Bravo-Hermsdorff, Gecia;
(2023)
Quantifying Human Priors over Social and Navigation Networks.
In: Lawrence., Neil, (ed.)
Proceedings of Machine Learning Research.
(pp. pp. 3063-3105).
PMLR: Honolulu, Hawaii, USA.
Preview |
Text
bravo-hermsdorff23a.pdf - Other Download (2MB) | Preview |
Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | Quantifying Human Priors over Social and Navigation Networks |
| Event: | 40 th International Conference on Machine Learning |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://proceedings.mlr.press/v202/bravo-hermsdorf... |
| Language: | English |
| Additional information: | This version is the version of record. For information on re-use, please refer to the publisher’s terms and condition |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10186219 |
Archive Staff Only
 |
View Item |
