Modeling cognitive processes within a creative problem-solving task: from symbolic to neuro-symbolic approaches in computational learning sciences

avril 2024

Directeur(s) de thèse : Frédéric Alexandre

Résumé de thèse

Integrating transversal skills such as creativity, problem solving and computational thinking, into the primary and secondary curricula is a key challenge in today’s educational field. We postulate that teaching and assessing transversal competencies could benefit from a better understanding of the learners’ behaviors in specific activities that require these competencies. To this end, computational learning sciences are an emerging field that requires the close collaboration of computational neuroscience and educational sciences to enable the assessment of learning processes. We focus on a creative problem-solving task in which the subject is engaged into building a “vehicle” by combining modular robotic cubes. As part of an exploratory research action, we propose several approaches based on symbolic to neuro-symbolic formalisms, in order to specify such a task and model the behavior and underlying cognitive processes of a subject engaged in this task. Despite being at a very preliminary stage, such a formalization seems promising to better understand complex mechanisms involved in creative problem solving at several levels: (i) the specification of the problem and the observables of interest to collect during the task; (ii) the cognitive representation of the problem space, depending on prior knowledge and affordance discovery, allowing to generate creative solution trajectories; (iii) an implementation of reasoning mechanisms within a neuronal substrate.