Automatic Process Model Generation for Cognitive Modeling

该项目的目标是实现人类推理过程的认知建模的范式转变，到目前为止，该模型主要集中在聚合反应的认知理论上。通过识别和评估现有理论的子组件和原子过程，并形式化个体间差异的特定参数集成的认知操作空间，我们的目标是以自动化的方式生成认知过程模型，该模型可以实现高预测性能并自适应地适应个体，从而允许对人类推理进行个性化认知建模。因此，代表了一个解决方案，以确定在目前的艺术状态的问题，并确定了潜在的改进，在当前的推理研究。由于组件重组的重点是预测能力，因此该方法不需要额外的理论假设，因此可以保证仅根据过程模型的实际附加值来选择认知过程。

The goal of this project is to achieve a paradigm shift in the cognitive modeling of human reasoning processes, which has so far focused on cognitive theories for aggregated responses. By identifying and evaluating subcomponents and atomic processes of existing theories and formalizing the space of cognitive operations in which specific parameters for interindividual differences are integrated, we aim to generate cognitive process models in an automated manner that can achieve high predictive performance and adaptively adjust to the individual, thus allowing for individualized cognitive modeling of human inference, and thus represents a solution to the problems identified in the state of the art and identified potential for improvement in current inference research. Since the focus of component recombination is on predictive power, and the method is thus free of additional theoretical assumptions, it can be guaranteed that the cognitive processes are selected solely on the basis of their actual added value for the process model.