Probabilities and polarity biases in conditional inference.

Abstract

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. Oaksfurd & 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 highprobability conclusion effect is observed. The relationships between the probabilistic model and other phenomena and other theories in conditional reasoning are discussed.

The probabilistic approach to human reasoning

“…Following other researchers in this area [25][26][27][28][29] we proposed a model of conditional reasoning based on conditional probability 21 . The greater the conditional probability of an inference the more it should be endorsed.…”

“…The greater the conditional probability of an inference the more it should be endorsed. The meaning of a conditional statement was defined in a two by two contingency table 7,21 (Fig. 1).…”

“…Consequently the model also predicts that the fallacies should be endorsed to some degree. This model has also been applied to a variety of other effects in conditional inference 21,22 (see Box 2).…”

“…This approach was first applied to Wason's selection task 5 , where many models have been proposed [6][7][8][9][10][11] and investigated [12][13][14][15][16][17] . More recently the probabilistic approach has been extended to the other core areas of the psychology of reasoning: syllogisms [18][19][20] and conditional inference [21][22][23][24] , where the need for a probabilistic approach has been most apparent [25][26][27][28][29] .…”

The modulation of conditional assertions and its effects on reasoning

“…6), who defends a dualprocess theory. They gave a better account of the data than either the suppositional theory (Evans & Over, 2004) or the probabilistic theory (Oaksford, Chater, & Larkin, 2000).…”