Reducing cognitive biases in probabilistic reasoning by the use of logarithm formats.

Research on probability judgment has traditionally emphasized that people are susceptible to biases because they rely on "variable substitution": the assessment of normative variables is replaced by assessment of heuristic, subjective variables. A recent proposal is that many of these biases may rather derive from constraints on cognitive integration, where the capacity-limited and sequential nature of controlled judgment promotes linear additive integration, in contrast to many integration rules of probability theory (Juslin, Nilsson, & Winman, 2009). A key implication by this theory is that it should be possible to improve peoples' probabilistic reasoning by changing probability problems into logarithm formats that require additive rather than multiplicative integration. Three experiments demonstrate that recasting tasks in a way that allows people to arrive at the answers by additive integration decreases cognitive biases, and while people can rapidly learn to produce the correct answers in an additive formats, they have great difficulty doing so with a multiplicative format.