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NEST: Neural estimation by sequential testing.

Sjoerd Bruin¹, Jiří Kosinka¹, Cara Tursun¹

¹Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747AG, The Netherlands.

Vision Research

|October 17, 2025

Summary

This study introduces a new adaptive psychophysical procedure using neural networks to estimate complex psychometric functions efficiently. The novel method outperforms existing techniques, reducing experiment time for psychophysical measurements.

Keywords:

Active learning Adaptive procedures Experimental design Neural networks Psychophysics

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Area of Science:

Psychophysics
Computational Neuroscience
Machine Learning

Background:

Adaptive psychophysical procedures enhance measurement efficiency and reliability.
Estimating multi-dimensional psychometric functions is challenging due to increasing experiment complexity.
Parametric techniques require prior knowledge of the psychometric function, while non-parametric methods like Gaussian processes are sensitive to kernel selection.

Purpose of the Study:

To develop a more efficient and reliable adaptive psychophysical procedure for estimating multi-dimensional psychometric functions.
To overcome limitations of existing methods, particularly when prior information about the psychometric function is limited.
To introduce a novel acquisition function for stimulus selection in adaptive procedures.

Main Methods:

Utilized a neural network as the core estimator for the psychometric function.
Introduced a novel acquisition function designed for optimal stimulus selection.
Benchmarked the proposed method through extensive simulations and real-world psychovisual experiments.

Main Results:

The proposed neural network-based method demonstrated superior performance compared to state-of-the-art techniques.
The new approach eliminated the need for manual kernel function selection, a common challenge in Gaussian process methods.
Significant reduction in experiment duration was observed, indicating increased efficiency.

Conclusions:

The novel adaptive psychophysical procedure offers a robust and efficient solution for estimating complex psychometric functions.
This method is particularly advantageous in scenarios with limited prior knowledge of the underlying psychometric function.
The findings suggest a promising direction for advancing adaptive psychophysical measurement techniques.