Quantifying Human Priors over Social and Navigation Networks
This work addresses the challenge of modeling implicit human biases in relational data, which is incremental in applying nonclassical statistical analysis to behavioral experiments.
The researchers tackled the problem of quantifying human priors over relational data in social and navigation networks, finding consistent sparsity patterns across domains and domain-specific features like triadic closure in social interactions.
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.