Model-based analyses: Promises, pitfalls, and example applications to the study of cognitive control
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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Quarterly Journal of Experimental Psychology 65 (2):252-267 (2012)
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.
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Nicholas Shea (2014). Reward Prediction Error Signals Are Meta‐Representational. Noûs 48 (2):314-341.
John T. Serences Birte U. Forstmann, Eric-Jan Wagenmakers, Tom Eichele, Scott Brown (2011). Reciprocal Relations Between Cognitive Neuroscience and Cognitive Models: Opposites Attract? Trends in Cognitive Sciences 15 (6):272.
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