The iterative reprocessing model: A multilevel framework for attitudes and evaluation
The iterative reprocessing model: A multilevel framework for attitudes and evaluation
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DOI：
10.1521/soco.2007.25.5.736
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发表时间：
2007-10-01
期刊：
影响因子：
1.9
通讯作者：
Van Bavel, Jay J.
中科院分区：
文献类型：
--
作者：
Cunningham, William A.;Zelazo, Philip David;Van Bavel, Jay J.
态度的双过程模型强调了评估过程是复杂且多方面的这一事实。然而，许多这些模型通常忽略了有助于评估的过程之间的重要相互作用。在本文中，我们提出了一种基于神经科学的多级模型，其中当前的评估是通过信息的迭代再处理从相对稳定的态度表征构建的。虽然初始迭代提供相对快速和肮脏的评估，但伴随反思过程的额外迭代会产生更细致的评估，并允许出现诸如矛盾心理之类的现象。重要的是，该模型预测，相对自动评估背后的流程将继续参与多次迭代，并且它们会影响更具反思性的流程，也受到更多反思性流程的影响。我们在计算、算法和实施分析层面描述了迭代再处理模型（Marr，1982），以更全面地描述其前提和预测。
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.