The probability heuristics model of syllogistic reasoning

A probability heuristic model (PHM) for syllogistic reasoning is proposed. An informational ordering over quantified statements suggests simple probab ility based heuristics for syllogistic reasoning. The most important is the "min-heuristic": choose the type of the least informative premise as the t ype of the conclusion. The rationality of this heuristic is confirmed by an analysis of the probabilistic validity of syllogistic reasoning which trea ts logical inference as a limiting case of probabilistic inference. A meta- analysis of past experiments reveals close fits with PHM. PHM also compares favorably with alternative accounts, including mental logics, mental model s, and deduction as verbal reasoning. Crucially, PHM extends naturally to g eneralized quantifiers, such as Most and Few, which have not been character ized logically and are, consequently, beyond the scope of current mental lo gic and mental model theories. Two experiments confirm the novel prediction s of PHM when generalized quantifiers are used in syllogistic arguments. PH M suggests that syllogistic reasoning performance may be determined by simp le but rational informational strategies justified by probability theory ra ther than by logic. (C) 1998 Academic Press.