Analogical transfer in multi-attribute decision making

We are sometimes faced with inference tasks in a domain of interest where we do not have sufficient information, but we could use our knowledge from other domains to help solve the problem. We frequently undergo this knowledge transfer process, but what are the underlying mechanisms that enable us to achieve this feat? One possible answer is through analogy. This study is interested in how analogy influences decision making performance in a new environment. The knowledge transferred to a new environment can be the importance of cues, and the strategies. The experiments in the study investigate analogical transfer from one domain to another in multi-attribute decision-making tasks. It investigates whether knowledge, such as cue-criterion correlations and best-performing strategy, can be transferred via analogical mapping. The goal of the modeling is to understand the mechanisms underlying analogical transfer in cue learning and strategy selection. The model has two components: reinforcement learning of strategy selection and analogical transfer. Both components will be implemented in ACT-R because it is a well-established framework for integrating cognitive models.