Information seeking on Bayesian conditional probability problems: A fuzzy‐trace theory account

Abstract Recently, the ‘heuristics and biases’ approach to the study of decision making has been criticized, with a call for better integrated theory. Three experiments stemming from fuzzy‐trace theory addressed information seeking on probability problems, and the cognitive representation of hit‐rates, base‐rates, and the contrapositive. As predicted by the fuzzy‐trace principle of ‘denominator neglect’, many subjects exhibited ‘conversion errors’, confusing the hit‐rate, P(A|B), with the answer, P(B|A). These subjects sought base‐rates less often than other subjects. On causal problems, more subjects correctly represented base‐rates, sought base‐rates more often, and produced more accurate estimates than on non‐causal problems. Subjects tutored on the meaning of the hit‐rate sought the base‐rate more often, and were more accurate than control subjects. Results are explained by fuzzy‐trace theory principles of gist extraction, fuzzy processing preference, denominator neglect, and output interference.