Intelligent Systems
Note: This research group has relocated.

28 results (View BibTeX file of all listed publications)

2024

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A mathematical principle for the gamification of behavior change

Lieder, F., Chen, P., Prentice, M., Amo, V., Tošić, M.

JMIR Serious Games , 12, JMIR Publications, March 2024 (article)

Abstract
Many people want to build good habits to become healthier, live longer, or become happier but struggle to change their behavior. Gamification can make behavior change easier by awarding points for the desired behavior and deducting points for its omission.

link (url) DOI [BibTex]

2024

Lieder, F., Chen, P., Prentice, M., Amo, V., Tošić, M. A mathematical principle for the gamification of behavior change JMIR Serious Games , 12, JMIR Publications, March 2024 (article)

link (url) DOI [BibTex]

2023

A Computational Process-Tracing Method for Measuring People’s Planning Strategies and How They Change Over Time
A Computational Process-Tracing Method for Measuring People’s Planning Strategies and How They Change Over Time

Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., He, R., Krueger, P. M., Lieder, F.

Behavior Research Methods, 55, pages: 20377-2079, June 2023 (article)

Abstract
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.

link (url) DOI Project Page [BibTex]

2023

Jain, Y. R., Callaway, F., Griffiths, T. L., Dayan, P., He, R., Krueger, P. M., Lieder, F. A Computational Process-Tracing Method for Measuring People’s Planning Strategies and How They Change Over Time Behavior Research Methods, 55, pages: 20377-2079, June 2023 (article)

link (url) DOI Project Page [BibTex]

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Learning from Consequences Shapes Reliance on Moral Rules vs. Cost-Benefit Reasoning

Maier, M., Cheung, V., Bartos, F., Lieder, F.

April 2023 (article) Submitted

Abstract
Many controversies arise from differences in how people resolve moral dilemmas by following deontological moral rules versus consequentialist cost-benefit reasoning (CBR). This article explores whether and, if so, how these seemingly intractable differences may arise from experience and whether they can be overcome through moral learning. We designed a new experimental paradigm to investigate moral learning from consequences of previous decisions. Our participants (N=387) faced a series of realistic moral dilemmas between two conflicting choices: one prescribed by a moral rule and the other favored by CBR. Critically, we let them observe the consequences of each of their decisions before making the next one. In one condition, CBR-based decisions consistently led to good outcomes, whereas rule-based decisions consistently led to bad outcomes. In the other condition, this contingency was reversed. We observed systematic, experience-dependent changes in people's moral rightness ratings and moral decisions over the course of just 13 decisions. Without being aware of it, participants adjusted how much moral weight they gave to CBR versus moral rules according to which approach produced better consequences in their respective experimental condition. These learning effects transferred to their subsequent responses to the Oxford Utilitarianism Scale, indicating genuine moral learning rather than task-specific effects. Our findings demonstrate the existence of rapid adaptive moral learning from the consequences of previous decisions. Individual differences in morality may thus be more malleable than previously thought.

DOI [BibTex]

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Systematic metacognitive reflection helps people discover far-sighted decision strategies: a process-tracing experiment

Becker, F., Wirzberger, M., Pammer-Schindler, V., Srinivas, S., Lieder, F.

Judgment and Decision Making, March 2023 (article) Accepted

DOI [BibTex]

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Formative assessment of the InsightApp: An ecological momentary intervention that helps people develop (meta-)cognitive skills to cope with stressful situations and difficult emotions

Amo, V., Prentice, M., Lieder, F.

JMIR Formative Research, March 2023 (article) Accepted

Abstract
Ecological Momentary interventions (EMIs) open new and exciting possibilities for conducting research and delivering mental health interventions in real-life environments via smartphones. This makes designing psychotherapeutic EMIs a promising step towards cost-effective, scalable digital solutions for improving mental health and understanding the effects and mechanisms of psychotherapy.

link (url) DOI [BibTex]

Automatic discovery and description of human planning strategies
Automatic discovery and description of human planning strategies

Skirzynski, J., Jain, Y. R., Lieder, F.

