title: Probabilistic alternatives to Bayesianism: the case of explanationism.
abstract: There has been a probabilistic turn in contemporary cognitive science. Far and away, most of the work in this vein is Bayesian, at least in name. Coinciding with this development, philosophers have increasingly promoted Bayesianism as the best normative account of how humans ought to reason. In this paper, we make a push for exploring the probabilistic terrain outside of Bayesianism. Non-Bayesian, but still probabilistic, theories provide plausible competitors both to descriptive and normative Bayesian accounts. We argue for this general idea via recent work on explanationist models of updating, which are fundamentally probabilistic but assign a substantial, non-Bayesian role to explanatory considerations.
authors.affiliation: Sciences, Normes, Décision, Paris-Sorbonne University Paris, France.
authors.forename: Igor
authors.initials: I
authors.lastname: Douven
authors.affiliation: Department of Philosophy, University of Utah Salt Lake City, UT, USA.
authors.forename: Jonah N
authors.initials: JN
authors.lastname: Schupbach
keywords: Bayesianism; explanation; inference; probability; updating