Figures and data
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 (2–6 levels) 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 press buttons with their non-dominant pinky finger within 6000 ms. Effort level was visualized as the height of a vertical bar (10%, 30%, 50%, 70%, or 90% of the participant’s calibrated maximum effort). The blank bar indicated no effort. (D) The prosocial decision-making task. Participants chose between a high-effort option (more effort for a larger reward) and a no-effort option (no effort for a smaller reward). ISI = interstimulus interval; ITI = intertrial interval.
Behavioral and rating results of the prosocial effort task. (A) The distribution of the number of button presses. (B–C) Response time data. Participants took longer to press the button for others than for themselves. They also required more time as effort demands increased and potential rewards decreased. (D) Rating data. Participants felt less effortful and more disliking when exerting effort for others than for themselves. Error bars represent the within-subject standard error of the mean.
Grand-average ERP waveforms and topographic maps of the RewP as a function of recipient (self vs. other) and valence (gain vs. non-gain) separately for effort (A) and reward (B) trials. Gray shaded bars represent time windows used for quantification.
ERP results in the prosocial effort task. (A) 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 two continuous predictors of effort and reward, whereas the right graph shows the fixed effects of effort at one standard deviation (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 low. (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 on the P3 as a function of recipient during performance evaluation. Participants exhibited comparable effort effects across self and other trials. Shaded areas depict the 95% confidence intervals.
Behavioral and computational results of the prosocial decision-making task. (A–B) Participants took longer to make decisions as effort level increased in self trials but not in other trials (A). Increased reward magnitude decreased the decision time more pronouncedly in self trials than in other 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). The black circles overlaid on the boxplots indicate the mean across participants. Shaded areas depict the 95% confidence intervals. Noted that seven subjects had an accuracy rate of less than 60% on catch trials, but this did not influence the results of the prosocial decision-making task. Moreover, two subjects were removed from discounting rate analysis due to their negative K values.
Grand-average 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.
Results of a linear mixed-effects model predicting response times in the prosocial effort task
Resuslts of linear regression models predicting rating data of difficulty, effort, and liking
Results of a linear mixed-effects model predicting RewP amplitudes in response to reward feedback in the prosocial effort task
Results of a linear mixed-effects model predicting P3 amplitudes in response to performance feedback in the prosocial effort task