Behavior Research Methods, January 2023 (article) Accepted

Abstract
Scientific discovery concerns finding patterns in data and creating insightful hypotheses that explain these patterns. Traditionally, each step of this process required human ingenuity. But the galloping development of computer chips and advances in artificial intelligence (AI) make it increasingly more feasible to automate some parts of scientific discovery. Understanding human planning is one of the fields in which AI has not yet been utilized. State-of-the-art methods for discovering new planning strategies still rely on manual data analysis. Data about the process of human planning is often used to group similar behaviors together. Researchers then use this data to formulate verbal descriptions of the strategies which might underlie those groups of behaviors. In this work we leverage AI to automate these two steps of scientific discovery. We introduce a method for the automatic discovery and description of human planning strategies from process-tracing data collected with the Mouselab-MDP paradigm. Our algorithm, called Human-Interpret, uses imitation learning to describe data gathered in the experiment in terms of a procedural formula and then translates that formula to natural language using a pre-defined predicate dictionary. We test our method on a benchmark data set that researchers have previously scrutinized manually. We find that the descriptions of human planning strategies that we obtain automatically are about as understandable as human-generated descriptions. They also cover a substantial proportion of all types of human planning strategies that had been discovered manually. Our method saves scientists' time and effort as all the reasoning about human planning is done automatically. This might make it feasible to more rapidly scale up the search for yet undiscovered cognitive strategies that people use for planning and decision-making to many new decision environments, populations, tasks, and domains. Given these results, we believe that the presented work may accelerate scientific discovery in psychology, and due to its generality, extend to problems from other fields.

link (url) DOI [BibTex]

2022

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Can we improve self-regulation during computer-based work with optimal feedback?

Wirzberger, M., Lado, A., Prentice, M., Oreshnikov, I., Passy, J., Stock, A., Lieder, F.

Behaviour & Information Technology, November 2022 (article) Submitted

Abstract
Distractions are omnipresent and can derail our attention, which is a precious and very limited resource. To achieve their goals in the face of distractions, people need to regulate their attention, thoughts, and behavior; this is known as self-regulation. How can self-regulation be supported or strengthened in ways that are relevant for everyday work and learning activities? To address this question, we introduce and evaluate a desktop application that helps people stay focused on their work and train self-regulation at the same time. Our application lets the user set a goal for what they want to do during a defined period of focused work at their computer, then gives negative feedback when they get distracted, and positive feedback when they reorient their attention towards their goal. After this so-called focus session, the user receives overall feedback on how well they focused on their goal relative to previous sessions. While existing approaches to attention training often use artificial tasks, our approach transforms real-life challenges into opportunities for building strong attention control skills. Our results indicate that optimal attentional feedback can generate large increases in behavioral focus, task motivation, and self-control – benefitting users to successfully achieve their long-term goals.

link (url) [BibTex]

An interdisciplinary synthesis of research on understanding and promoting well-doing
An interdisciplinary synthesis of research on understanding and promoting well-doing

Lieder, F., Prentice, M., Corwin-Renner, E.

Social and Personality Psychology Compass, 16(9), September 2022 (article)

Abstract
People’s intentional pursuit of prosocial goals and values (i.e., well-doing) is critical to the flourishing of humanity in the long run. Understanding and promoting well-doing is a shared goal across many fields inside and outside of social and personality psychology. Several of these fields are (partially) disconnected from each other and could benefit from more integration of existing knowledge, interdisciplinary collaboration, and cross-fertilization. To foster the transfer and integration of knowledge across these different fields, we provide a brief overview with pointers to some of the key articles in each field, highlight connections, and introduce an integrative model of the psychological mechanisms of well-doing. We identify some gaps in the current understanding of well-doing, such as the paucity of research on well-doing with large and long-lasting positive consequences. Building on this analysis, we identify opportunities for high-impact research on well-doing in social and personality psychology, such as understanding and promoting the effective pursuit of highly impactful altruistic goals.

