Description du projet
Étude de la planification humaine face aux incertitudes
Nous avons la capacité de faire des plans et de formuler des stratégies visant à résoudre des problèmes, seuls ou en groupe. Alors que ces processus sont considérés comme remarquables, la manière dont ils se forment n’est pas encore établie. Dans ce contexte, le projet ThinkAhead, financé par l’UE, se penchera sur la manière dont les gens échafaudent des plans face à l’incertitude quant à la structure de la tâche, aux séquences d’action et aux contributions du soi et des autres aux plans coopératifs. Plus particulièrement, il étudiera une nouvelle théorie qui envisage la planification humaine du point de vue de l’inférence probabiliste, sur la base de codes prédictifs hiérarchiques constitués d’informations comprimées ou d’abstractions de tâches. En combinant des méthodes de modélisation expérimentale et informatique, le projet cherchera à valider cette nouvelle théorie.
Objectif
Humans have an impressive ability to form action plans in several domains of cognition; for example, planning routes to goals in spatial navigation, or the necessary steps to assemble complex objects, alone or together with other persons. However, the computations that underlie human individual and social planning remain largely unknown.
This proposal aims to explain the ways humans face three key forms of uncertainty arising in planning domains; namely, uncertainty about task structure, action sequences, and the contributions of self and others to cooperative plans. To this aim, it advances a radically new theory about human planning, within a Bayesian approach that has been successfully adopted to explain uncertainties arising in perception and control. The theory under scrutiny is that humans plan using probabilistic inference based on hierarchical predictive codes (HPCs): compressed information or task abstractions that afford a powerful form of uncertainty-minimization, by highlighting salient junction points of the problem at hand, analogous to saliency maps for visual search.
The methodology will combine empirical and computational modeling methods, to systematically validate the hypotheses of HPC theory about human planning in the face of uncertainties. A cornerstone of the methodology consists in conducting model-based analyses of human participants' behavior while they solve navigation-and-building tasks, alone or in dyads. This approach will permit us to compare the predictions stemming from HPC with those of alternative planning theories and ultimately, to understand the computations that underlie human planning.
This ambitious proposal will produce groundbreaking advancements in our understanding of a high-level executive function - planning - while also contextualizing it within the influential theory of predictive processing. Our results will have important implications for psychology, neuroscience, philosophy, AI and robotics.
Champ scientifique
Mots‑clés
Programme(s)
Régime de financement
ERC-COG - Consolidator GrantInstitution d’accueil
00185 Roma
Italie