Coherence-Based Reasoning: Modelling Attention and Information Search

人类在感知、解决问题、推理和判断中努力寻求连贯的表征。因此，感知和知识以某种方式结合起来，形成和谐的解释，以准备适当的反应。并行约束满足模型（PCS）以数学上精确的方式实现了一致性原则。在判断和决策研究中，PCS模型已成功地应用于描述自动信息整合过程，并预测新的现象，如一致性效应，即决策过程中信息评估的变化。PCS-DM已被开发为用于自适应决策的完全指定的模型（Glöckner，Hilbig & Jekel，2014，Cognition），并且已被证明比竞争模型更好地解释选择，响应时间和信心。在目前的工作中，我们的目标是扩展PCS-DM通过添加一个规范的连贯性为基础的机制的注意力和主动信息搜索，迄今未指定的方面的模型。实验测试将致力于调查扩展模型的能力，预测注意力的决定，直接访问的信息，以及积极寻找新的信息。

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.