link (url) DOI [BibTex]

Lieder, F., Prentice, M., Corwin-Renner, E. An interdisciplinary synthesis of research on understanding and promoting well-doing Social and Personality Psychology Compass, 16(9), September 2022 (article)

link (url) DOI [BibTex]

Boosting human decision-making with AI-generated decision aids
Boosting human decision-making with AI-generated decision aids

Becker, F., Skirzyński, J., van Opheusden, B., Lieder, F.

Computational Brain & Behavior, 5(4):467-490, July 2022 (article)

Abstract
Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids is a tedious manual process. Advances in cognitive science might make it possible to automate this process in the future. We recently introduced machine learning methods for discovering optimal strategies for human decision-making automatically and an automatic method for explaining those strategies to people. Decision aids constructed by this method were able to improve human decision-making. However, following the descriptions generated by this method is very tedious. We hypothesized that this problem can be overcome by conveying the automatically discovered decision strategy as a series of natural language instructions for how to reach a decision. Experiment 1 showed that people do indeed understand such procedural instructions more easily than the decision aids generated by our previous method. Encouraged by this finding, we developed an algorithm for translating the output of our previous method into procedural instructions. We applied the improved method to automatically generate decision aids for a naturalistic planning task (i.e., planning a road trip) and a naturalistic decision task (i.e., choosing a mortgage). Experiment 2 showed that these automatically generated decision-aids significantly improved people's performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting has potential for improving human decision-making in the real world.

DOI [BibTex]

Becker, F., Skirzyński, J., van Opheusden, B., Lieder, F. Boosting human decision-making with AI-generated decision aids Computational Brain & Behavior, 5(4):467-490, July 2022 (article)

DOI [BibTex]

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Leveraging machine learning to automatically derive robust decision strategies from imperfect models of the real world

Mehta, A., Jain, Y. R., Kemtur, A., Stojcheski, J., Consul, S., Tosic, M., Lieder, F.

Computational Brain & Behavior, 5, pages: 343-377, Springer Nature, June 2022 (article)

Abstract
Teaching people clever heuristics is a promising approach to improve decision-making under uncertainty. The theory of resource-rationality makes it possible to leverage machine learning to discover optimal heuristics automatically. One bottleneck of this approach is that the resulting decision strategies are only as good as the model of the decision-problem that the machine learning methods were applied to. This is problematic because even domain experts cannot give complete and fully accurate descriptions of the decisions they face. To address this problem, we develop strategy discovery methods that are robust to potential inaccuracies in the description of the scenarios in which people will use the discovered decision strategies. The basic idea is to derive the strategy that will perform best in expectation across all possible real-world problems that could have given rise to the likely erroneous description that a domain expert provided. To achieve this, our method uses a probabilistic model of how the description of a decision problem might be corrupted by biases in human judgment and memory. Our method uses this model to perform Bayesian inference on which real-world scenarios might have given rise to the provided descriptions. We applied our Bayesian approach to robust strategy discovery in two domains: planning and risky choice. In both applications, we find that our approach is more robust to errors in the description of the decision problem and that teaching the strategies it discovers significantly improves human decision-making in scenarios where approaches ignoring the risk that the description might be incorrect are ineffective or even harmful. The methods developed in this article are an important step towards leveraging machine learning to improve human decision-making in the real world because they tackle the problem that the real world is fundamentally uncertain.

Leveraging Machine Learning to Automatically Derive Robust Decision Strategies from Imperfect Knowledge of the Real World link (url) DOI [BibTex]

Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning

Consul, S., Heindrich, L., Stojcheski, J., Lieder, F.

