Strong ordinal properties of an additive model for the sequential processing of probabilistic information

Abstract A general theory of probabilistic information processing, together with its formal representation as an additive-difference model, is specialized to account for behavior when information is observed sequentially. Both the basic assumptions and the derived additive model are testable separately with ordinal data from individual subjects. Furthermore, scale values for the sequential information can be derived with the ordinal data and the model. Eight subjects were run extensively in probabilistic information processing tasks, in each problem sequentially receiving two samples of information in order to decide which of two hypotheses was more likely correct. The additive model was supported by the data of all but one subject; for those subjects described by this model, the prior assumptions received moderate to strong support. The pattern of results was employed to discuss additive versus averaging processes, sequential effects, and the general processing theory.

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