Deep

A model of gaming preferences informed by the hierarchical nature of goal-oriented cognition

Dados Bibliográficos

ID19065644
AutoresE Dubourg (0000-0002-2162-6526, Centre National de la Recherche Scientifique), Valérian Chambon (0000-0002-2401-1906, Centre National de la Recherche Scientifique)
Ano2024
Data de publicação2024-06-06
Open AccessNão
TipoPREPRINT
EditoraCenter for Open Science (PUBLISHER)
DOI10.31219/osf.io/z3mvu
OpenAlexW4399380261
IdiomaEN
Citações recebidas1
Referências citadas20

Video game design and player engagement revolve around the concept of agency, which refers to the ability to influence and shape one’s environment through personal choices and actions. However, agency is far from being a unitary concept: different types of agency can be distinguished according to the nature of the agent’s goal. Recent models of voluntary action in cognitive science suggest that goals are organized hierarchically along two major gradients: (1) a gradient of abstraction that depends on the role the goal plays in controlling and monitoring action, (2) a gradient of exploration that depends on the relationship the goal has with other goals or actions in the hierarchy. Here, we argue that a careful examination of these models can help better characterize players’ experiences. Specifically, we hypothesize that variability in players’ preferences for different forms of agency can account for inter-individual differences in gaming preferences. To test this hypothesis, we first performed a factor analysis on a wide variety of game-related actions that participants (N=750) were asked to rate on an interest scale. As predicted by the theoretical framework, we found that game preferences varied along 4 dimensions organized along gradients of goal abstraction and exploration (i.e., Discovering, Experimenting, Expanding, Performing, or DEEP dimensions). We then used a Large Language Model (LLM), to automatically annotate video games (N=16,000) on each of these dimensions, to directly test the hierarchical structure of goal-directed actions in video games. Finally, in a pre-registered experiment (N=1000), we show that the DEEP dimensions as measured by the DEEP questionnaire predict participants’ preferred video games and are correlated with expected psychological and socio-demographic factors. We suggest that this research can help improve existing taxonomies of video game types, better understand player preferences, and refine the relationship between game design and human psychology. We also believe that it can, in turn, inform and further ground empirically models of goal-directed actions in cognitive science

Cognition · Cognitive psychology · Cognitive science · Goal Orientation · Human–computer interaction · Management science · Computer Science · Decision-Making and Behavioral Economics · Educational Games and Gamification · Engineering · Neuroscience · Psychological and Educational Research Studies · Psychology · Social Psychology