Precommitment, or taking away a future choice from oneself, is a mechanism for overcoming impulsivity. Here we review recent work suggesting that precommitment can be best explained through a distributed decision-making system with multiple discounting rates. This model makes specific predictions about precommitment behavior and is especially interesting in light of the emerging multiple-systems view of decision-making, in which functional systems with distinct neural substrates use different computational strategies to optimize decisions. Given the growing consensus that impulsivity constitutes a common point of breakdown in decisionmaking processes, with common neural and computational mechanisms across multiple psychiatric disorders, it is useful to translate precommitment into the common language of temporal difference reinforcement learning that unites many of these behavioral and neural data.
- Discounting function
- Temporal diference reinforcement learning