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Short-sighted decisions can have devastating consequences, and teaching people to make their decisions in a more far-sighted way is challenging. Previous research found that reflecting on one’s behavior can boost learning from success and failure. Here, we explore the potential benefits of guiding people to reflect on whether and how they thought about what to do (i.e., systematic metacognitive reflection). We devised a series of Socratic questions that prompt people to reflect on their decision-making and tested their effectiveness in a process-tracing experiment with a 5-step planning task (
). Each participant went through several cycles of making a series of decisions and then either reflecting on how they made those decisions, answering unrelated questions, or moving on to the next decision right away. We found that systematic metacognitive reflection helps people discover adaptive, far-sighted decision strategies faster. Our results suggest that systematic metacognitive reflection is a promising approach to boosting people’s decision-making competence.
The commentaries raised questions about normativity, human rationality, cognitive architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We respond to these questions and clarify that RRA is a methodological advance that extends the scope of rational modeling to understanding cognitive processes, why they differ between people, why they change over time, and how they could be improved.
Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis. The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.
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