Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control

Abstract

We discuss a recent approach to investigating cognitive control, which has the potential to deal with some of the challenges inherent in this endeavour. In a model-based approach, the researcher defines a formal, computational model that performs the task at hand and whose performance matches that of a research participant. The internal variables in such a model might then be taken as proxies for latent variables computed in the brain. We discuss the potential advantages of such an approach for the study of the neural underpinnings of cognitive control and its pitfalls, and we make explicit the assumptions underlying the interpretation of data obtained using this approach.

Keywords:

• Cognitive control
• Model-based approach
• Model
• Reinforcement learning
• Action regulation

Acknowledgments

Rogier B. Mars and Nicholas J. Shea contributed equally to this work. R.B.M. would like to thank Nicolaas Mars for many hours of enlightening and fun discussion. N.J.S. would like to thank Martin Davies and Tim Bayne for helpful discussion of the background to these issues. R.B.M. is supported by a Marie Curie Intra-European Fellowship (EIF) within the 6th European Community Framework Programme and the Medical Research Council UK. N.J.S.'s research is supported by the Oxford University Press (OUP) John Fell Research Fund, the James Martin 21st Century School, the Oxford Centre for Neuroethics, the Wellcome Trust, and the Mary Somerville Junior Research Fellowship, Somerville College.

Notes

¹ Here, we consider the distinction between computation and algorithm heuristically useful, rather than suggesting that one can be drawn precisely in each case.