Cognitive dissonance reduction as constraint satisfaction.
A constraint satisfaction neural network model (the consonance model) simulated data from the two major cognitive dissonance paradigms of insufficient justification and free choice. In several cases, the model fit the human data better than did cognitive dissonance theory. Superior fits were due to the inclusion of constraints that were not part of dissonance theory and to the increased precision inherent to this computational approach. Predictions generated by the model for a free choice between undesirable alternatives were confirmed in a new psychological experiment. The success of the consonance model underscores important, unforeseen similarities between what had been formerly regarded as the rather exotic process of dissonance reduction and a variety of other, more mundane psychological processes. Many of these processes can be understood as the progressive application of constraints supplied by beliefs and attitudes.
Shultz
T R
TR
Department of Psychology, McGill University, Montréal, Québec, Canada. shultz@psych.mcgill.ca
Lepper
M R
MR
D003071:Cognition; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer