title: Probabilistic models of cognition: conceptual foundations.
abstract: Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, 'sophisticated' probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore how the approach relates to studies of explicit probabilistic reasoning, and give a brief overview of the field as it stands today.
authors.forename: Nick
authors.initials: N
authors.lastname: Chater
authors.forename: Joshua B
authors.initials: JB
authors.lastname: Tenenbaum
authors.forename: Alan
authors.initials: A
authors.lastname: Yuille
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology; D003071:Cognition / Q000502:physiology; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D011338:Probability Theory