Computational Brain & Behavior, 5, pages: 185-216, Springer, 2022 (article)

Abstract
To make good decisions in the real world people need efficient planning strategies because their computational resources are limited. Knowing which planning strategies would work best for people in different situations would be very useful for understanding and improving human decision-making. But our ability to compute those strategies used to be limited to very small and very simple planning tasks. To overcome this computational bottleneck, we introduce a cognitively-inspired reinforcement learning method that can overcome this limitation by exploiting the hierarchical structure of human behavior. The basic idea is to decompose sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. This hierarchical decomposition enables us to discover optimal strategies for human planning in larger and more complex tasks than was previously possible. The discovered strategies outperform existing planning algorithms and achieve a super-human level of computational efficiency. We demonstrate that teaching people to use those strategies significantly improves their performance in sequential decision-making tasks that require planning up to eight steps ahead. By contrast, none of the previous approaches was able to improve human performance on these problems. These findings suggest that our cognitively-informed approach makes it possible to leverage reinforcement learning to improve human decision-making in complex sequential decision-problems. Future work can leverage our method to develop decision support systems that improve human decision making in the real world.

link (url) DOI Project Page Project Page [BibTex]

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Rational use of cognitive resources in human planning

Callaway, F., Opheusden, B. V., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., Lieder, F.

Nature Human Behaviour, 6, pages: 1112-1125, April 2022 (article)

Abstract
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.

link (url) DOI [BibTex]

Callaway, F., Opheusden, B. V., Gul, S., Das, P., Krueger, P. M., Griffiths, T. L., Lieder, F. Rational use of cognitive resources in human planning Nature Human Behaviour, 6, pages: 1112-1125, April 2022 (article)

link (url) DOI [BibTex]

What to learn next? Aligning gamification rewards to long-term goals using reinforcement learning
What to learn next? Aligning gamification rewards to long-term goals using reinforcement learning

Pauly, R., Heindrich, L., Amo, V., Lieder, F.

March 2022 (article) Accepted

Abstract
Nowadays, more people can access digital educational resources than ever before. However, access alone is often not sufficient for learners to fulfil their learning goals. To support motivation, learning environments are often gamified, meaning that they offer points for interacting with them. But gamification can add to learners’ tendencies to choose learning activities in a short-sighted manner. An example for a short-sighted choice bias is the preference for an easy task offering a quick sense of accomplishment (and in gamified environments often a quick accumulation of points) over a harder task offering to make real progress. The concept of optimal brain points demonstrates that methods from the field of reinforcement learning, specifically reward shaping, allow us to align short-term rewards for learning choices with their expected long-term benefit in a learning context. Building on that work, we here present a scalable approach to supporting self-directed learning in digital learning environments applicable to real-world educational games. It can motivate learners to choose the learning activities that are most beneficial for them in the long run. This is achieved by incentivizing each learning activity in a way that reflects how much progress can be made by completing it and how that progress relates to their learning goal. Specifically, the approach entails modelling how learners choose between learning activities as a Markov Decision Process and applying methods from reinforcement learning to compute which learning choices optimize the learners progress based on their current knowledge. We specify how our developed method can be applied to the English-learning App “Dawn of Civilisation”. We further present the first evaluation of the approach in a controlled online experiment with a simplified learning task, which showed that the derived incentives can significantly improve both learners’ choice behaviour and their learning outcomes.

link (url) [BibTex]

Leveraging artificial intelligence to improve people’s planning strategies
Leveraging artificial intelligence to improve people’s planning strategies

Callaway, F., Jain, Y. R., Opheusden, B. V., Das, P., Iwama, G., Gul, S., Krueger, P. M., Becker, F., Griffiths, T. L., Lieder, F.

