# Adaptive Nonparametric Psychophysics

arXiv: https://arxiv.org/abs/2104.09549 · HTML: https://mlsanity.com/paper/2104.09549/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2021.

## Built on by (most cited first, 5 of 5)

- [qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization](https://mlsanity.com/paper/2303.15746/) (2023)
- [Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation](https://mlsanity.com/paper/2203.09751/) (2022)
- [Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences](https://mlsanity.com/paper/2306.06296/) (2023)
- [AExGym: Benchmarks and Environments for Adaptive Experimentation](https://mlsanity.com/paper/2408.04531/) (2024)
- [Dose-finding design based on level set estimation in phase I cancer clinical trials](https://mlsanity.com/paper/2504.09157/) (2025)

## Abstract (arXiv)

We introduce a new set of models and adaptive psychometric testing methods for multidimensional psychophysics. In contrast to traditional adaptive staircase methods like PEST and QUEST, the method is multi-dimensional and does not require a grid over contextual dimensions, retaining sub-exponential scaling in the number of stimulus dimensions. In contrast to more recent multi-dimensional adaptive methods, our underlying model does not require a parametric assumption about the interaction between intensity and the additional dimensions. In addition, we introduce a new active sampling policy that explicitly targets psychometric detection threshold estimation and does so substantially faster than policies that attempt to estimate the full psychometric function (though it still provides estimates of the function, albeit with lower accuracy). Finally, we introduce AEPsych, a user-friendly open-source package for nonparametric psychophysics that makes these technically-challenging methods accessible to the broader community.