학술논문

Informing Machine Perception With Psychophysics
Document Type
Periodical
Source
Proceedings of the IEEE Proc. IEEE Proceedings of the IEEE. 112(2):88-96 Feb, 2024
Subject
General Topics for Engineers
Engineering Profession
Aerospace
Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Fields, Waves and Electromagnetics
Geoscience
Nuclear Engineering
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Power, Energy and Industry Applications
Communication, Networking and Broadcast Technologies
Photonics and Electrooptics
Training
Computer science
Supervised learning
Psychology
Reinforcement learning
Oral communication
Observers
Language
ISSN
0018-9219
1558-2256
Abstract
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 into 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, such as 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 [1], [2], [3], [4], [5]. 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 (AI).