Descripción del proyecto
Estudio de la planificación humana ante la incertidumbre
Las personas tienen la capacidad de hacer planes y formular estrategias de resolución de problemas, ya sea en solitario o en grupo. Aunque estos procesos se consideran extraordinarios, no está claro cómo se establecen. En este contexto, el equipo del proyecto financiado con fondos europeos ThinkAhead explorará cómo las personas planifican ante la incertidumbre en lo que respecta a la estructura de las tareas, las secuencias de acción y las aportaciones propias y ajenas a los planes cooperativos. En concreto, se investigará una novedosa teoría que considera la planificación humana desde la perspectiva de la inferencia probabilística basada en códigos jerárquicos y predictivos que constan de abstracciones comprimidas de la información o las tareas. Mediante la combinación de métodos de modelización experimental y computacional, el equipo del proyecto tratará de validar la novedosa teoría.
Objetivo
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.
Ámbito científico
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Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
00185 Roma
Italia