Model-Based Theorizing in Cognitive Neuroscience

Abstract

Weisberg ( [2006] ) and Godfrey-Smith ( [2006] , [2009] ) distinguish between two forms of theorizing: data-driven ‘abstract direct representation’ and modelling. The key difference is that when using a data-driven approach, theories are intended to represent specific phenomena and so directly represent them, while models may not be intended to represent anything and so represent targets indirectly, if at all. The aim here is to compare and analyse these practices, in order to outline an account of model-based theorizing that involves direct representational relationships. This is based on the way that computational templates are now used in cognitive neuroscience, and draws on the dynamic and tentative process of any kind of theory construction, and the idea of partial, purpose-relative representation. 1 Introduction2 Modelling and Abstract Direct Representation 2.1 Abstract direct representation (data-driven) 2.2 Model-based theorizing 2.3 Similarities and differences 2.3.1 Similarities 2.3.2 Differences3 Model-Based Theorizing in Cognitive Neuroscience 3.1 The key ideas: Templates and plausibility 3.2 Steps of theorizing4 What Kind of Representation?5 Theorizing and Robustness6 Conclusion

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