The flexibility-generalisability trade-off during the acquisition and reuse of abstract representations

Humans derive knowledge about the world by abstracting and decontextualising information acquired through experience. Abstraction involves a trade-off between flexibility and generalisability; rich, detailed knowledge can be used flexibly in a narrow context, while abstract knowledge can be used in a more limited way but can be generalized to a wider variety of contexts. How the brain acquires and reuses abstract knowledge is an open area of research. In particular, how the structure of learning (the order and information content of observations) affects the properties of acquired knowledge is not well understood. Firstly, we plan to use fMRI to investigate how patterns of brain activity correspond to flexibility and generalisation performance in a cognitive task. Secondly, we aim to design an active learning task to study how the structure of learning mediates the trade-off between flexibility and generality. This would provide a biological basis for how the structure of learning affects the properties of acquired knowledge and performance, contributing towards a Systems Neuroscience understanding of learning and generalisation. Additionally, these insights could have applications in education and machine learning, by informing pedagogical methods and education technology, as well as contributing to the design of more robust and flexible computational models.

Relevant BBSRC priority areas:
- Systems approaches to the biosciences
- Data-driven biology
- Technology development for the biosciences
- Brain science and mental health
- e-Science