Abstract
The reward after-effect of effort expenditure refers to the phenomenon that previous effort investment changes the subjective value of rewards when obtained. However, the neural mechanisms underlying the after-effects of effort exertion are still not fully understood. We investigated the modulation of reward after-effects by effort type (cognitive vs. physical) through the lens of neural dynamics. Thirty-two participants performed a physically or cognitively demanding task during an effort phase and then played a simple gambling game during a subsequent reward phase to earn monetary rewards while their electroencephalogram (EEG) was recorded. We found that previous effort expenditure decreased electrocortical activity during feedback evaluation. Importantly, this effort effect occurred in a domain-general manner during the early stage (as indexed by the reward positivity) but in a domain-specific manner during the later and more elaborative stage (as indexed by the P3 and delta oscillation) of reward evaluation. Additionally, effort expenditure enhanced P3 sensitivity to feedback valence regardless of effort type. Our findings suggest that cognitive and physical effort, although bearing some surface resemblance to each other, may have dissociable neural influences on the reward after-effects.
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Data are not openly available; however, EEG scripts can be made available upon request. This study was not preregistered.
Notes
A repeated-measures ANOVA with effort type and effort level as within-subjects factors revealed that failed trials were significantly more in the high-effort condition than the low-effort condition, F(1, 31) = 129.00, p < .001, ηp2 = .81, and in the cognitive task than in the physical task, F(1, 31) = 34.36, p < .001, ηp2 = .53. A significant interaction between effort type and effort level, F(1, 31) = 8.60, p = .006, ηp2 = .22, revealed that increased failed trials as a function of effort level was more pronounced in the cognitive task, t(32) = 10.72, p < .001, Cohen’s d = 1.57, than in the physical task, t(32) = 5.39, p < .001, Cohen’s d = 0.90.
Because the two tasks were different and the difficulty was not equated, we standardized the RT data across high and low effort trials within participants for each task and then analyzed the standardized data by using an Effort Type × Effort Level ANOVA. Like the subtraction approach, the interaction between effort type and effort level remained significant, F(1, 31) = 54.90, p < .001, ηp2 = .64. We also analyzed RT data accumulated across successful and failed trials using an Effort Type × Effort Level ANOVA. Results revealed that RTs were significantly slower in the high-effort condition than the low-effort condition, F(1, 31) = 462.46, p < .001, ηp2 = .94. However, neither the main effect of effort type, F(1, 31) = 0.47, p = .498, ηp2 = .01, nor the interaction between effort type and effort level, F(1, 31) = 1.25, p = .271, ηp2 = .04, reached significance. These results suggest that participants invested comparable amounts of time into the cognitive and physical tasks.
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Huiping Jiang: Data curation; formal analysis; investigation; methodology; visualization; writing—original draft. Ya Zheng: Conceptualization; funding acquisition; supervision; writing—review and editing.
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Jiang, H., Zheng, Y. Dissociable neural after-effects of cognitive and physical effort expenditure during reward evaluation. Cogn Affect Behav Neurosci 23, 1500–1512 (2023). https://doi.org/10.3758/s13415-023-01131-2
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DOI: https://doi.org/10.3758/s13415-023-01131-2