An additive model for sequential decision making

Abstract

54 undergraduates were shown sequences of red and white lights, where each light represented a sample with replacement from a population. After each successive light, Ss either estimated the proportion of white lights in the population (estimation) or judged the probability that the population contained more white than red (inference). Stimulus sequences were constructed from factorial designs. This permitted simple tests of an additive model of sequential decision making derived from a theory of information integration. The additive model worked fairly well in 2 experiments and was able to handle both general recency effects and effects due to sequence length. These effects, along with a failure to find a difference between the estimation and inference conditions, raise some questions about previous Bayesian treatments of sequential decision making. Supplementary data with a static decision-making task were nonadditive, in disagreement with the information-integration approach. (23 ref.) (PsycINFO Database Record (c) 2006 APA, all rights reserved). © 1970 American Psychological Association.