Bayesian Cognitive Science, Unification, and Explanation. (30th September 2015) - Colombo, Matteo; Hartmann, Stephan
ABSTRACT: It is often claimed that the greatest value of the Bayesian framework in cognitive science consists in its unifying power. Several Bayesian cognitive scientists assume that unification is obviously linked to explanatory power. But this link is not obvious, as unification in science is a heterogeneous notion, which may have little to do with explanation. While a crucial feature of most adequate explanations in cognitive science is that they reveal aspects of the causal mechanism that produces the phenomenon to be explained, the kind of unification afforded by the Bayesian framework to cognitive science does not necessarily reveal aspects of a mechanism. Bayesian unification, nonetheless, can place fruitful constraints on causal–mechanical explanation. 1 Introduction 2 What a Great Many Phenomena Bayesian Decision Theory Can Model 3 The Case of Information Integration 4 How Do Bayesian Models Unify? 5 Bayesian Unification: What Constraints Are There on Mechanistic Explanation? 5.1 Unification constrains mechanism discovery 5.2 Unification constrains the identification of relevant mechanistic factors 5.3 Unification constrains confirmation of competitive mechanistic models 6 Conclusion Appendix