Representational changes underlying human learning: A computational cognitive neuroscience perspective

The human mind has been trained intensively in a wide variety of complex information processing tasks. In addition, many individuals develop expertise in specific domains such as chess or microscopy. Previous studies have supported the hypothesis that expert learners do not develop super-powers, such as a larger working-memory capacity, but instead develop a more efficient information processing that is domain specific (see e.g. Bilalic, 2017). To understand the underlying representational changes at the neural level, we need to characterize neural representations in detail, which for a long time has not been possible due to a lack of suitable experimental and computational methods. The goal of this project is to investigate the neural basis of human learning through a state-of-the-art computational cognitive neuroscience approach. We will compare the unfolding of representational structure across brain space (functional magnetic resonance imaging combined with multi-voxel pattern analyses) and across time (representational dynamics as studied with multivariate electroencephalography) between human experts and non-experts, and between human and artificial expert systems (deep neural networks). Through this multidisciplinary approach we will obtain an unprecedented interdisciplinary understanding of how universal learning and domain-specific expertise alter the human mind and brain.

Keywords:Object recognition, Visual learning, Human brain imaging, Deep neural networks, Visual neuroscience

Disciplines:Neuroimaging, Cognitive neuroscience, Knowledge representation and machine learning