Effort produces after-effects costly for others but valued for self
- Department of Psychology, Guangzhou University, China
- Center for Reward and Social Cognition, School of Education, Guangzhou University, China
- Guangdong Provincial Key Laboratory of Social Cognitive Neuroscience and Mental Health and Department of Psychology, Sun Yat-sen University, China
- Department of Psychology, Dalian Medical University, China
Figures
Figure 1
Experimental tasks.
(A) The role assignment task. Participants were introduced to another anonymous person and designated as a decider to invest physical effort for monetary rewards for themselves and others. (B) The prosocial effort task. Participants exerted physical effort (five levels: 10%, 30%, 50%, 70%, or 90% of the participant’s calibrated maximum effort, shown as bars 2–6 in panel C) to earn a potential reward of varying amounts (¥0.2, ¥0.4, ¥0.6, ¥0.8, or ¥1.0) for themselves and others. Successful effort had a 50% chance of yielding a reward. (C) Effort levels. The physical task required participants to rapidly press a button with their nondominant pinky finger within 6000 ms. Each effort level was visualized as the height of a vertical bar. The leftmost blank bar (bar 1) indicated no effort and was used only in the baseline option of the prosocial decision-making task. (D) The prosocial decision-making task. Participants chose between a high-effort option (more effort for a larger reward) and a baseline option (no effort for a smaller reward). ISI = interstimulus interval; ITI = intertrial interval.
Figure 2
Behavioral and rating results of the prosocial effort task.
(A) Distribution of the maximum effort level (i.e. the average button-press count across three 6000 ms trials) across participants. (B–C) Response speed (button presses per second) data. Participants responded faster for themselves than for others. Response speed also increased as effort demands increased. (D) Rating data. Participants felt less effort and more disliking when exerting effort for others than for themselves. Error bars represent the within-subject standard error of the mean (n = 40).
Figure 3 with 1 supplement
Grand-average event-related potential (ERP) waveforms and topographic maps of the reward positivity (RewP) as a function of recipient (self vs. other) and valence (gain vs. nongain) separately for effort (A) and reward (B) trials.
Gray shaded bars represent time windows used for quantification.
Figure 3—figure supplement 1
Grand-average event-related potential (ERP) waveforms and topographic maps of the P3 as a function of recipient (self vs. other) separately for effort (A) and reward (B) trials.
Gray shaded bars represent time windows used for quantification.
Figure 4
Reward positivity (RewP) results in the prosocial effort task.
(A) Estimated beta weights for the mixed-effects model predicting RewP amplitudes. The RewP model was specified as: Amplitude ~ Recipient * Effort * Magnitude * Valence + (Recipient + Effort + Magnitude | Participant). (B) Fixed effects of reward magnitude on the RewP as a function of recipient and valence during reward evaluation, showing a significant three-way interaction. (C) Fixed effects of effort and reward on the RewP as a function of recipient during reward evaluation. The left graph displays the fixed effects with effort and reward as continuous predictors, whereas the right graph shows fixed effects of effort at 1 SD below and above the mean reward magnitude. An effort-enhancement effect emerged when participants invested effort for themselves, whereas an effort-discounting effect occurred when they exerted effort for others. This dissociable after-effect was present only when reward magnitude was high. Error bars and shaded areas depict 95% confidence intervals (n = 40). *p<0.05, **p<0.01, ***p<0.001.
Figure 5
Behavioral and discounting results of the prosocial decision-making task.
(A–B) Participants took longer to make decisions as effort level increased in self-benefiting trials but not in other-benefiting trials (A). Increased reward magnitude decreased the decision time more pronouncedly in self-benefiting trials than in other-benefiting trials (B). (C–D) Participants were less willing to invest effort for others than for themselves. (E) Effort exertion discounted rewards to a higher degree when the beneficiary was others compared to when it was themselves (left and middle). A higher discounting rate for others was associated with a higher discounting rate for self (right). Data were analyzed using linear mixed-effects models for panels A–B, mixed-effects logistic models for panels C–D, and Pearson correlation for panel E. For panels A–D, the left panels display the raw data overlaid with boxplots, whereas the right panels show the model-predicted fixed effects. The black circles overlaid on the boxplots indicate the mean across participants. Shaded areas depict 95% confidence intervals. The sample size is n = 40, except for the K analyses in E (middle and right) where n = 38 because two participants had negative original K values and were thus excluded. Note that seven participants had an accuracy rate of less than 60% on catch trials, but this did not influence the results of the prosocial decision-making task.
Figure 6
Cross-task modulation results.
Individual differences in effort discounting (log-transformed K) estimated from the prosocial decision-making task modulated the neural after-effects of effort exertion in the prosocial effort task. For self-benefiting trials, high-discounting individuals exhibited an effort-enhancement effect on the reward positivity (RewP) specifically at low reward magnitude; conversely, for other-benefiting trials, low-discounting individuals exhibited an effort-discounting effect only at high reward magnitude. These plots decompose the significant four-way interaction (recipient × effort × magnitude × K value) derived from the linear mixed-effects model. Fixed effects are visualized at ±1 SD from the mean. Shaded areas depict 95% confidence intervals (n = 40). Note that two participants had negative original K values.
Additional files
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Supplementary file 1
Results of mixed-effects models predicting response success (logistic; left) and response speed (linear; right) in the prosocial effort task.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp1-v1.docx
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Supplementary file 2
Results of linear regression models predicting rating data of difficulty, effort, and liking.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp2-v1.docx
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Supplementary file 3
Results of reward positivity (RewP) models with response speed (left) and effort rating (right) as covariates.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp3-v1.docx
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Supplementary file 4
Results of a linear mixed-effects model predicting P3 amplitudes in response to performance feedback in the prosocial effort task.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp4-v1.docx
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Supplementary file 5
Results of a linear mixed-effects model predicting decision times in the prosocial decision-making task.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp5-v1.docx
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Supplementary file 6
Results of a mixed-effects logistic regression model predicting decision choices in the prosocial decision-making task.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp6-v1.docx
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Supplementary file 7
Results of reward positivity (RewP) models with discounting rate (logK) and high-effort choice proportions as fixed predictors.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp7-v1.docx
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Supplementary file 8
Simulation-based sensitivity analysis for fixed effects in the reward positivity (RewP) model.
- https://cdn.elifesciences.org/articles/103566/elife-103566-supp8-v1.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/103566/elife-103566-mdarchecklist1-v1.pdf
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Effort produces after-effects costly for others but valued for self
eLife 13:RP103566.
https://doi.org/10.7554/eLife.103566.4
