Inferring characteristics of stimulus encoding mechanisms using rippled noise stimuli

Abstract

Several psychophysical studies have used masking techniques to infer characteristics of stimulus encoding mechanisms underlying early visual processing. These studies typically suggest the existence of multiple frequency-and orientation-selective filters or ‘channels’. To evaluate the usefulness of such a multiple channel encoding front-end in more general models of pattern vision, knowledge about various channel properties is required. Notably, estimates of channel characteristics such as shape and bandwidth vary considerably among studies. One problem is that inferring encoding mechanism characteristics requires (often unwarranted) assumptions regarding various aspects of the visual system (eg, linearity of contrast processing). Differences in estimates of the channels may reveal important nonlinearities that need to be taken into account. In the present study, we start from reported channel characteristics …

Matthias Bethge
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center

Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.