119(12), PNAS, March 2022 (article)

Abstract
Human decision making is plagued by systematic errors that can have devastating consequences. Previous research has found that such errors can be partly prevented by teaching people decision strategies that would allow them to make better choices in specific situations. Three bottlenecks of this approach are our limited knowledge of effective decision strategies, the limited transfer of learning beyond the trained task, and the challenge of efficiently teaching good decision strategies to a large number of people. We introduce a general approach to solving these problems that leverages artificial intelligence to discover and teach optimal decision strategies. As a proof of concept, we developed an intelligent tutor that teaches people the automatically discovered optimal heuristic for environments where immediate rewards do not predict long-term outcomes. We found that practice with our intelligent tutor was more effective than conventional approaches to improving human decision making. The benefits of training with our cognitive tutor transferred to a more challenging task and were retained over time. Our general approach to improving human decision making by developing intelligent tutors also proved successful for another environment with a very different reward structure. These findings suggest that leveraging artificial intelligence to discover and teach optimal cognitive strategies is a promising approach to improving human judgment and decision making.

link (url) DOI [BibTex]

Callaway, F., Jain, Y. R., Opheusden, B. V., Das, P., Iwama, G., Gul, S., Krueger, P. M., Becker, F., Griffiths, T. L., Lieder, F. Leveraging artificial intelligence to improve people’s planning strategies 119(12), PNAS, March 2022 (article)

link (url) DOI [BibTex]

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Discovering Rational Heuristics for Risky Choice

Krueger, P., Callaway, F., Gul, S., Griffiths, T., Lieder, F.

PsyArXiv Preprints, January 2022 (article) Submitted

Abstract
For computationally limited agents such as humans, perfectly rational decision-making is almost always out of reach. Instead, people may rely on computationally frugal heuristics that usually yield good outcomes. Although previous research has identified many such heuristics, discovering good heuristics and predicting when they will be used remains challenging. Here, we present a machine learning method that identifies the best heuristics to use in any given situation. To demonstrate the generalizability and accuracy of our method, we compare the strategies it discovers against those used by people across a wide range of multi-alternative risky choice environments in a behavioral experiment that is an order of magnitude larger than any previous experiments of its type. Our method rediscovered known heuristics, identifying them as rational strategies for specific environments, and discovered novel heuristics that had been previously overlooked. Our results show that people adapt their decision strategies to the structure of the environment and generally make good use of their limited cognitive resources, although they tend to collect too little information and their strategy choices do not always fully exploit the structure of the environment.

Discovering Rational Heuristics for Risky Choice link (url) DOI [BibTex]

Krueger, P., Callaway, F., Gul, S., Griffiths, T., Lieder, F. Discovering Rational Heuristics for Risky Choice PsyArXiv Preprints, January 2022 (article) Submitted

Discovering Rational Heuristics for Risky Choice link (url) DOI [BibTex]

2021

A Rational Reinterpretation of Dual Process Theories
A Rational Reinterpretation of Dual Process Theories

Milli, S., Lieder, F., Griffiths, T. L.

Cognition, 217, December 2021 (article)

Abstract
Highly influential "dual-process" accounts of human cognition postulate the coexistence of a slow accurate system with a fast error-prone system. But why would there be just two systems rather than, say, one or 93? Here, we argue that a dual-process architecture might reflect a rational tradeoff between the cognitive flexibility afforded by multiple systems and the time and effort required to choose between them. We investigate what the optimal set and number of cognitive systems would be depending on the structure of the environment. We find that the optimal number of systems depends on the variability of the environment and the difficulty of deciding when which system should be used. Furthermore, we find that there is a plausible range of conditions under which it is optimal to be equipped with a fast system that performs no deliberation (``System 1'') and a slow system that achieves a higher expected accuracy through deliberation (``System 2''). Our findings thereby suggest a rational reinterpretation of dual-process theories.

link (url) DOI [BibTex]

2021

Milli, S., Lieder, F., Griffiths, T. L. A Rational Reinterpretation of Dual Process Theories Cognition, 217, December 2021 (article)

link (url) DOI [BibTex]

Resource-Rational Models of Human Goal Pursuit
Resource-Rational Models of Human Goal Pursuit

Prystawski, B., Mohnert, F., Tošić, M., Lieder, F.

