Description du projet
Examiner l’influence des modèles internes sur les particularités de l’expérience visuelle
Le cerveau est généralement considéré comme un système prédictif qui relie les stimuli sensoriels à des modèles internes de la manière dont le monde devrait apparaître. Nous savons toutefois peu de choses sur les caractéristiques de ces modèles internes et sur la façon dont ils diffèrent d’une personne à l’autre. Financé par le Conseil européen de la recherche, le projet PEP entend examiner systématiquement le contenu des modèles internes en recourant à l’analyse méthodique de dessins de personnes représentant des scènes de la vie réelle. Les conclusions serviront de base à une recherche cognitive, neuronale et informatique approfondie visant à déterminer comment les modèles internes sont liés à l’expérience visuelle et linguistique individuelle, à l’architecture fonctionnelle du cerveau et à la perception des scènes.
Objectif
In the cognitive and neural sciences, the brain is widely viewed as a predictive system. On this view, the brain conceives the world by comparing sensory inputs to internally generated models of what the world should look like. Despite this emphasis on internal models, their key properties are not well understood. We currently do not know what exactly is contained in our internal models and how these contents vary systematically across individuals. In the absence of suitable methods for assessing the contents of internal models, the predictive brain has essentially remained a black box.
Here, we develop a novel approach for opening this black box. Focusing on natural vision, we will use creative drawing methods to characterize internal models. Through the careful analysis of drawings of real-world scenes, we will distill out the contents of individual people’s internal models. These insights will form the basis for a comprehensive cognitive, neural, and computational investigation of natural vision on the individual level: First, we will establish how individual differences in the contents of internal models explain the efficiency of scene vision, on the behavioral and neural levels. Second, we will harness variations in people’s drawings to determine the critical features of internal models that guide scene vision. Third, we will enrich the currently best deep learning models of vision with information about internal models to obtain computational predictions for individual scene perception. Finally, we will systematically investigate how individual differences in internal models mimic idiosyncrasies in visual and linguistic experience, functional brain architecture, and scene exploration.
Our project will illuminate natural vision from a new angle – starting from a characterization of individual people’s internal models of the world. Through this change of perspective, we can make true progress in understanding what exactly is predicted in the predictive brain.
Champ scientifique
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
ERC - Support for frontier research (ERC)Institution d’accueil
35390 Giessen
Allemagne