| Abstract: | Attitudes are a key construct in health psychology due to their central role in motivating and changing behavior. An expectancy‐value framework has been the dominant conceptual approach for exploring the impact of attitudes on health behavior, applications of which emphasize volition and rational decision making. More recently, attention has focused on automatic attitudes, which are believed to capture reflexive aspects of motivation. Dual‐process models such as the MODE generally treat expectancy‐value and automaticity accounts as representing separate processing pathways. However, both accounts depend on associative learning. Learning history pairs beliefs or features with evaluations; subsequent activation of beliefs or features activates associated evaluations and drives overall attitude. Therefore, a single information processing architecture may accommodate expectancy‐value and automaticity approaches within a unifying framework, and this is provided by neural network (connectionist) accounts. In this paper, we highlight how a greater emphasis on the information processing mechanics of associative learning can provide a more parsimonious model of attitudes, which may also extend to a wider array of memory‐related phenomena of relevance to health psychology. |
