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.

Negative emotions can make maladaptive behavior more likely, especially when people have poor emotion regulation and metacognitive skills (ERMSs). We developed an app to help non-clinical populations train and apply good ERMSs. The app teaches ERMSs with the help of gamified features such as customizable emotion avatars and points for practicing ERMSs. In an initial, brief pre/post test of the app, 60 participants used it to reflect on a difficult emotional challenge and (non-)beneficial ways of acting. Then, they completed a metacognitive skill-building module. After using the app, participants' scores showed significantly decreased/increased perceived likelihood of unwanted/beneficial actions, decreased emotional struggle and emotional intensity, and decreased/increased cognitive endorsement of self-limiting/self-efficacious beliefs (Paired Samples Wilcoxon Test average effect size = 0.71, range = [.26, .87], all p<0.008). These results provide an important proof-of-concept for the app. A subsequent study will test the app's effectiveness for at least two weeks using event-contingent reporting for participants' real-life regulatory challenges and ERMS training in context.

The purpose of the present studies was to identify an effective tool for helping people to select virtuous life goals that promote their own well-being and contribute to the well-being of others (well-doing). Across two studies, we tested four candidate interventions against each other and a control condition. In the first study (N = 218), the intervention conditions were the eulogy and valued living questionnaire exercises from the Acceptance and Commitment Therapy literature. In the second study (N = 537), the intervention conditions were self-affirmation and value self-confrontation from the social psychology literature and the eulogy exercise. The eulogy exercise is a very brief reflection (3-5 minutes) on how one would like to be remembered by friends and family speaking at one’s funeral. The valued living questionnaire exercise involves rating 10 life domains for importance and behavioral consistency with that importance and reflecting on discrepancies. Self-affirmation involves writing about a time when one acted in line with one’s values. And value self-confrontation involves inducing a discrepancy between participants’ values and those of a socially desirable group. Participants were randomly assigned to one of these brief interventions or a control condition. They were then asked to select a life goal that they would like to start pursuing or make more progress on in the near future. In Study 1, selection was open-ended and participants indicated which of 5 life domains it best fit, including interpersonal goals. In Study 2, the goal was selected from a list of prosocial, personal growth, or materialistic life goals. Across both studies, we found that the eulogy exercise stood out as an effective intervention for helping people select life goals that are likely to promote well-being and well-doing, such as wanting to improve other people’s lives, and avoid life goals that are associated with vices, such as wanting to have many expensive possessions. These findings point to the usefulness of humanistic-existential approaches for promoting character development via life goals and provide an example of how philosophically-informed psychological interventions can be effective.

Goal-directed behaviour is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behaviour in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We found that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit. Models of goal pursuit based on the principle of resource rationality captured human behavior better than both a model of optimal goal pursuit and heuristics that are not resource-rational. We conclude that human goal pursuit is jointly shaped by its function, the structure of the environment, and cognitive costs and constraints on human planning and attention. Our findings are an important step toward understanding humans goal pursuit, as cognitive limitations play a crucial role in shaping people's goal-directed behaviour.

Globally 258 million children and youth do not have access to school (Unesco, 2019), while 600 million receive ineffective education (Unesco, 2017). Solve Education! (SE!) is a non-profit organization committed to enable these young people to empower themselves through education, and currently operates in over 7 countries. Their team includes educationists, technologists, and business executives, who work together with governments and local communities to reach young people with disadvantaged backgrounds. Solve Education!’s main mobile application “The Dawn of Civilisation” (DoC), is an open platform that can deliver different learning content, with the focus on English literacy. It is designed to support lower end devices, as well as offline learning. At the Rationality Enhancement Group, we are laying the scientific foundation for helping people do more good in better ways. We combine methods from computational cognitive science, psychology, human-computer interaction, and artificial intelligence for the development of practical tools, strategies, and interventions that support people in their personal growth. In our collaboration with SE!, we aim at learning from and contributing to real-world challenges by applying our research to enhance SE!’s learning platform. We are currently working on two projects. The first project’s goal is to develop a principled approach to incentivize efficient self-directed learning with digital educational resources and to evaluate its effectiveness regarding learners’ behaviors and success in cooperation with SE!. Specifically, SE!’s DoC serves as the digital educational resource and allows to evaluate the approach with very high ecological validity. The planned intervention is based on the concept of optimal brain points developed by Xu, Wirzberger & Lieder (2019). The core idea is to incentivize effort and smart study choices rather than performance and to do so in a way that learners cannot exploit shortcuts to accumulate game points without also moving closer to their actual learning goals. If successful, SE! can build upon the intervention to further enhance the benefits their users draw from DoC. The second project is based on hierarchical goal setting and consists of a digital assistant that helps users set real-world goals and make progress towards them by reaching milestones with DoC. In this talk, in addition to introducing our work together with SE, we will highlight the mutual benefits of the collaboration between scientists and socially impactful organizations.

