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2020


A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition
A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition

Amo, V., Lieder, F.

SIG 8 Meets SIG 16, September 2020 (conference) Accepted

Abstract
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.

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A gamified app that helps people overcome self-limiting beliefs by promoting metacognition [BibTex]


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How to navigate everyday distractions: Leveraging optimal feedback to train attention control

Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F.

Annual Meeting of the Cognitive Science Society, July 2020 (conference)

Abstract
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.

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How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]


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Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment

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

CogSci 2020, July 2020, Anirudha Kemtur and Yash Raj Jain contributed equally to this publication. (conference)

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 information and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by model-ing model-misspecification 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.

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Project Page [BibTex]

Project Page [BibTex]


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ACTrain: Ein KI-basiertes Aufmerksamkeitstraining für die Wissensarbeit [ACTrain: An AI-based attention training for knowledge work]

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

66th Spring Conference of the German Ergonomics Society, 2020 (conference)

Abstract
Unser digitales Zeitalter lebt von Informationen und stellt unsere begrenzte Verarbeitungskapazität damit täglich auf die Probe. Gerade in der Wissensarbeit haben ständige Ablenkungen erhebliche Leistungseinbußen zur Folge. Unsere intelligente Anwendung ACTrain setzt genau an dieser Stelle an und verwandelt Computertätigkeiten in eine Trainingshalle für den Geist. Feedback auf Basis maschineller Lernverfahren zeigt anschaulich den Wert auf, sich nicht von einer selbst gewählten Aufgabe ablenken zu lassen. Diese metakognitive Einsicht soll zum Durchhalten motivieren und das zugrunde liegende Fertigkeitsniveau der Aufmerksamkeitskontrolle stärken. In laufenden Feldexperimenten untersuchen wir die Frage, ob das Training mit diesem optimalen Feedback die Aufmerksamkeits- und Selbstkontrollfertigkeiten im Vergleich zu einer Kontrollgruppe ohne Feedback verbessern kann.

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link (url) Project Page [BibTex]

2017


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Optimal gamification can help people procrastinate less

Lieder, F., Griffiths, T. L.

Annual Meeting of the Society for Judgment and Decision Making, Annual Meeting of the Society for Judgment and Decision Making, November 2017 (conference)

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Project Page [BibTex]

2017


Project Page [BibTex]


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An automatic method for discovering rational heuristics for risky choice

Lieder, F., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin, TX: Cognitive Science Society, 2017, Falk Lieder and Paul M. Krueger contributed equally to this publication. (inproceedings)

Abstract
What is the optimal way to make a decision given that your time is limited and your cognitive resources are bounded? To answer this question, we formalized the bounded optimal decision process as the solution to a meta-level Markov decision process whose actions are costly computations. We approximated the optimal solution and evaluated its predictions against human choice behavior in the Mouselab paradigm, which is widely used to study decision strategies. Our computational method rediscovered well-known heuristic strategies and the conditions under which they are used, as well as novel heuristics. A Mouselab experiment confirmed our model’s main predictions. These findings are a proof-of-concept that optimal cognitive strategies can be automatically derived as the rational use of finite time and bounded cognitive resources.

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Project Page [BibTex]

Project Page [BibTex]


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A reward shaping method for promoting metacognitive learning

Lieder, F., Krueger, P. M., Callaway, F., Griffiths, T. L.

In Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision-Making, 2017 (inproceedings)

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Project Page [BibTex]

Project Page [BibTex]


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When does bounded-optimal metareasoning favor few cognitive systems?

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

In AAAI Conference on Artificial Intelligence, 31, 2017 (inproceedings)

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[BibTex]

[BibTex]


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The Structure of Goal Systems Predicts Human Performance

Bourgin, D., Lieder, F., Reichman, D., Talmon, N., Griffiths, T.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Learning to (mis) allocate control: maltransfer can lead to self-control failure

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

In The 3rd Multidisciplinary Conference on Reinforcement Learning and Decision Making. Ann Arbor, Michigan, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Mouselab-MDP: A new paradigm for tracing how people plan

Callaway, F., Lieder, F., Krueger, P. M., Griffiths, T. L.

In The 3rd multidisciplinary conference on reinforcement learning and decision making, 2017 (inproceedings)

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[BibTex]

[BibTex]


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Enhancing metacognitive reinforcement learning using reward structures and feedback

Krueger, P. M., Lieder, F., Griffiths, T. L.

In Proceedings of the 39th Annual Meeting of the Cognitive Science Society, 2017 (inproceedings)

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Project Page Project Page [BibTex]

Project Page Project Page [BibTex]


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Helping people choose subgoals with sparse pseudo rewards

Callaway, F., Lieder, F., Griffiths, T. L.

