Quantifying Human Priors over Social and Navigation Networks
Gecia Bravo-Hermsdorff
Human knowledge is largely implicit and relational -- do we have a friend in
common? can I walk from here to there? In this work, we leverage the
combinatorial structure of graphs to quantify human priors over such relational
data. Our experiments focus on two domains that have been continuously relevant
over evolutionary timescales: social interaction and spatial navigation. We
find that some features of the inferred priors are remarkably consistent, such
as the tendency for sparsity as a function of graph size. Other features are
domain-specific, such as the propensity for triadic closure in social
interactions. More broadly, our work demonstrates how nonclassical statistical
analysis of indirect behavioral experiments can be used to efficiently model
latent biases in the data.