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An experimental manipulation of the value of effort

Nature Human Behaviour (2024)Cite this article

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Abstract

People who take on challenges and persevere longer are more likely to succeed in life. But individuals often avoid exerting effort, and there is limited experimental research investigating whether we can learn to value effort. We developed a paradigm to test the hypothesis that people can learn to value effort and will seek effortful challenges if directly incentivized to do so. We also dissociate the effects of rewarding people for choosing effortful challenges and performing well. The results provide limited evidence that rewarding effort increased people’s willingness to choose harder tasks when rewards were no longer offered (near transfer). There was also mixed evidence that rewarding effort increased willingness to choose harder tasks in another unrelated and unrewarded task (far transfer). These heterogeneous results highlight the need for further research to understand when this paradigm may be the most effective for increasing and generalizing the value of effort.

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Fig. 1: Design.
Fig. 2: Bayesian posterior densities for the effect of condition on effort preferences.
Fig. 3: Effort preference relative to baseline preference in the pre-training section.
Fig. 4: Exploratory analyses and Bayesian posterior densities for the effect of condition on task accuracy.
Fig. 5: Effort preference correlations.

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Data availability

Data and materials are provided at the repository https://osf.io/9unj5Source data are provided with this paper.

Code availability

Code is available at the repository https://osf.io/9unj5

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Acknowledgements

We acknowledge H. Ritz for providing feedback on the task design and N. Lin for designing the task stimuli. This work was supported by a grant from the Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05280) awarded to M.I. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA

    Hause Lin

  2. Department of Psychology, Cornell University, Ithaca, NY, USA

    Hause Lin

  3. Center for Advanced Human Brain Imaging Research, Rutgers University, Piscataway, NJ, USA

    Andrew Westbrook

  4. Department of Psychology, University of Toronto, Toronto, Ontario, Canada

    Frank Fan & Michael Inzlicht

  5. Rotman School of Management, University of Toronto, Toronto, Ontario, Canada

    Michael Inzlicht

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Contributions

H.L., A.W. and M.I. contributed to the conception and design of the work. A.W. and M.I. provided critical oversight of and feedback on the work. H.L. and F.F. programmed the experiment and collected the data. H.L. wrote the manuscript. A.W. and M.I. provided critical feedback.

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Correspondence to Hause Lin.

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Nature Human Behaviour thanks Masud Husain, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Figs. 1–6 and Table 1.

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Supplementary Data 1

Supplementary Fig 1. Source data (Table 1): pilot results. Supplementary Fig. 2. Source data (Table 2): Bayesian posterior estimates for models predicting effort preference and controlling for pre-training effort preference and pre-training task accuracy. Supplementary Fig. 3. Source data (Table 3): exploratory analyses and Bayesian posterior densities for the effect of condition on task reaction time. Supplementary Fig. 4. Source data (Table 4): task accuracy for reaction time quartiles. Supplementary Fig. 5. Source data (Table 5): exploratory analyses and Bayesian posterior densities for the interaction effect between condition and pre-training effort preference. Supplementary Fig. 6. Source data (Table 6): casual forests examining heterogeneous treatment effects.

Source data

Source Data Fig. 1

Data underlying value functions.

Source Data Fig. 2

Bayesian posterior samples.

Source Data Fig. 3

Effort preference difference scores.

Source Data Fig. 4

Bayesian posterior samples.

Source Data Fig. 5

Data for computing correlations.

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Lin, H., Westbrook, A., Fan, F. et al. An experimental manipulation of the value of effort. Nat Hum Behav (2024). https://doi.org/10.1038/s41562-024-01842-7

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  • DOI: https://doi.org/10.1038/s41562-024-01842-7

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