Proceedings of the Annual Meeting of the Cognitive Science Society

One possible method for improving real-world quantitative estimation is to "seed the knowledgebase" with explicit quantitative facts. This method was employed in two population estimation experiments. In Experiment 1, subjects estimated the populations of 99 countries. They then studied the populations of 24 of these countries. Finally, they estimated the populations of all 99 countries a second time. A s predicted, the post-learning estimates for the 75 "transfer" countries were much more accurate (48%) than the pre-leaming estimates. However, the rank-order correlations between estimated population and true populations showed almost no improvement. These results suggested that there m a y be two analytically distinct components to estimation, a range component and a ranking component, and that an arbitrary set of quantitative facts is likely to affect the former but not the latter. The aim of Experiment 2 was to demonstrate that one can affect the ranking component by presenting subjects with a consistent set of population facts. In this experiment, one group of subjects was presented with facts that consistently confirmed their prior beUef that European countries are quite large and Asian countries are quite small. Another group was presented with a set that consistently disconfirmed this view. As predicted, rank-order correlations between estimated and true populations were negatively affected by the biasconfirming facts and positively affected by the bias disconfirming facts.