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Dynamic, cognitive, computational and neural underpinnings of flexible decision-making

Principal Supervisor: Dr Romy Froemer

Secondary Supervisor(s): Dr Damian Cruse

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

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Deadline: 4 January, 2024


Project Outline

Background

Humans are incredibly flexible in their decision-making. Faced with the same information, the same set of options, we can come to select entirely different actions depending on our current goals, what we know about our environment, how certain we are about different aspects of our knowledge or our environment, and situational constraints. While the cognitive and computational mechanisms of simple choice have been widely studied and formalized in successful computational models that in turn guided neuroscientific discoveries, these choices are limited in their scope and the mechanisms thus don’t scale up to the flexible behaviour that humans are able to show in daily life. We have recently begun to incorporate insights from research into cognitive control, metacognition, and planning to better understand how this flexibility can be achieved (Frömer & Shenhav, 2022, Frömer et al., 2019, Frömer et al, 2022).

Objective

A core aim of this project is to extend this work to identify how active information sampling is guided by metacognitive and control mechanisms, how it shapes decision-making dynamics, and to dissect the dynamic neural mechanism that implement these processes (Frömer et al 2021, Frömer et al 2023). The project further aims to identify how core determinants of active information sampling are learned and how learning interacts with decision-making.

Method

The research will involve a range of methods including computational modelling, behavioural experiments, simultaneous EEG and eye-tracking, and potentially fMRI. The research will take place within an international collaborative research network including researchers from Brown University, University of Hamburg, NYU and University of Oxford. Research visits to one of these labs will be possible as part of the PhD. 

References

Frömer, R., & Shenhav, A. (2022). Filling the gaps: Cognitive control as a critical lens for understanding mechanisms of value-based decision-making. Neuroscience & Biobehavioral Reviews, 134, 104483. doi:https://doi.org/10.1016/j.neubiorev.2021.12.006

Frömer, R., Dean Wolf, C. K., & Shenhav, A. (2019). Goal congruency dominates reward value in accounting for behavioral and neural correlates of value-based decision-making. Nature Communications, 10(1), 4926. doi:10.1038/s41467-019-12931-x

Frömer, R., Callaway, F., Griffiths, T., & Shenhav, A. (2022). Considering what we know and what we don't know: Expectations and Confidence guide value integration in value-based decision-making. PsyArXiv. doi:10.31234/osf.io/2sqyt

Frömer, R., Nassar, M. R., Bruckner, R., Stürmer, B., Sommer, W., & Yeung, N. (2021). Response-based outcome predictions and confidence regulate feedback processing and learning. Elife, 10, e62825. doi:10.7554/eLife.62825

Froemer, R., Nassar, M. R., Ehinger, B. V., & Shenhav, A. (2023). Common neural choice signals emerge artifactually amidst multiple distinct value signals. bioRxiv, 2022.2008.2002.502393. doi:10.1101/2022.08.02.502393

Techniques

  • Behavioural experiments
  • Computational modelling of behaviour
  • Eye-tracking
  • EEG
  • Possibly fMRI