8 March 2024
Dani Smith Bassett
Department of Bioengineering
University of Pennsylvania
Human learners acquire not only disconnected bits of information, but complex interconnected networks of relational knowledge. The capacity for such learning naturally depends on the architecture of the knowledge network itself. I will describe recent work assessing network constraints on the learnability of relational knowledge, and a free energy model that offers an explanation for such constraints. I will then broaden the discussion to the generic manner in which humans communicate using systems of interconnected stimuli or concepts, from language and music, to literature and science. I will describe an analytical framework to study the information generated by a system as perceived by a biased human observer, and provide experimental evidence that this perceived information depends critically on a system's network topology. Applying the framework to several real networks, we find that they communicate a large amount of information (having high entropy) and do so efficiently (maintaining low divergence from human expectations). Finally, I will use these intuitions to ask the question: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactively mitigate biases in learning? I will show that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information. Taken together, our results provide a unique network-based lens through which to understand how humans build models of their networked world.