Abstraction Processes in Associative Learning

联想学习通常指的是学习世界中的属性、对象或事件之间的关系。由此产生的表示可能会有所不同，他们保留的细节水平，我们认为这是一个方面，其中非模态表示不同于模态的。非模态表征从个体刺激的细节中抽象出来，专注于它们的共享或诊断属性。相比之下，模态表征保留了所遇到的刺激的特征。据推测，非模态表示允许更强和更广泛的泛化，并且比模态表示更鲁棒。在这个项目中，我们调查的假设，即学习的关系的抽象依赖于刺激环境的结构。基于错误驱动学习和线索竞争的概念，我们研究了表征的抽象性的潜在调节因素，如刺激的可变性和刺激呈现的顺序。在我们的项目中，我们采取了一个综合的角度来看，比较这些因素的作用之间的两个中央联想学习范式，即评价条件反射和概念学习。

Associative learning typically refers to the learning of relationships between attributes, objects, or events in the world. The resulting representations may differ in the level of detail they preserve, which we consider one aspect in which amodal representations differ from modal ones. Amodal representations abstract away from the details of individual stimuli to focus on their shared or diagnostic properties. By contrast, modal representations preserve the features of the stimuli encountered. Presumably, amodal representations allow for stronger and wider generalization and are more robust than modal representations. In this project, we investigate the hypothesis that the abstractness of the relationships learned depends on the structure of the stimulus environment. Based on the notion of error-driven learning and cue-competition, we investigate potential moderators of the abstractness of representations such as stimulus variability and the sequence of stimulus presentation. In our project, we take on an integrative perspective, comparing the role of these factors between two central associative learning paradigms, namely evaluative conditioning and concept learning.