The Emergence of Abstract Representations in Learning and Development

• Applicant: Professorin Dr. Yee Lee Shing
• Subject Area: Developmental and Educational Psychology ; Biological Psychology and Cognitive Neuroscience
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 459426179

Abstract knowledge, such as categories, enables the human brain to process the overwhelming amount of incoming information from the environment in an efficient way, namely by reducing the complexity and dimension of information. One aspect of human cognition unmatched by artificial intelligence (AI) is the capacity to generalize after experiencing few samples. For example, a concept for “bird” as a category may emerge in a child after experiencing just several instances of birds. Acquisition of abstract knowledge is assumed to entail the extraction and consolidation of regularities across event memories (i.e. episodic memory). On the other hand, episodic memory preserves the specificity of individual events by binding together unique combinations of features from an episode. Thus far, it is not well understood how the brain learns to capture regularities across experiences for the acquisition of categorical knowledge, and how these dynamic processes differ between children and adults. Therefore, this project will examine the emergence of abstract representation for categories and how it interacts with episodic memory of specific experiences. We will develop an age appropriate experimental paradigm for children and adults that allows tracking the representations of both abstract knowledge and episodic memory in the human brain. These representations are indexed not just by behavioral measures, but also measures with high temporal and spatial resolution, i.e. eye-tracking and functional magnetic resonance imaging data, in order to characterize the dynamic processes across time. Computational methods will be used to quantitatively characterize the underlying structures within the multivariate, high-dimensional data, and at the same time, explore the extent to which AI models can approximate the human data in the latent representational structure. Gaining insights on how categorical knowledge emerges in the human brain, particularly during child development with its heightened brain plasticity, may help to inform future AI models that are capable of learning new categories efficiently with few learning episodes and subsequently applying them flexibly to new situations.

• DFG Programme: Research Units
• Subproject of: FOR 5368: KI-FOR Abstract Representations in Neural Architectures (ARENA)