Tyler Malloy ^* Cleotilde Gonzalez

• Carnegie Mellon University, Pittsburgh, United States

Generative Artificial Intelligence has made significant impacts in many fields, including computational cognitive modeling of decision making, although these applications have not yet been theoretically related to each other. In this work, we present a model that integrates generative and cognitive models, using a variety of stimuli, applications, and training methods.Alongside this model integration, we propose a general categorization of different methods of this integration within decision-making research. This categorization is used to compare the existing literature and to provide insight into the design of an ablation study to evaluate the main features of our proposed model in three experimental paradigms. These experiments used for model comparison involve modeling human learning and decision making based on both visual information and natural language, in tasks that vary in realism and complexity. This comparison of applications takes as its basis Instance-Based Learning Theory, a theory of experiential decision making from which many models have emerged and been applied to a variety of domains and applications. The results of this comparison demonstrate the importance of generative models in both forming memories and predicting actions in decision-modeling research.