Modeling the Effect of Evaluative Conditioning on Implicit Attitude Acquisition and Performance on the Implicit Association Test

Using a previously proposed computational model of human performance on the Implicit Associations Test (IAT), we explore how evaluative conditioning could inform attitude acquisition and formation of automatic associations in memory, and demonstrate the effects of such learning on implicit task performance on the test. This is achieved by augmenting the model with a learning mechanism based on a modified Hebbian learning rule that adapts associative strengths between concepts depending on the temporal proximity of their activation. By manipulating the frequencies at which different stimuli are paired and presented as input to the network, we demonstrate how virtual subjects could acquire associative strengths that were subsequently reflected in simulated IATs as stronger relative preferences in favor of targets that were more frequently presented with positively-valenced stimuli. The model predicts that associations that are already strong have limited prospects for continued reinforcement.