预先承诺的理论解释及其神经机制

预先承诺是指个体提前做出一个有长远目标的决定来限制对即时诱惑的选择，从而避免在未来决策中由于意志力缺乏而出现的自我控制失败。最后期限法和限制选择法是开展预先承诺研究的常用范式。本研究在系统回顾预先承诺的三种理论解释（自我控制资源模型、神经网络模型和时间差强化学习模型）的基础上，尝试提出一个整合性的认知神经机制模型来解释预先承诺的作用过程。未来研究可进一步丰富预先承诺的研究范式和应用范围，并探究预先承诺调控非理性决策的心理与神经机制。

Pre-commitment: Theoretical Explanations and Neural Mechanisms

This study reviewed the concept, research paradigms, and theories of pre-commitment, and developed an integrated model to reveal its internal cognitive neural mechanisms. Pre-commitment is a kind of self-control strategy. It aids individuals to make a decision that facilitates long-term goals, thus inhibiting the choice of instant temptation and avoiding self-control failure. The deadline paradigm and the binding choice paradigm are the two research paradigms commonly used to examine pre-commitment. The deadline paradigm, including self-imposed deadlines and evenly-spaced-deadlines, requires participants to set a deadline as a pre-commitment that helps them persist on the goals. The binding choice paradigm helps participants overcome temptation via limiting the options that may elicit impulsiveness, which includes pre-commitment, delaying, and reward feedback phases. Currently there are three theories that account for pre-commitment, namely self-control strength model, neural network model and temporal difference reinforcement learning model. The self-control strength model assumes self-control as limited energy resource. Making a pre-commitment (e.g., goal setting) helps individuals reduce the consumption of self-control when resolving internal conflicts, thereby promoting the accomplishment of long-term goals. The neural network model assumes that pre-commitment helps resolve the conflicts between the higher brain (i.e., frontal lobe) and the lower brain (i.e., limbic system), which further strengthens their cooperation and improves self-control ability. The model consists of two simple feedback-forwarding multilayer artificial neural networks (ANNs), which represents that higher and lower brain systems work together for obtaining benefits. The temporal difference reinforcement learning (TDRL) is a calculation model of pre-commitment, which provides a specific computational assumption to explore the underlying mechanisms of the choice of pre-commitment and explains decision-making under complex circumstances.This study proposed an integrated model to explain the cognitive neural mechanism of pre-commitment. The model assumes that pre-commitment facilitates individuals to choose long-term benefits mainly via activating a number of brain regions including lateral frontopolar cortex (LFPC), dorsolateral prefrontal cortex (DLPFC), ventromedial prefrontal cortex (vmPFC) and posterior parietal cortex (PPC), and through recruiting a number of psychological mechanisms such as self-control, metacognition and value estimation. On the one hand, DLPFC and PPC, which are responsible for willpower, play a significant role in overcoming impulses caused by temptation. On the other hand, LFPC and vmPFC execute the function of metacognition and expected value, which renders individuals to make optimal choice by weighing the values of different plans. The functional connection between DLPFC and vmPFC reflects an interaction between self-control and estimation of value during the pre-committing processes.Future researches can focus on the advanced cognitive functions in the process of pre-commitment (e.g., category learning, working memory, creative reasoning, problem solving and attention transferring). In addition, it is necessary to explore more research paradigms and enrich the neural mechanisms of pre-commitment on regulating irrational decision-making. Finally, pre-commitment strategies may be applied to prevent adolescent negative social behaviors (e.g., internet addiction, aggression, dangerous driving, drug addiction, and antisocial behavior).