# Gibbs Sampling with People

arXiv: https://arxiv.org/abs/2008.02595 · HTML: https://mlsanity.com/paper/2008.02595/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 28 of 28)

- [Getting aligned on representational alignment](https://mlsanity.com/paper/2310.13018/) (2023)
- [Large language models predict human sensory judgments across six modalities](https://mlsanity.com/paper/2302.01308/) (2023)
- [On Benchmarking Human-Like Intelligence in Machines](https://mlsanity.com/paper/2502.20502/) (2025)
- [Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines](https://mlsanity.com/paper/2205.11558/) (2022)
- [Enhancing Zero-shot Text-to-Speech Synthesis with Human Feedback](https://mlsanity.com/paper/2406.00654/) (2024)
- [Words are all you need? Language as an approximation for human similarity judgments](https://mlsanity.com/paper/2206.04105/) (2022)
- [On the Informativeness of Supervision Signals](https://mlsanity.com/paper/2211.01407/) (2022)
- [Probing BERT's priors with serial reproduction chains](https://mlsanity.com/paper/2202.12226/) (2022)
- [Predicting Human Similarity Judgments Using Large Language Models](https://mlsanity.com/paper/2202.04728/) (2022)
- [How Individual Traits and Language Styles Shape Preferences In Open-ended User-LLM Interaction: A Preliminary Study](https://mlsanity.com/paper/2504.17083/) (2025)
- [Exploring emotional prototypes in a high dimensional TTS latent space](https://mlsanity.com/paper/2105.01891/) (2021)
- [Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning](https://mlsanity.com/paper/2204.01437/) (2022)
- [Steering Risk Preferences in Large Language Models by Aligning Behavioral and Neural Representations](https://mlsanity.com/paper/2505.11615/) (2025)
- [The Universal Law of Generalization Holds for Naturalistic Stimuli](https://mlsanity.com/paper/2306.08564/) (2023)
- [Recovering Mental Representations from Large Language Models with Markov Chain Monte Carlo](https://mlsanity.com/paper/2401.16657/) (2024)
- [Characterizing Similarities and Divergences in Conversational Tones in Humans and LLMs by Sampling with People](https://mlsanity.com/paper/2406.04278/) (2024)
- [Why Human Guidance Matters in Collaborative Vibe Coding](https://mlsanity.com/paper/2602.10473/) (2026)
- [Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody](https://mlsanity.com/paper/2205.04820/) (2022)
- [Comparing Abstraction in Humans and Large Language Models Using Multimodal Serial Reproduction](https://mlsanity.com/paper/2402.03618/) (2024)
- [On Language Models' Sensitivity to Suspicious Coincidences](https://mlsanity.com/paper/2504.09387/) (2025)
- [Around the world in 60 words: A generative vocabulary test for online research](https://mlsanity.com/paper/2302.01614/) (2023)
- [Quantifying Human Priors over Social and Navigation Networks](https://mlsanity.com/paper/2402.18651/) (2024)
- [Are Expressions for Music Emotions the Same Across Cultures?](https://mlsanity.com/paper/2502.08744/) (2025)
- [Beyond the Horizon: Decoupling Multi-View UAV Action Recognition via Partial Order Transfer](https://mlsanity.com/paper/2504.20530/) (2025)
- [Probing Perceptual Priors of MLLMs via Gibbs Sampling with Interpretable Generative Controls](https://mlsanity.com/paper/2608.27727/) (2026)
- [A Human-in-the-Loop Approach to Improving Cross-Text Prosody Transfer](https://mlsanity.com/paper/2406.06601/) (2024)
- [Guided Data Generation for Understanding Model Behavior](https://mlsanity.com/paper/2502.06658/) (2025)
- [Integrating Contextual Embeddings into Evaluation of Expressive MIDI Piano Performances](https://mlsanity.com/paper/2607.27909/) (2026)

## Abstract (arXiv)

A core problem in cognitive science and machine learning is to understand how humans derive semantic representations from perceptual objects, such as color from an apple, pleasantness from a musical chord, or seriousness from a face. Markov Chain Monte Carlo with People (MCMCP) is a prominent method for studying such representations, in which participants are presented with binary choice trials constructed such that the decisions follow a Markov Chain Monte Carlo acceptance rule. However, while MCMCP has strong asymptotic properties, its binary choice paradigm generates relatively little information per trial, and its local proposal function makes it slow to explore the parameter space and find the modes of the distribution. Here we therefore generalize MCMCP to a continuous-sampling paradigm, where in each iteration the participant uses a slider to continuously manipulate a single stimulus dimension to optimize a given criterion such as 'pleasantness'. We formulate both methods from a utility-theory perspective, and show that the new method can be interpreted as 'Gibbs Sampling with People' (GSP). Further, we introduce an aggregation parameter to the transition step, and show that this parameter can be manipulated to flexibly shift between Gibbs sampling and deterministic optimization. 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 (StyleGAN), a promising approach for applying GSP to high-dimensional perceptual spaces. We conclude by discussing future cognitive applications and ethical implications.