Topics in Cognitive Science, 14(3):528-549 , Online, Wiley Online Library, August 2021 (article)

Abstract
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.

Resource-rational models of human goal pursuit link (url) DOI Project Page [BibTex]

Prystawski, B., Mohnert, F., Tošić, M., Lieder, F. Resource-Rational Models of Human Goal Pursuit Topics in Cognitive Science, 14(3):528-549 , Online, Wiley Online Library, August 2021 (article)

Resource-rational models of human goal pursuit link (url) DOI Project Page [BibTex]

Toward a Formal Theory of Proactivity
Toward a Formal Theory of Proactivity

Lieder, F., Iwama, G.

Cognitive, Affective, & Behavioral Neuroscience, 42, pages: 490-508, Springer, June 2021 (article)

Abstract
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.

Toward a formal theory of proactivity link (url) DOI Project Page [BibTex]

Lieder, F., Iwama, G. Toward a Formal Theory of Proactivity Cognitive, Affective, & Behavioral Neuroscience, 42, pages: 490-508, Springer, June 2021 (article)

Toward a formal theory of proactivity link (url) DOI Project Page [BibTex]

Automatic Discovery of Interpretable Planning Strategies
Automatic Discovery of Interpretable Planning Strategies

Skirzyński, J., Becker, F., Lieder, F.

Machine Learning, 110, pages: 2641-2683, 2021 (article)

Abstract
When making decisions, people often overlook critical information or are overly swayed by irrelevant information. A common approach to mitigate these biases is to provide decisionmakers, especially professionals such as medical doctors, with decision aids, such as decision trees and flowcharts. Designing effective decision aids is a difficult problem. We propose that recently developed reinforcement learning methods for discovering clever heuristics for good decision-making can be partially leveraged to assist human experts in this design process. One of the biggest remaining obstacles to leveraging the aforementioned methods for improving human decision-making is that the policies they learn are opaque to people. To solve this problem, we introduce AI-Interpret: a general method for transforming idiosyncratic policies into simple and interpretable descriptions. Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule. We evaluate our new AI-Interpret algorithm and employ it to translate information-acquisition policies discovered through metalevel reinforcement learning. The results of three large behavioral experiments showed that the provision of decision rules as flowcharts significantly improved people’s planning strategies and decisions across three different classes of sequential decision problems. Furthermore, a series of ablation studies confirmed that our AI-Interpret algorithm was critical to the discovery of interpretable decision rules and that it is ready to be applied to other reinforcement learning problems. We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making.

Automatic Discovery of Interpretable Planning Strategies The code for our algorithm and the experiments is available link (url) Project Page Project Page [BibTex]

Learning to Overexert Cognitive Control in a Stroop Task
Learning to Overexert Cognitive Control in a Stroop Task

Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., Cohen, J.

Cognitive, Affective, & Behavioral Neuroscience, 21, pages: 453-471, January 2021, Laura Bustamante and Falk Lieder contributed equally to this publication. (article)

Abstract
How do people learn when to allocate how much cognitive control to which task? According to the Learned Value of Control (LVOC) model, people learn to predict the value of alternative control allocations from features of a given situation. This suggests that people may generalize the value of control learned in one situation to other situations with shared features, even when the demands for cognitive control are different. This makes the intriguing prediction that what a person learned in one setting could, under some circumstances, cause them to misestimate the need for, and potentially over-exert control in another setting, even if this harms their performance. To test this prediction, we had participants perform a novel variant of the Stroop task in which, on each trial, they could choose to either name the color (more control-demanding) or read the word (more automatic). However only one of these tasks was rewarded, it changed from trial to trial, and could be predicted by one or more of the stimulus features (the color and/or the word). Participants first learned colors that predicted the rewarded task. Then they learned words that predicted the rewarded task. In the third part of the experiment, we tested how these learned feature associations transferred to novel stimuli with some overlapping features. The stimulus-task-reward associations were designed so that for certain combinations of stimuli the transfer of learned feature associations would incorrectly predict that more highly rewarded task would be color naming, which would require the exertion of control, even though the actually rewarded task was word reading and therefore did not require the engagement of control. Our results demonstrated that participants over-exerted control for these stimuli, providing support for the feature-based learning mechanism described by the LVOC model.

