Publications

Artificial intelligence systems use an increasing amount of computation and data to solve very specific problems. By contrast, human minds solve a wide range of problems using a fixed amount of computation and limited experience. We identify two abilities that we see as crucial to this kind of general intelligence: meta-reasoning (deciding how to allocate computational resources) and meta-learning (modeling the learning environment to make better use of limited data). We summarize the relevant AI literature and relate the resulting ideas to recent work in psychology.

How does the brain learn how to plan? We reverse-engineer people's underlying learning mechanisms by combining rational process models of cognitive plasticity with recently developed empirical methods that allow us to trace the temporal evolution of people's planning strategies. We find that our Learned Value of Computation model (LVOC) accurately captures people's average learning curve. However, there were also substantial individual differences in metacognitive learning that are best understood in terms of multiple different learning mechanisms-including strategy selection learning. Furthermore, we observed that LVOC could not fully capture people's ability to adaptively decide when to stop planning. We successfully extended the LVOC model to address these discrepancies. Our models broadly capture people's ability to improve their decision mechanisms and represent a significant step towards reverse-engineering how the brain learns increasingly effective cognitive strategies through its interaction with the environment.

Goals are essential to human cognition and behavior. But how do we pursue them? To address this question, we model how capacity limits on planning and attention shape the computational mechanisms of human goal pursuit. We test the predictions of a simple model based on previous theories in a behavioral experiment. The results show that to fully capture how people pursue their goals it is critical to account for people’s limited attention in addition to their limited planning. Our findings elucidate the cognitive constraints that shape human goal pursuit and point to an improved model of human goal pursuit that can reliably predict which goals a person will achieve and which goals they will struggle to pursue effectively.

Procrastination and impulsivity take a significant toll on people’s lives and the economy at large. Both can result from the misalignment of an action's proximal rewards with its long-term value. Therefore, aligning immediate reward with long-term value could be a way to help people overcome motivational barriers and make better decisions. Previous research has shown that game elements, such as points, levels, and badges, can be used to motivate people and nudge their decisions on serious matters. Here, we develop a new approach to decision support that leveragesartificial intelligence and game elements to restructure challenging sequential decision problems in such a way that it becomes easier for people to take the right course of action. A series of four increasingly more realistic experiments suggests that this approach can enable people to make better decisions faster, procrastinate less, complete their work on time, and waste less time on unimportant tasks. These findings suggest that our method is a promising step towards developing cognitive prostheses that help people achieve their goals by enhancing their motivation and decision-making in everyday life.

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.

People's reliance on suboptimal heuristics gives rise to a plethora of cognitive biases in decision-making including the present bias, which denotes people's tendency to be overly swayed by an action's immediate costs/benefits rather than its more important long-term consequences. One approach to helping people overcome such biases is to teach them better decision strategies. But which strategies should we teach them? And how can we teach them effectively? Here, we leverage an automatic method for discovering rational heuristics and insights into how people acquire cognitive skills to develop an intelligent tutor that teaches people how to make better decisions. As a proof of concept, we derive the optimal planning strategy for a simple model of situations where people fall prey to the present bias. Our cognitive tutor teaches people this optimal planning strategy by giving them metacognitive feedback on how they plan in a 3-step sequential decision-making task. Our tutor's feedback is designed to maximally accelerate people's metacognitive reinforcement learning towards the optimal planning strategy. A series of four experiments confirmed that training with the cognitive tutor significantly reduced present bias and improved people's decision-making competency: Experiment 1 demonstrated that the cognitive tutor's feedback can help participants discover far-sighted planning strategies. Experiment 2 found that this training effect transfers to more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor can have additional benefits over being told the strategy in words. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.

Online courses offer much-needed opportunities for lifelong self-directed learning, but people rarely follow through on their noble intentions to complete them. To increase student retention educational software often uses game elements to motivate students to engage in and persist in learning activities. However, gamification only works when it is done properly, and there is currently no principled method that educational software could use to achieve this. We develop a principled feedback mechanism for encouraging good study choices and persistence in self-directed learning environments. Rather than giving performance feedback, our method rewards the learner's efforts with optimal brain points that convey the value of practice. To derive these optimal brain points, we applied the theory of optimal gamification to a mathematical model of skill acquisition. In contrast to hand-designed incentive structures, optimal brain points are constructed in such a way that the incentive system cannot be gamed. Evaluating our method in a behavioral experiment, we find that optimal brain points significantly increased the proportion of participants who instead of exploiting an inefficient skill they already knew-attempted to learn a difficult but more efficient skill, persisted through failure, and succeeded to master the new skill. Our method provides a principled approach to designing incentive structures and feedback mechanisms for educational games and online courses. We are optimistic that optimal brain points will prove useful for increasing student retention and helping people overcome the motivational obstacles that stand in the way of self-directed lifelong learning.

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.

Cognitive biases shape many decisions people come to regret. To help people overcome these biases, Clear-erThinking.org developed a free online tool, called the Decision Advisor (https://programs.clearerthinking.org/decisionmaker.html). The Decision Advisor assists people in big real-life decisions by prompting them to generate more alternatives, guiding them to evaluate their alternatives according to principles of decision analysis, and educates them about pertinent biases while they are making their decision. In a within-subjects experiment, 99 participants reported significantly fewer biases and less regret for a decision supported by the Decision Advisor than for a previous unassisted decision.

Many studies have investigated how goal characteristics affect goal achievement. However, most of them considered only a small number of characteristics and the psychometric properties of their measures remains unclear. To overcome these limitations, we developed and validated a comprehensive questionnaire of goal characteristics with four subscales - measuring the goal’s content, attainability, interestingness, and usefulness respectively. 590 participants completed the questionnaire online. A confirmatory factor analysis supported the four subscales and their structure. The GC questionnaire (https://osf.io/qfhup) can be easily applied to investigate goal setting, pursuit and adjustment in a wide range of contexts.

As people age, their cognitive abilities tend to deteriorate, including their ability to make complex plans. To remediate this cognitive decline, many commercial brain training programs target basic cognitive capacities, such as working memory. We have recently developed an alternative approach: intelligent tutors that teach people cognitive strategies for making the best possible use of their limited cognitive resources. Here, we apply this approach to improve older adults' planning skills. In a process-tracing experiment we found that the decline in planning performance may be partly because older adults use less effective planning strategies. We also found that, with practice, both older and younger adults learned more effective planning strategies from experience. But despite these gains there was still room for improvement-especially for older people. In a second experiment, we let older and younger adults train their planning skills with an intelligent cognitive tutor that teaches optimal planning strategies via metacognitive feedback. We found that practicing planning with this intelligent tutor allowed older adults to catch up to their younger counterparts. These findings suggest that intelligent tutors that teach clever cognitive strategies can help aging decision-makers stay sharp.