In Proceedings of the Third Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2017 (inproceedings)

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[BibTex]

[BibTex]

2015


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When to use which heuristic: A rational solution to the strategy selection problem

Lieder, F., Griffiths, T. L.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

Abstract
The human mind appears to be equipped with a toolbox full of cognitive strategies, but how do people decide when to use which strategy? We leverage rational metareasoning to derive a rational solution to this problem and apply it to decision making under uncertainty. The resulting theory reconciles the two poles of the debate about human rationality by proposing that people gradually learn to make rational use of fallible heuristics. We evaluate this theory against empirical data and existing accounts of strategy selection (i.e. SSL and RELACS). Our results suggest that while SSL and RELACS can explain people's ability to adapt to homogeneous environments in which all decision problems are of the same type, rational metareasoning can additionally explain people's ability to adapt to heterogeneous environments and flexibly switch strategies from one decision to the next.

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link (url) Project Page [BibTex]

2015


link (url) Project Page [BibTex]


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Children and Adults Differ in their Strategies for Social Learning

Lieder, F., Sim, Z. L., Hu, J. C., Griffiths, T. L., Xu, F.

In Proceedings of the 37th Annual Conference of the Cognitive Science Society, 2015 (inproceedings)

Abstract
Adults and children rely heavily on other people’s testimony. However, domains of knowledge where there is no consensus on the truth are likely to result in conflicting testimonies. Previous research has demonstrated that in these cases, learners look towards the majority opinion to make decisions. However, it remains unclear how learners evaluate social information, given that considering either the overall valence, or the number of testimonies, or both may lead to different conclusions. We therefore formalized several social learning strategies and compared them to the performance of adults and children. We find that children use different strategies than adults. This suggests that the development of social learning may involve the acquisition of cognitive strategies.

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link (url) [BibTex]

link (url) [BibTex]


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Learning from others: Adult and child strategies in assessing conflicting ratings

Hu, J., Lieder, F., Griffiths, T. L., Xu, F.

In Biennial Meeting of the Society for Research in Child Development, Philadelphia, Pennsylvania, USA, 2015 (inproceedings)

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[BibTex]

[BibTex]


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Utility-weighted sampling in decisions from experience

Lieder, F., Griffiths, T. L., Hsu, M.

In The 2nd Multidisciplinary Conference on Reinforcement Learning and Decision Making, 2015 (inproceedings)

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[BibTex]

[BibTex]

2014


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Algorithm selection by rational metareasoning as a model of human strategy selection

Lieder, F., Plunkett, D., Hamrick, J. B., Russell, S. J., Hay, N. J., Griffiths, T. L.

In Advances in Neural Information Processing Systems 27, 2014 (inproceedings)

Abstract
Selecting the right algorithm is an important problem in computer science, because the algorithm often has to exploit the structure of the input to be efficient. The human mind faces the same challenge. Therefore, solutions to the algorithm selection problem can inspire models of human strategy selection and vice versa. Here, we view the algorithm selection problem as a special case of metareasoning and derive a solution that outperforms existing methods in sorting algorithm selection. We apply our theory to model how people choose between cognitive strategies and test its prediction in a behavioral experiment. We find that people quickly learn to adaptively choose between cognitive strategies. People's choices in our experiment are consistent with our model but inconsistent with previous theories of human strategy selection. Rational metareasoning appears to be a promising framework for reverse-engineering how people choose among cognitive strategies and translating the results into better solutions to the algorithm selection problem.

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Project Page [BibTex]

2014


Project Page [BibTex]


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The high availability of extreme events serves resource-rational decision-making

Lieder, F., Hsu, M., Griffiths, T. L.

In Proceedings of the 36th Annual Conference of the Cognitive Science Society, 2014 (inproceedings)

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[BibTex]

[BibTex]


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Layers of Abstraction: (Neuro)computational models of learning local and global statistical regularities

Diaconescu, A., Lieder, F., Mathys, C., Stephan, K. E.

In 20th Annual Meeting of the Organization for Human Brain Mapping, 2014 (inproceedings)

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[BibTex]

[BibTex]

2013


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Controllability and Resource-Rational Planning

Lieder, F., Goodman, N. D., Huys, Q. J.

In Computational and Systems Neuroscience (Cosyne), pages: 112, 2013 (inproceedings)

Abstract
Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.

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[BibTex]

2013


[BibTex]


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Learned helplessness and generalization

Lieder, F., Goodman, N. D., Huys, Q. J. M.

In 35th Annual Conference of the Cognitive Science Society, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Reverse-Engineering Resource-Efficient Algorithms

Lieder, F., Goodman, N. D., Griffiths, T. L.

In NIPS Workshop Resource-Efficient Machine Learning, 2013 (inproceedings)

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[BibTex]

[BibTex]