Type of project: computational modeling, cognitive neuroscience, experimental psychology, big data analysis
We are interested in the neurocognitive mechanisms underlying human self-beliefs, i.e., how people evaluate their own cognitive skills. Although recent studies have started to examine how self-beliefs are formed, little is known about the functional roles of self-beliefs, and in particular, how self-beliefs impact adaptive effort investment. To put it simply, if we do not believe in ourselves, are we not unlikely to try very hard in the first place? We will study whether and how self-beliefs allow us to estimate how much resources we need to invest to achieve a goal. We are also interested in the relationship between self-beliefs, confidence and perceived controllability, that is how much people subjectively believe that their actions will eventually impact action outcomes.
Skills to be taught:
This project will involve the development of mathematical/computational models of learning and decision-making that make key predictions to be tested against empirical data. It will also involve developing new experimental designs, building on existing tasks in cognitive psychology and computational psychiatry. You will have the opportunity to learn advanced statistical tools for behavioral analyses, and for simulating and fitting computational models of behavior.
Main References for the project and suggested reading:
Lee, D. G., & Daunizeau, J. (2021). Trading mental effort for confidence in the metacognitive control of value-based decision-making. Elife, 10, e63282.
Rouault M, Seow T, Gillan CM, Fleming SM. Psychiatric symptom dimensions are associated with dissociable shifts in metacognition but not task performance. Biol Psychiatry. 2018
Rouault, M., Dayan, P., & Fleming, S. M. (2019). Forming global estimates of self-performance from local confidence. Nature communications, 10(1), 1-11.