Michał J. Wójcik
Attention & Working memory Lab (Stokes Lab) & Cognitive Computational Neuroscience Lab (Hunt Lab)
The learning dynamic of abstraction in humans, non-human primates, and artificial networks
My research focuses on how the brain gives rise to representations of complex relations that can be encountered in the environment. Furthermore, a special focus of my DPhil is the process of generalising them across different domains to facilitate learning.
To probe the neuronal activity for the emergent properties necessary for generalisation, I use human electroencephalography and high resolution neural population recordings from animals. Novel machine learning algorithms and representational geometry are employed to open a window into the computational processes underlying generalisation and its link to flexible, inteligent behaviour.
Some artificial systems like are also able to learn an abstract representation and achieve a high level of generalisation. However, training them requires more resources than human learning. Understanding what makes human learning so energy-efficient and fast could provide useful insights in such fields like machine learning and artificial intelligence.
I am supervised by Mark Stokes, Laurence Hunt and Nicholas Myers, and funded by the Clarendon Fund Scholarship in partnership with the Saven European Scholarship.