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bioRxiv · 10.1101/2022.10.28.513278

The neural basis of cost-benefit trade-offs in effort investment: a quantitative activation likelihood estimation meta-analysis

da Silva Castanheira, K.·Spreng, N.·Vassena, E.·Otto, A. R.
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Abstract

Influential theories of cognitive effort-based decision-making suggest that a cost-benefit trade-off guides mental effort allocation and that this trade-off may be reflected in shared neural activity in circuits tracking potential reward and task demand, supporting the idea that. While the dorsal medial prefrontal cortex (mPFC) - and particularly the anterior cingulate cortex (dACC) - has been proposed as a candidate region implementing this computation, it remains unclear whether mPFC/dACC activity tracks rewards and task demand independently or integrates them to reflect effort intensity. Recent accounts posit that the dACC plays a key role in mediating cost-benefit trade-offs. However, empirical evidence remains inconsistent. We conducted a systematic meta-analysis of neuroimaging studies, using the activation-likelihood estimation method to quantify brain activity across 45 studies (N = 1273 participants) investigating choices and task performance in reward-guided cognitive control. We observed significant recruitment of the mPFC/dACC, putamen, and anterior insula for processing larger rewards and higher task demands. The mPFC/dACC clusters sensitive to task demands and rewards were anatomically distinct: caudal mPFC/dACC activity tracked increasing task demands, while rostral mPFC/dACC activity tracked increasing reward. Interestingly, caudal mPFC/dACC activity tracked the integration of reward and task-demand, compatible with cost-benefit trade-off theories of dACC function. These findings provide evidence for distinct signals for mental demand and reward in the mPFC/dACC, which are integrated to support the decision to invest mental effort.

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da Silva Castanheira, K., Spreng, N., Vassena, E., Otto, A. R.. 2022-10-31. The neural basis of cost-benefit trade-offs in effort investment: a quantitative activation likelihood estimation meta-analysis. https://doi.org/10.1101/2022.10.28.513278

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