Counterfactual thinking, where one envisions alternative pos- sible events and their outcomes, is hypothesized to be one of the primary ways in which we reason about causal relation- ships (e.g., Pearl, 2000; Woodward, 2003). Recent computational and experimental work suggests that both adults and children may reason about causality in a manner consistent with probabilistic graphical models – coherent, complex representations of causal structure that allow distinctive kinds of inferences (e.g., Gopnik et al., 2004; Griffiths & Tenenbaum, 2009). In particular, the causal models approach supports and distinguishes two types of inferences, predictions, on the one hand, and interventions, including counterfactual interventions, on the other. In predictions, we take what we think is true now as a premise and then use the model to calculate what else will be true. In counterfactuals, we take some value of the model that we currently think is not true as a premise, and calculate what would follow if it were.
|Title of host publication||Proceedings of the 35th Annual Meeting of the Cognitive Science Society, CogSci 2013, Berlin, Germany, July 31 - August 3, 2013|
|Number of pages||2|
|Publication status||Published - 2013|