The Influence of Causal Knowledge in Two-Alternative Forced-Choice Tasks

Rocio Garcia-Retamero*, 1, Ulrich Hoffrage1, Stephanie M. Muller1, Antonio Maldonado1
1 Universidad de Granada, Spain
2 Faculty of Business and Economics, University of Lausanne, Switzerland

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© 2009 Garcia-Retamero et al.

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: ( This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Facultad de Psicologia, Universidad de Granada, Campus Universitario de Cartuja s/n, 18071 Granada, Spain; Tel: +34958246240, Fax: +34958246239; E-mail:


Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limited computational capacity, time, and knowledge when making decisions. These heuristics are effective to the extent that they can exploit the structure of information in the environment in which they operate. They require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. We suggest that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. Specifically, we claim that causal knowledge can act as a meta-cue for identifying highly valid cues and help to estimate cue-validities. Causal knowledge, however, can also bias people's decisions. We review experimental evidence that tested these hypotheses.

Keywords: Causal Knowledge, Information Search, Belief Revision Model, Take the Best, Decision Making, Judgment.