Learning to Overexert Cognitive Control in a Stroop Task Learning to Overexert Cognitive Control in a Stroop Tas link (url) DOI [BibTex]

Bustamante, L., Lieder, F., Musslick, S., Shenhav, A., Cohen, J. Learning to Overexert Cognitive Control in a Stroop Task Cognitive, Affective, & Behavioral Neuroscience, 21, pages: 453-471, January 2021, Laura Bustamante and Falk Lieder contributed equally to this publication. (article)

Learning to Overexert Cognitive Control in a Stroop Task Learning to Overexert Cognitive Control in a Stroop Tas link (url) DOI [BibTex]

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Do Behavioral Observations Make People Catch the Goal? A Meta-Analysis on Goal Contagion

Brohmer, H., Eckerstorfer, L. V., van Aert, R. C., Corcoran, K.

International Review of Social Psychology , 34(1):3, Online, January 2021 (article)

Abstract
Goal contagion is a social-cognitive approach to understanding how other people’s behavior influences one’s goal pursuit: An observation of goal-directed behavior leads to an automatic inference and activation of the goal before it can be adopted and pursued thereafter by the observer. We conducted a meta-analysis focusing on experimental studies with a goal condition, depicting goal-directed behavior and a control condition. We searched four databases (PsychInfo, Web of Science, ScienceDirect, and JSTOR) and the citing literature on Google Scholar, and eventually included e = 48 effects from published studies, unpublished studies and registered reports based on 4751 participants. The meta-analytic summary effect was small − g = 0.30, 95%CI [0.21; 0.40], τ² = 0.05, 95%CI [0.03, 0.13] − implying that goal contagion might occur for some people, compared to when this goal is not perceived in behavior. However, the original effect seemed to be biased through the current publication system. As shown by several publication-bias tests, the effect could rather be half the size, for example, selection model: g = 0.15, 95%CI [–0.02; 0.32]. Further, we could not detect any potential moderator (such as the presentation of the manipulation and the contrast of the control condition). We suggest that future research on goal contagion makes use of open science practices to advance research in this domain.

link (url) DOI [BibTex]

Brohmer, H., Eckerstorfer, L. V., van Aert, R. C., Corcoran, K. Do Behavioral Observations Make People Catch the Goal? A Meta-Analysis on Goal Contagion International Review of Social Psychology , 34(1):3, Online, January 2021 (article)

link (url) DOI [BibTex]

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Development and Validation of a Goal Characteristics Questionnaire

Iwama, G., Weber, F., Prentice, M., Lieder, F.

Collabra Psychology, 2021 (article) Submitted

Abstract
How motivated a person is to pursue a goal may depend on many different properties of the goal, such as how specific it is, how important it is to the person, and how actionable it is. Rigorously measuring all of the relevant goal characteristics is still very difficult. Existing measures are scattered across multiple research fields. Some goal characteristics are not yet covered, while others have been measured under ambiguous terminology. Other conceptually related characteristics have yet to be adapted to goals. Last but not least, the validity of most measures of goal characteristics has yet to be assessed. The aim of this study is to: a) integrate, refine, and extend previous measures into a more comprehensive battery of self-report measures, the Goal Characteristics Questionnaire (GCQ; https://osf.io/3gxk5/?view_only=1ff0e62127c64b82862a0fe7d73c4faf), and b) investigate its evidence of validity. In two empirical studies, this paper provides evidence for the validity of the measures regarding their internal structure, measurement invariance, and convergence and divergence with other relevant goal-related measures, such as the motivation, affect, and the dimensions of Personal Project Analysis. The results show that our goal characteristic dimensions have incremental validity for explaining important outcomes, such as goal commitment and well-being. It concludes with practical recommendations for using the GCQ in research on goal-setting and goal-pursuit, and a discussion about directions for future studies.

