Review for NeurIPS paper: Gibbs Sampling with People

This paper introduces a new method for eliciting human representations of perceptual concepts, such as what RGB values people think correspond to the color "sunset" or what auditory dimensions (e.g. Rather than eliciting representations via guess-and-check (i.e., start with a dataset and then apply human-generated labels), this method (Gibbs Sampling with People, or GSP) enables inference to go in the other direction (i.e., start with labels, and then identify percepts that match those labels). GSP extends prior work (MCMC with People) to allow eliciting representations of much higher-dimensional stimuli. The reviewers unanimously praised this paper for tackling an important and relevant problem in cognitive science, for its breadth of empirical results, and for its novelty over prior work. R2 stated that the paper is "impressive in scale, scope, and results", R3 stated that it was "very relevant to the NeurIPS community and very novel", and R4 felt there could be "a potentially large impact of this work" with "substantial interest" amongst the NeurIPS community.