Probabilities and polarity biases in conditional inference.
A probabilistic computational level model of conditional inference is proposed that can explain polarity biases in conditional inference (e.g., J. St. B. T. Evans, 1993). These biases are observed when J. St. B. T. Evans's (1972) negations paradigm is used in the conditional inference task. The model assumes that negations define higher probability categories than their affirmative counterparts (M. Oaksford & K. Stenning, 1992); for example, P(not-dog) > P(dog). This identification suggests that polarity biases are really a rational effect of high-probability categories. Three experiments revealed that, consistent with this probabilistic account, when high-probability categories are used instead of negations, a high-probability conclusion effect is observed. The relationships between the probabilistic model and other phenomena and other theories in conditional reasoning are discussed.
Oaksford
M
M
School of Psychology, Cardiff University, Wales, United Kingdom. oaksford@cardiff.ac.uk
Chater
N
N
Larkin
J
J
D006801:Humans; D015233:Models, Statistical; D011340:Problem Solving