link (url) DOI [BibTex]

2020

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Improving Human Decision-Making using Metalevel-RL and Bayesian Inference

Kemtur, A., Jain, Y. R., Mehta, A., Callaway, F., Consul, S., Stojcheski, J., Lieder, F.

NeurIPS Workshop on Challenges for Real-World RL, December 2020 (article) Accepted

Abstract
Teaching clever heuristics is a promising approach to improve decision-making. We can leverage machine learning to discover clever strategies automatically. Current methods require an accurate model of the decision problems people face in real life. But most models are misspecified because of limited in-formation and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by modeling model-misspecification using common cognitive biases and handling uncertainty about the real-world according to Bayesian inference. We translate our methods into an intelligent tutor that automatically discovers and teaches robust planning strategies. Our robust cognitive tutor significantly improved human decision-making when the model was so biased that conventional cognitive tutors were no longer effective. These findings highlight that our robust strategy discovery methods are a significant step towards leveraging artificial intelligence to improve human decision-making in the real world.Teaching clever heuristics is a promising approach to improve decision-making. We can leverage machine learning to dis- cover clever strategies automatically. Current methods require an accurate model of the decision problems people face in real life. But most models are misspecified because of limited information and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by modeling model-misspecification using common cognitive biases and handling uncertainty about the real-world according to Bayesian inference. We translate our methods into an intelligent tutor that automatically discovers and teaches robust planning strategies. Our robust cognitive tutor significantly improved human decision-making when the model was so biased that conventional cognitive tutors were no longer effective. These findings highlight that our robust strategy discovery methods are a significant step towards leveraging artificial intelligence to improve human decision-making in the real world.

Improving Human Decision-Making using Metalevel-RL and Bayesian Inference [BibTex]

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Advancing Rational Analysis to the Algorithmic Level

Lieder, F., Griffiths, T. L.

Behavioral and Brain Sciences, 43, Cambridge University Press, March 2020 (article)

Abstract
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.

Advancing rational analysis to the algorithmic level link (url) DOI Project Page [BibTex]

Lieder, F., Griffiths, T. L. Advancing Rational Analysis to the Algorithmic Level Behavioral and Brain Sciences, 43, Cambridge University Press, March 2020 (article)

Advancing rational analysis to the algorithmic level link (url) DOI Project Page [BibTex]

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Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources

Lieder, F., Griffiths, T. L.

Behavioral and Brain Sciences, 43, E1, February 2020 (article)

Abstract
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.

DOI [BibTex]

2019

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Doing More with Less: Meta-Reasoning and Meta-Learning in Humans and Machines

Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., Lieder, F.

Current Opinion in Behavioral Sciences, 29, pages: 24-30, October 2019 (article)

Abstract
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.

DOI Project Page [BibTex]

2019

Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., Lieder, F. Doing More with Less: Meta-Reasoning and Meta-Learning in Humans and Machines Current Opinion in Behavioral Sciences, 29, pages: 24-30, October 2019 (article)

DOI Project Page [BibTex]

Cognitive Prostheses for Goal Achievement
Cognitive Prostheses for Goal Achievement

Lieder, F., Chen, O. X., Krueger, P. M., Griffiths, T. L.

Nature Human Behavior, 3, August 2019 (article)

Abstract
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.

DOI Project Page [BibTex]

Lieder, F., Chen, O. X., Krueger, P. M., Griffiths, T. L. Cognitive Prostheses for Goal Achievement Nature Human Behavior, 3, August 2019 (article)

DOI Project Page [BibTex]