DCIST researchers are addressing the problem of how information should be presented from robots to humans, and unearthing new biases in the human’s perception and encoding of information. The insights provide an opportunity to tune the information presented to a human for maximal learnability. In a paper titled Network Constraints on the Learnability of Probabilistic Motor Sequences, Ari E Kahn, Elisabeth A. Karuza, Jean M. Vettel, and Danielle S. Bassett from the University of Pennsylvania, U.S. Army Research Laboratory, and Pennsylvania State University, show that the graph architecture underlying temporal sequences of stimuli fundamentally constrains learning, and moreover that tools from network science provide a valuable framework for assessing how learners encode complex, temporally structured information. Find the paper here.
Point of Contact: Danielle Bassett