1 IntroductionCausality has had a tumultuous history, taking on many definitions: from Aristotle, who held that four categories of causes existed in nature; to David Hume, who asserted it to be outside the realms of probabilistic reasoning and science [Michotte & Miles, 1963], and Bertrand Russell, who considered causality a psychological construct [Russel, 1913]; to modern atemporal causal calculus, developed by Pearl . Thus, finally falling in place as the relation underpinning statistical science and, what on the surface seemed to be, probabilistic cognition Pearl ; now: causal cognition.
2 CausalityCausality plays a central role in every scientific discipline: from the social sciences to biological and health sciences; from formal logic to legal reasoning, and politico-economic sciences; and from physics to cognitive science.
3 Causal Bayesian NetworksCBNs are a mathematical construct, a graphical probabilistic model. They consist of a directed acyclic graph (aptly chosen since they respect flow of time and causality) in which the vertices are random variables (representing causes and effects, since every effect can be the cause of another effect) and the edges are causal relationships (defined over the variables). Each of the variables' vertices, the causal links, posses a probabilistic connection strength, due to noise and incomplete data [Pearl, 2000,Lagnado & Sloman, 2002].
4 Observing, Intervening and Counterfactually ReasoningCBNs, like many modelling strategies, are directly inspired by the cognitive system; by explicitly representing conditional independence, provided it is a reliable mental construct, the CBN should be consistent with the world as it is humanly perceived [Pearl, 2000].
5 Causal CognitionEquivalences between CBNs' and the brain's causality construal certainly exist, either due to the fact CBNs are inspired by the cognitive system or due to properties that emerge from those idealised assumptions. A closer look at how CBNs may be considered a normative description of causal cognition follows.
6 ConclusionCBNs, and causal calculus in general, and the cognitive system are both powerful enough to be able to deal with counterfactual statements, which are the tip of a complex structural query language used to extract information from the knowledge represented therein. They are a sophisticated way of capturing causal relationships, and numerous applications of causal modelling to cognition bare fruitful results.
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