter Kool
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Botvinick
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matthew Botvinick.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kool, W., Botvinick, M. Mental labour. Nat Hum Behav 2, 899–908 (2018). https://doi.org/10.1038/s41562-018-0401-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-018-0401-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing