One of the most unique and impressive feats of the human mind is its ability to discover and continuouslyrefine its own cognitive strategies. Elucidating the underlying learning and adaptation mechanisms is verydifficult because changes in cognitive strategies are not directly observable. One important domain in whichstrategies and mechanisms are studied is planning. To enable researchers to uncover how people learn howto plan, we offer a tutorial introduction to a recently developed process-tracing paradigm along with a newcomputational method for inferring people’s planning strategies and their changes over time from the resultingprocess-tracing data. Our method allows researchers to reveal experience-driven changes in people’s choice ofindividual planning operations, planning strategies, strategy types, and the relative contributions of differentdecision systems. We validate our method on simulated and empirical data. On simulated data, its inferencesabout the strategies and the relative influence of different decision systems are accurate. When evaluated on human data generated using our process-tracing paradigm, our computational method correctly detects theplasticity-enhancing effect of feedback and the effect of the structure of the environment on people’s planningstrategies. Together, these methods can be used to investigate the mechanisms of cognitive plasticity and toelucidate how people acquire complex cognitive skills such as planning and problem-solving. Importantly, ourmethods can also be used to measure individual differences in cognitive plasticity and examine how differenttypes (pedagogical) interventions affect the acquisition of cognitive skills.

Making good decisions requires thinking ahead, but the huge number of actions and outcomes one could consider makes exhaustive planning infeasible for computationally constrained agents, such as humans. How people are nevertheless able to solve novel problems when their actions have long-reaching consequences is thus a long-standing question in cognitive science. To address this question, we propose a model of resource-constrained planning that allows us to derive optimal planning strategies. We find that previously proposed heuristics such as best-first search are near-optimal under some circumstances, but not others. In a mouse-tracking paradigm, we show that people adapt their planning strategies accordingly, planning in a manner that is broadly consistent with the optimal model but not with any single heuristic model. We also find systematic deviations from the optimal model that might result from additional cognitive constraints that are yet to be uncovered.

Which information is worth considering depends on how much effort it would take to acquire and process it. From this perspective people’s tendency to neglect considering the long-term consequences of their actions (present bias) might reflect that looking further into the future becomes increasingly more effortful. In this work, we introduce and validate the use of Bayesian Inverse Reinforcement Learning (BIRL) for measuring individual differences in the subjective costs of planning. We extend the resource-rational model of human planning introduced by Callaway, Lieder, et al. (2018) by parameterizing the cost of planning. Using BIRL, we show that increased subjective cost for considering future outcomes may be associated with both the present bias and acting without planning. Our results highlight testing the causal effects of the cost of planning on both present bias and mental effort avoidance as a promising direction for future work.

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.

The human mind has an unparalleled ability to acquire complex cognitive skills, discover new strategies, and refine its ways of thinking and decision-making; these phenomena are collectively known as cognitive plasticity. One important manifestation of cognitive plasticity is learning to make better–more far-sighted–decisions via planning. A serious obstacle to studying how people learn how to plan is that cognitive plasticity is even more difficult to observe than cognitive strategies are. To address this problem, we develop a computational microscope for measuring cognitive plasticity and validate it on simulated and empirical data. Our approach employs a process tracing paradigm recording signatures of human planning and how they change over time. We then invert a generative model of the recorded changes to infer the underlying cognitive plasticity. Our computational microscope measures cognitive plasticity significantly more accurately than simpler approaches, and it correctly detected the effect of an external manipulation known to promote cognitive plasticity. We illustrate how computational microscopes can be used to gain new insights into the time course of metacognitive learning and to test theories of cognitive development and hypotheses about the nature of cognitive plasticity. Future work will leverage our computational microscope to reverse-engineer the learning mechanisms enabling people to acquire complex cognitive skills such as planning and problem solving.

Although process data indicates that people often rely on various (often heuristic) strategies to choose between risky options, our models of heuristics cannot predict people's choices very accurately. To address this challenge, it has been proposed that people adaptively choose from a toolbox of simple strategies. But which strategies are contained in this toolbox? And how do people decide when to use which decision strategy? Here, we develop a model according to which each person selects decisions strategies rationally from their personal toolbox; our model allows one to infer which strategies are contained in the cognitive toolbox of an individual decision-maker and specifies when she will use which strategy. Using cross-validation on an empirical data set, we find that this rational model of strategy selection from a personal adaptive toolbox predicts people's choices better than any single strategy (even when it is allowed to vary across participants) and better than previously proposed toolbox models. Our model comparisons show that both inferring the toolbox and rational strategy selection are critical for accurately predicting people's risky choices. Furthermore, our model-based data analysis reveals considerable individual differences in the set of strategies people are equipped with and how they choose among them; these individual differences could partly explain why some people make better choices than others. These findings represent an important step towards a complete formalization of the notion that people select their cognitive strategies from a personal adaptive toolbox.