Coherence-Based Reasoning: Modelling Attention and Information Search

人类努力在感知、解决问题、推理和判断方面获得连贯的表达。因此，感知和知识以一种方式结合在一起，形成和谐的解释，以便准备适当的回应。并行约束满足模型(PCS)以精确的数学方式实现一致性原则。在判断和决策研究中，PCS模型已被成功地应用于描述自动信息整合过程和预测新的现象，如一致性效应，即决策过程中信息评价的变化。PCS-DM已经发展成为一种完全特定的自适应决策模型(Glöockner，HilBig&Jekel，2014，Cognition)，并且它被证明比竞争模型更好地考虑了选择、反应时间和信心。在目前的工作中，我们的目标是通过增加基于一致性的注意和主动信息搜索机制的规范来扩展PCS-DM，这是该模型迄今尚未指定的方面。实验测试将致力于调查扩展模型预测注意力在使用直接可访问信息的决策中的能力，以及主动搜索新信息的能力。

Humans strive for coherent representations in perception, problem solving, reasoning, and judgment. Thereby perceptions and knowledge are combined in a way to form harmonious interpretations in order to prepare appropriate responses. Parallel constraint satisfaction models (PCS) implement coherence principles in a mathematically precise way. In judgment and decision making research, PCS models have been applied successfully to describe automatic information integration processes and to predict new phenomena such as coherence effects, that is, shifts in the evaluation of information during decision making. PCS-DM has been developed as a fully specified model for adaptive decision making (Glöckner, Hilbig & Jekel, 2014, Cognition), and it has been shown to account for choices, response times and confidence better than competing models. In the current work we aim to extend PCS-DM by adding a specification for coherence-based mechanisms of attention and active information search, hitherto unspecified aspects of the model. Experimental tests will strive at investigating the extended model's ability to predict attention in decisions with directly accessible information as well as active search for new information.