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Dual-process models of attitudes highlight the fact that evaluative processes are complex and multifaceted. Nevertheless, many of these models typically neglect important interactions among processes that can contribute to an evaluation. In this article, we propose a multilevel model informed by neuroscience in which current evaluations are constructed from relatively stable attitude representations through the iterative reprocessing of information. Whereas initial iterations provide relatively quick and dirty evaluations, additional iterations accompanied by reflective processes yield more nuanced evaluations and allow for phenomena such as ambivalence. Importantly, this model predicts that the processes underlying relatively automatic evaluations continue to be engaged across multiple iterations, and that they influence and are influenced by more reflective processes. We describe the Iterative Reprocessing Model at the computational, algorithmic, and implementational levels of analysis (Marr, 1982) to more fully characterize its premises and predictions.
Recent advances in neuroscientific methods have provided researchers with an unprecedented opportunity to examine the neural correlates of human thought and emotion. Inspired by achievements in cognitive domains, social psychologists have recently turned to neuroscience to add a layer of understanding to the processes of social judgment and behavior. Efforts to understand social processes across multiple levels of analysis have inspired a new field, variably called social neuroscience (Cacioppo, Berntson, Sheridan, & McClintock, 2000), social cognitive neuroscience (Ochsner & lieberman, 2001), and the social brain sciences (Adolphs, 2003). Incorporating knowledge regarding brain function into our understanding of attitudes and evaluation promises to lead to the refinement of theoretical models and generation of novel hypotheses. We propose an initial multilevel framework for understanding some of the core operating characteristics of the human evaluative system. We contend that consideration of the neural and computational processes underlying evaluation provides several novel and provocative answers to outstanding "controversies" among attitude theorists, including whether or not attitudes are constructed or stable, whether or not there is one 'true' attitude, the relation between "automatic" and "controlled" evaluations, and the nature of attitudinal ambivalence. We also believe that our model of the human evaluative system will inform other debates in the field of attitudes and related fields, such as prejudice, judgment and decisionmaking, and emotions.
Our discussion will be grounded in the Iterative Reprocessing (IR) Model of evaluation (Cunningham & Zelazo, 2007)....