Significance Many bad decisions and their devastating consequences could be avoided if people used optimal decision strategies. Here, we introduce a principled computational approach to improving human decision making. The basic idea is to give people feedback on how they reach their decisions. We develop a method that leverages artificial intelligence to generate this feedback in such a way that people quickly discover the best possible decision strategies. Our empirical findings suggest that a principled computational approach leads to improvements in decision-making competence that transfer to more difficult decisions in more complex environments. In the long run, this line of work might lead to apps that teach people clever strategies for decision making, reasoning, goal setting, planning, and goal achievement.
Beyond merely reacting to their environment and impulses, people have the remarkable capacity to proactively set and pursue their own goals. The extent to which they leverage this capacity varies widely across people and situations. The goal of this article is to propose and evaluate a model of proactivity and reactivity. We proceed in three steps. First, we model proactivity in a widely used cognitive control task known as the AX Continuous Performance Task (AX-CPT). Our theory formalizes an important aspect of proactivity as meta-control over proactive and reactive control. Second, we perform a quantitative model comparison to identify the number and nature of meta-control decisions that are involved in the regulation of proactive behavior. Our findings suggest that individual differences in proactivity are governed by two independent meta-control decisions, namely deciding whether to set an intention for what to do in a future situation and deciding whether to recall one’s intentions when the situation occurs. Third, we test the assumptions and qualitative predictions of the winning model against data from numerous experiments varying the incentives, cognitive load, and statistical structure of the task. Our results suggest that proactivity can be understood in terms of computational models of meta-control. Future work will extend our models from proactive control in the AX-CPT to proactive goal creation and goal pursuit in the real world.
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