# Guiding Machine Perception with Psychophysics

arXiv: https://arxiv.org/abs/2207.02241 · HTML: https://mlsanity.com/paper/2207.02241/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Generalisation in humans and deep neural networks](https://mlsanity.com/paper/1808.08750/) (2018)
- [Deep Learning for Medical Image Processing: Overview, Challenges and Future](https://mlsanity.com/paper/1704.06825/) (2017)
- [The Limits and Potentials of Deep Learning for Robotics](https://mlsanity.com/paper/1804.06557/) (2018)
- [AGIL: Learning Attention from Human for Visuomotor Tasks](https://mlsanity.com/paper/1806.03960/) (2018)
- [Visual Psychophysics for Making Face Recognition Algorithms More Explainable](https://mlsanity.com/paper/1803.07140/) (2018)
- [PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition](https://mlsanity.com/paper/1611.06448/) (2016)
- [Measuring Human Perception to Improve Handwritten Document Transcription](https://mlsanity.com/paper/1904.03734/) (2019)
- [Predicting First Impressions with Deep Learning](https://mlsanity.com/paper/1610.08119/) (2016)

## Built on by (most cited first, 3 of 3)

- [Using Human Perception to Regularize Transfer Learning](https://mlsanity.com/paper/2211.07885/) (2022)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [Psychophysical Machine Learning](https://mlsanity.com/paper/2208.11236/) (2022)

## Abstract (arXiv)

Gustav Fechner's 1860 delineation of psychophysics, the measurement of sensation in relation to its stimulus, is widely considered to be the advent of modern psychological science. In psychophysics, a researcher parametrically varies some aspects of a stimulus, and measures the resulting changes in a human subject's experience of that stimulus; doing so gives insight to the determining relationship between a sensation and the physical input that evoked it. This approach is used heavily in perceptual domains, including signal detection, threshold measurement, and ideal observer analysis. Scientific fields like vision science have always leaned heavily on the methods and procedures of psychophysics, but there is now growing appreciation of them by machine learning researchers, sparked by widening overlap between biological and artificial perception citerojas2011automatic, scheirer2014perceptual,escalera2014chalearn,zhang2018agil, grieggs2021measuring. Machine perception that is guided by behavioral measurements, as opposed to guidance restricted to arbitrarily assigned human labels, has significant potential to fuel further progress in artificial intelligence.