Expectancy confirmation in attitude learning: A connectionist account

Abstract

AbstractConnectionist computer simulation was employed to model how learners acquire attitudes towards novel objects under conditions where (a) they are given prior expectancies that the objects as a whole are mostly good or mostly bad; and (b) they can only discover the true valence of the objects by approaching them. Expectancy confirmation was operationalized through modifying connection weights more after experiencing good than bad objects (positive bias), or more after experiencing bad than good objects (negative bias). Negative bias led the network to misclassify more good objects as bad, such negative attitudes resisting change because of the lack of corrective feedback relating to avoided objects. Conversely, positive bias encouraged approach and hence feedback leading to more accurate discrimination of good and bad objects, as well as to higher estimates of the valence of objects not presented during training. These findings suggest that expectancy confirmation may emerge “automatically” from basic learning processes. Copyright © 2008 John Wiley & Sons, Ltd.