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How Base Rate Statistics and Counterfactuals Influence Causal Models and Recommendations for Traffic Safety How does counterfactual thinking—thinking about how events could have been different—affect our beliefs about factors that cause changes in statistics? Slovic and Fischhoff (1977): Participants were given the outcome of an experiment, then asked to think of how it could have turned out otherwise, which reduced bias. Lord, Lepper, and Preston (1984): Participants who considered possible causes for the opposite of their expected outcomes, expressed less extreme beliefs than controls. The present project focused on beliefs about causes and ways to prevent traffic fatalities. Consider the Opposite and Incorporation of 2010 Statistic No reliable difference in surprise or number of new causes given between those who consideed the opposite and those who did not. However, Foresight participants who initially thought there had been an increase in fatalities were reliably more surprised than Hindsight participants (Mann-Whitney U, z= -4.08, p<.001) and provided numerically more new causes (t(52)=1.358, p=.09, one-tailed) after learning actual statistic. Of those who considered the opposite and changed their estimates, change in reestimate did not predict surprise. (Spearman’s rho =-.02, n.s.) Discussion References Lord, C., Lepper, M., & Preston, E. (1984). Considering the opposite: A corrective strategy for social judgment. Journal of Personality and Social Psychology, 47, Markman, K., Lindberg, M., Kray, L., & Galinsky, A. (2007). Implications of counterfactual structure for creative generation and analytical problem solving. Personality and Social Psychology Bulletin, 33, Slovic, P., & Fischhoff, B. (1977). On the psychology of experimental surprises. Journal of Experimental Psychology: Human Perception and Performance, 3, Upon learning that one estimated in the wrong direction, how does one explain actual statistics? Examples: Original Believed Cause: cell phone usage Later “Old” Cause: cell phone laws implemented Similar to Markman et al.’s (2007) subtractive counterfactuals Later “New” Cause: safer roads Similar to Markman et al.’s (2007) additive counterfactuals Jennifer Milazzo, Edward Munnich, Jade Stannard, and Katheryn Conde Central Question Design University of San Francisco Surprise upon learning actual statistics may have overwhelmed differences between those who considered the opposite and those who did not in the Foresight group. After considering the opposite, Hindsight participants provided more new causes relative to old causes affecting 2015 statistics when they considered the opposite. Suggests qualitative shift in thinking about causal factors in traffic fatalities—listing new causal factors rather than modifying original causal factors. Consider the opposite may have produced an additive counterfactual mindset (Markman et al., 2007)—greater creativity—at least for those who received it after learning statistic (Hindsight). Our findings suggest that actively considering opposite causes leads to less rigid interpretation of statistics, and might be useful to educators teaching critical evaluation of media sources. 98 participants were recruited through Amazon Mechanical Turk Completed one of four surveys on surveymonkey.com Both Foresight Groups received a 2005 traffic fatalities statistic and answered questions about 2010 fatalities 145 out of every million Americans were killed in car accidents in With this statistic in mind, please estimate how many Americans were killed in car accidents in Next, think of the factor(s) you believe to have caused an increase/decrease in U.S. traffic fatalities between 2005 and 2010 Foresight + Consider The Opposite Group Participants were told to suppose that the traffic fatality statistic actually moved in the opposite direction of what they predicted and to describe factor(s) that would have contributed to this Both Foresight Groups re-estimated 2010 statistic All Groups received statistics for both 2005 and 2010 and answered questions about 2010 fatalities In 2005, 145 out of every million Americans were killed in car accidents, whereas in 2010, 106 out of every million Americans were killed in car accidents. Please choose the statement that indicates how surprised you are with the change in statistics between 2005 and 2010 (not at all surprised… extremely surprised). Next, think of the factor(s) you believe to have caused an increase/decrease in U.S. traffic fatalities between 2005 and Hindsight + Consider the Opposite Group considered why statistic could have moved in opposite direction All groups Estimated car accident fatalities for 2015 and described factor(s) that would cause an increase/decrease Described action(s) that could reduce car accident fatalities by 2015 and estimated how low fatalities could go by 2015 Participants and Procedure Examples of New Vs. Old Causes Between-Subjects Independent Variables: Prior Estimation (Hindsight vs. Foresight): Whether or not one estimated statistics and considered causes before receiving actual statistics. Considering the Opposite: Whether participants were asked to consider changes in the statistic in the opposite direction. Dependent Variables: 1.Incorporating 2010 fatality statistics: a. Level of surprise upon learning the actual statistics, b. Number of new vs. old causes used to explain changes in statistics for Expectations for 2015 fatalities: Number of new vs. old factors used to explain changes in statistics expected for Hopes for 2015 fatalities: Number of new vs. old actions that could be taken to reduce traffic fatalities Results Consider the Opposite and Hopes for 2015 No reliable differences in new vs. old causes participants mentioned in actions they would take to further reduce fatalities for 2015, but reliably more new causes cited overall (t(97)=4.183, p<.001, one-tailed) Consider the Opposite and Beliefs for 2015 Foresight Participants: Directional pattern, but no reliable difference in the number of new minus old causes. Hindsight Participants: Difference between new and old causes was reliability larger for those who considered the opposite, t(43)=-1.79, p=.04 (one- tailed) Method Participants would think of more new causes for traffic fatalities—relative to causes listed earlier—after considering what might have driven fatalities in the opposite of expected direction. Hypothesis