Modeling human decision-making in naturalistic settings

The widespread availability of interactive devices implies that choices are often based on information presented in graphical user interfaces (GUIs), such as when booking a flight or a hotel online. These tasks involve selecting an option among competing alternatives within visual displays, necessitating evidence accumulation to support decision-making. Modeling how people make decisions in such settings is valuable for both theory and practice. A better understanding of naturalistic decision-making could, for instance, aid in developing practical applications like decision support systems. However, much of the previous work on modeling decision-making focuses on controlled settings that differ significantly from everyday decision contexts. Additionally, the process of information gathering in information-rich interfaces is under-studied, limiting our understanding of decision-making in such settings. This thesis aims to bridge this gap by extending models of decision-making from controlled environments to naturalistic settings to explain and predict choices, using the case of GUIs. The contributions of this thesis can be summarized in three main claims. Firstly, I argue that applying existing cognitive models in naturalistic settings—where the experimental design cannot be controlled—must be undertaken cautiously. This is due to the quality of model fitting being dependent on the often limited and uncontrolled set of tasks presented to the users. Such circumstances can lead to potential issues with parameter recovery, as demonstrated with the application of two classic decision-making models under risk to naturalistic game logs. Secondly, I contend that understanding the information gathering process preceding a choice is critical for effectively modeling decision-making in GUIs. I discuss how information gathering may be studied through representative eye-tracking studies where the stimuli preserve the structure of the naturalistic environment. In these settings, users' visual attention, the order in which they focus on elements, and their reaction times are influenced by the features of the GUI. I present evidence of these observations from two empirical studies focusing on visual search and browsing. My third claim states that information gathering should be integrated into models of naturalistic decision-making. This can be achieved by representing decision-making as a partially observable Markov decision process (POMDP) solved using reinforcement learning (RL). The proposed approach is based on the concept of computational rationality, which I argue is suitable for modelling naturalistic choice for three reasons: it appropriately captures mechanisms that explain information gathering, relaxes data requirements, and allows for the incorporation of machine learning-based elements for enhanced predictive accuracy. I demonstrate this approach through the example of multi-attribute choice, reproducing various context effects in property selection. This thesis concludes by discussing the implications of these findings for our theoretical understanding of decision-making in naturalistic settings, connecting the results to the Adaptive Interaction framework. Additionally, I consider workflows as a tool to further advance modeling of decision-making in naturalistic environments.