title: Probability discounting among cigarette smokers and nonsmokers: molecular analysis discerns group differences.
abstract: Despite numerous studies documenting differences in temporal discounting among cigarette smokers and nonsmokers, studies of probability discounting have had mixed results. This study sought to clarify these results with a molecular analysis of data obtained from a study of probability discounting. Thirty heavy cigarette smokers and 29 nonsmokers were compared using a probability discounting procedure. When these data were fit to a hyperbolic model of discounting, nonstatistically significant group difference between smokers and nonsmokers were observed in the predicted direction. A molecular analysis of indifference points indicated that statistically significant differences between groups were obscured by a possible floor effect. Specifically, indifference points obtained from high probabilities were lower for heavy cigarette smokers relative to nonsmokers, whereas no difference was observed with indifference points obtained from low probabilities. This molecular analysis suggests that the prior inconsistent results are due to floor effects resulting from greater inclusion of low probabilities. Rate of smoking may also be an important factor, with differences more likely observed when heavy smokers are enrolled.
authors.affiliation: Center for Addiction Research, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA. ryi@uams.edu
authors.forename: Richard
authors.initials: R
authors.lastname: Yi
authors.forename: Whitney D
authors.initials: WD
authors.lastname: Chase
authors.forename: Warren K
authors.initials: WK
authors.lastname: Bickel
mesh_terms: D000328:Adult; D000465:Algorithms; D000704:Analysis of Variance; D002755:Choice Behavior; D005106:Exploratory Behavior; D005260:Female; D006801:Humans; D008297:Male; D008960:Models, Psychological; D010555:Personality Inventory; D011336:Probability; D012907:Smoking / Q000523:psychology*; D012919:Social Behavior; D013997:Time Factors