Quantifying Human Priors over Social and Navigation Networks
Authors: Gecia Bravo-Hermsdorff
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1Department of Statistical Science, University of London, UK. |
| Pseudocode | Yes | Algorithm 1 GENERIC MCMCP EXPERIMENT Initialize: hypothesis0 for t = 1 to T do evidencet = EXPMNT(hypothesist 1) hypothesist = PTCPNTt(evidencet) end for |
| Open Source Code | No | No explicit statement about providing open-source code for the methodology is found in the paper. |
| Open Datasets | No | The paper states: |
| Dataset Splits | No | The paper states: |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers are listed in the paper. |
| Experiment Setup | Yes | For the results in figures 3, 4, and 5, for each number of nodes and each cover story, we selected the order r of the prior by cross-validation using a 80% training set, 20% test set split, and 64 repetitions of the process. For all of fit priors, we find that higher-order fits (r = 4, 5, or 6) were selected. |