Beyond merely reacting to their environment and impulses, people have the remarkable capacity to proactively set and pursue their own goals. But the extent to which they leverage this capacity varies widely across people and situations. The goal of this article is to make the mechanisms and variability of proactivity more amenable to rigorous experiments and computational modeling. We proceed in three steps. First, we develop and validate a mathematically precise behavioral measure of proactivity and reactivity that can be applied across a wide range of experimental paradigms. Second, we propose a formal definition of proactivity and reactivity, and develop a computational model of proactivity in the AX Continuous Performance Task (AX-CPT). Third, we develop and test a computational-level theory of meta-control over proactivity in the AX-CPT that identifies three distinct meta-decision-making problems: intention setting, resolving response conflict between intentions and automaticity, and deciding whether to recall context and intentions into working memory. People's response frequencies in the AX-CPT were remarkably well captured by a mixture between the predictions of our models of proactive and reactive control. Empirical data from an experiment varying the incentives and contextual load of an AX-CPT confirmed the predictions of our meta-control model of individual differences in proactivity. Our results suggest that proactivity can be understood in terms of computational models of meta-control. Our model makes additional empirically testable predictions. Future work will extend our models from proactive control in the AX-CPT to proactive goal creation and goal pursuit in the real world.

The translation of abstract, long-term goals, such as “make a contribution to the field of motivation science,” into short-term, actionable intentions is inherently difficult. Hierarchical goal-setting, a goal-setting strategy in which people construct a hierarchy of increasingly more concrete and proximal subgoals is a promising way to support this process. We designed a goal-setting chatbot that helps people craft action hierarchies for achieving their life goals. We conducted a large online field experiment with two follow-up surveys at one week and one month after the intervention to evaluate the effects of a brief hierarchical planning session with our chatbot on goal pursuit. Although there were no main effects of hierarchical planning on goal-related outcomes, exploratory analyses indicated that hierarchical goal-setting enabled people to make more progress towards goals that appeared less actionable. This suggests that supporting hierarchical goal-setting with chatbots is a promising approach to helping people who don’t know how to pursue their goals.

We tested two reflection techniques derived from Acceptance Commitment Therapy for helping people set life goals that are self-determined, communal, and future-minded. Participants were assigned randomly to control, Eulogy, or the Valued Living Questionnaire (VLQ) conditions. Eulogy participants envisioned what they wanted people to say about them at their funeral. In VLQ, participants rated the importance of life domains and how consistent their behavior has recently been with the importance assigned to each domain. Participants then set a life goal, rated it for self-determination, and indicated its time horizon and life domain. Despite only requiring internal reflection, Eulogy was particularly effective for generating self-determined goals that were interpersonal and future-minded. The Eulogy exercise may be a useful and important building block for inspiring the setting and effective pursuit of goals that are simultaneously self-determined, communal, and future-minded. Future research will examine its efficacy in changing experienced well-being and enacted well-doing.

Previous research has shown that approaching learning with a growth mindset is key for maintaining motivation and overcoming setbacks. Mindsets are systems of beliefs that people hold to be true. They influence a person's attitudes, thoughts, and emotions when they learn something new or encounter challenges. In clinical psychology, metareasoning (reflecting on one's mental processes) and meta-awareness (recognizing thoughts as mental events instead of equating them to reality) have proven effective for overcoming maladaptive thinking styles. Hence, they are potentially an effective method for overcoming self-limiting beliefs in other domains as well. However, the potential of integrating assisted metacognition into mindset interventions has not been explored yet. Here, we propose that guiding and training people on how to leverage metareasoning and meta-awareness for overcoming self-limiting beliefs can significantly enhance the effectiveness of mindset interventions. To test this hypothesis, we develop a gamified mobile application that guides and trains people to use metacognitive strategies based on Cognitive Restructuring (CR) and Acceptance Commitment Therapy (ACT) techniques. The application helps users to identify and overcome self-limiting beliefs by working with aversive emotions when they are triggered by fixed mindsets in real-life situations. Our app aims to help people sustain their motivation to learn when they face inner obstacles (e.g. anxiety, frustration, and demotivation). We expect the application to be an effective tool for helping people better understand and develop the metacognitive skills of emotion regulation and self-regulation that are needed to overcome self-limiting beliefs and develop growth mindsets.

What should I work on first? What can wait until later? Which projects should I prioritize and which tasks are not worth my time? These are challenging questions that many people face every day. People’s intuitive strategy is to prioritize their immediate experience over the long-term consequences. This leads to procrastination and the neglect of important long-term projects in favor of seemingly urgent tasks that are less important. Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run. Unfortunately, computing the optimal number of points with standard dynamic programming methods quickly becomes intractable as the number of a person’s projects and the number of tasks required by each project increase. Here, we introduce and evaluate a scalable method for identifying which tasks are most important in the long run and incentivizing each task according to its long-term value. Our method makes it possible to create to-do list gamification apps that can handle the size and complexity of people’s to-do lists in the real world.

To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people's productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p < .01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

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.

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.

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.

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.