Computational Mechanisms of Cognitive Growth
How do we learn how to think and decide? In this project, we aim to
- Reverse-engineer the learning mechanisms through which people acquire and refine their cognitive strategies using process-tracing paradigms and computational modeling.
- Investigate how people learn to learn from their successes and failures (meta reinforcement learning).
- Develop tools and interventions to promote cognitive growth, including
- Software tools for learning from life experiences
- Cognitive training programs for strengthening people's ability to effectively pursue their goals (goal-directedness, self-control, grit, industriousness, and focus), and improve themselves.
Our work on cognitive tutors builds on the theory of metacognitive reinforcement learning developed in this project to leverage artificial intelligence for teaching people optimal cognitive strategies.
Publications:
- Lieder, F., Shenhav, A., Musslick, S., & Griffiths, T.L. (2018). Rational metareasoning and the plasticity of cognitive control. PLoS Computational Biology. DOI: 10.13140/RG.2.2.24500.14721
- Lieder, F., & Griffiths, T. L. (2017). Strategy selection as rational metareasoning. Psychological Review, 124(6), 762-794.http://dx.doi.org/10.1037/rev0000075 [OSF Repository]
- Krueger, P.M.*, Lieder, F.*, & Griffiths, T.L. (2017). Enhancing Metacognitive Reinforcement learning using reward structures and feedback. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.). Proceedings of the 39th Annual Meeting of the Cognitive Science Society. Austin TX: Cognitive Science Society. * These authors contributed equally. [Article]
- Metacognitive reinforcement learning shapes planning and decision-making. Manuscript in preparation with Fred Callaway, Sayan Gul, Paul M. Krueger, Priyam Das, and Tom Griffiths
- Bustamante, L.*, Lieder, F.*, Musslick, S., Shenhav, A., & Cohen, J. (in preparation). Learning to (mis)allocate control: maltransfer can lead to self-control failure. * These authors contributed equally.