Gibbs Sampling with People
Authors: Peter Harrison, Raja Marjieh, Federico Adolfi, Pol van Rijn, Manuel Anglada-Tort, Ofer Tchernichovski, Pauline Larrouy-Maestri, Nori Jacoby
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In an initial study, we show GSP clearly outperforming MCMCP; we then show that GSP provides novel and interpretable results in three other domains, namely musical chords, vocal emotions, and faces. We validate these results through large-scale perceptual rating experiments. The final experiments use GSP to navigate the latent space of a state-of-the-art image synthesis network (Style GAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications. All combined, these 25 experiments represent data from 5,178 human participants. |
| Researcher Affiliation | Academia | Peter M. C. Harrison* Max Planck Institute for Empirical Aesthetics Frankfurt peter.harrison@ae.mpg.de Raja Marjieh* Max Planck Institute for Empirical Aesthetics Frankfurt raja.marjieh@ae.mpg.de Federico Adolfi Max Planck Institute for Empirical Aesthetics Frankfurt federico.adolfi@ae.mpg.de Pol van Rijn Max Planck Institute for Empirical Aesthetics Frankfurt pol.van-rijn@ae.mpg.de Manuel Anglada-Tort Max Planck Institute for Empirical Aesthetics Frankfurt manuel.anglada-tort@ae.mpg.de Ofer Tchernichovski Hunter College CUNY The CUNY Graduate Center otcherni@hunter.cuny.edu Pauline Larrouy-Maestri Max Planck Institute for Empirical Aesthetics Frankfurt pauline.larrouy-maestri@ae.mpg.de Nori Jacoby Max Planck Institute for Empirical Aesthetics Frankfurt nori.jacoby@ae.mpg.de |
| Pseudocode | No | The paper describes the MCMC and Gibbs sampling algorithms conceptually but does not include structured pseudocode or algorithm blocks with specific labels or formatting. |
| Open Source Code | Yes | Appendices, code, and raw data are hosted at https://doi.org/10.17605/OSF.IO/RZK4S. |
| Open Datasets | Yes | Following [50], we apply this approach to the generative adversarial network Style GAN [51, 52], pretrained on the FFHQ dataset of faces from Flickr [51], and applying PCA to the intermediate latent code (termed w in the original papers). We began with three sentences from the Harvard sentence corpus [35] recorded by a female speaker [36], chosen to facilitate comparison with previous research; these sentences are phonologically balanced and semantically neutral. |
| Dataset Splits | No | The paper describes "validation experiments" involving new participant groups rating generated samples, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or sample counts) for the data used in the experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Style GAN but does not provide specific version numbers for any software dependencies, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | Each method was evaluated using across-participant chains of length 30, with five chains per color category, with each chain s starting location sampled from a uniform distribution over the color space (Exp. 1a, 1b, 1c). We constructed 18 across-participant GSP chains of length 50 with uniformly sampled starting locations and three chains for each adjective (Fig. 4A, Exp. 4a). We used 293 US participants from AMT, aggregating 5 trials per iteration using the arithmetic mean. |