RESEARCH ARTICLE
Causation and Conditionals in the Cognitive Science of Human Reasoning
Mike Oaksford*, 1, Nick Chater2
Article Information
Identifiers and Pagination:
Year: 2010Volume: 3
First Page: 105
Last Page: 118
Publisher ID: TOPSYJ-3-105
DOI: 10.2174/1874350101003010105
Article History:
Received Date: 8/12/2009Revision Received Date: 18/1/2010
Acceptance Date: 18/1/2010
Electronic publication date: 13/7/2010
Collection year: 2010
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
This article traces the philosophical and psychological connections between causation and the conditional, if… then, across the two main paradigms used in conditional reasoning, the selection task and the conditional inference paradigm. It is argued that hypothesis testing in the selection task reflects the philosophical problems identified by Quine and Goodman for the material conditional interpretation of causal laws. Alternative formal theories to the material conditional only became available with the advent of possible worlds semantics (Lewis, 1973; Stalnaker, 1968). The relationship proposed by this semantics between counterfactual and indicative conditionals is outlined and it is concluded that moving away from the abstractions of possible worlds proposes a central role for prior knowledge in conditional inference. This conclusion is consistent with probabilistic approaches to conditional inference which provide measures of the strength of a dependency between the antecedent and the consequent of a conditional similar to those proposed in causal learning. Findings in conditional inference suggest that people are influenced not only by the strength of a dependency but also by the existence of the structural relationship, the broader causal framework in which a dependency is embedded, and the inhibitory and excitatory processes like those required to implement Causal Bayes nets or neural networks. That these findings may have a plausible explanation using the tools of current theories in causal learning suggests a potentially fruitful convergence of research in these two areas.
