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Transcranial stimulation and decision-making

Decision making (DM) is essential for everyday functioning. Noninvasive brain stimulation (NIBS) methods such as transcranial magnetic and electrical stimulation have been used extensively to understand the behavioral and neural correlates of choice arbitration. However, the considerable inconsistency across findings renders it difficult to draw firm conclusions about the magnitude and robustness of their effects. This variability is likely due to differences in stimulation protocols across studies, but also to statistical fluctuations arising from insufficient power. In this chapter, we make a distinction between perceptual and value-based DM, and evaluate NIBS studies through the lens of computational modeling. We argue for the importance of recovering latent DM parameters via formal mathematical models, and describe how this approach can elucidate NIBS mechanisms on phenomena such as speed-accuracy trade-off and reinforcement learning. Finally, we provide recommendations for future studies to improve the reliability and replicability of NIBS findings.