title: Reasoning as we read: establishing the probability of causal conditionals.
abstract: Indicative conditionals of the form if p then q (e.g., if student tuition fees rise, then applications for university places will fall) invite consideration of a hypothetical event (e.g., tuition fees rising) and of one of its possible consequences (e.g., applications falling). Since a rise in tuition fees is an uncertain event with equally uncertain consequences, a reader may believe the statement to a greater or lesser extent. As a conditional is read, the earliest point at which this probabilistic evaluation can take place is as the consequent clause is wrapped up (e.g., as the critical word fall is read in the example above). Wrap-up processing occurs at the end of the clause, as it is evaluated and integrated into the evolving discourse representation. Five sources of probability may plausibly influence the evaluation of a conditional as it is wrapped up; these are P(p), P(q), P(pq), P(q|p), and P(not-p or q). A total of 128 conditionals were constructed, with these probabilities calculated for each item in a pretest. The conditionals were then embedded in vignettes and read by 36 participants on a word-by-word basis. Using linear mixed-effects modeling, we found that wrap-up reading times were predicted by pretest ratings of P(p) and P(q|p). There was no influence of P(q), P(pq), or P(not-p or q) on wrap-up reading times. Our findings are consistent with the suppositional theory of conditionals proposed by Evans and Over (2004) but do not support the mental-models theory advanced by Johnson-Laird and Byrne (2002).
authors.affiliation: School of Psychological Sciences, University of Manchester, Manchester M13 9PL, UK. matthew.haigh@manchester.ac.uk
authors.forename: Matthew
authors.initials: M
authors.lastname: Haigh
authors.forename: Andrew J
authors.initials: AJ
authors.lastname: Stewart
authors.forename: Louise
authors.initials: L
authors.lastname: Connell
mesh_terms: D032882:Comprehension; D003210:Concept Formation; D006801:Humans; D016014:Linear Models; D011337:Probability Learning; D011340:Problem Solving; D011930:Reaction Time; D011932:Reading; D012660:Semantics