Authors:
Ian Bright
and
Raymond Pavloski
Affiliation:
Indiana University of Pennsylvania, United States
Keyword(s):
Hard Problem, Neurotechnology, Qualia, Recurrent Neural Network, Tolerance Space, Topological Vision,
Visual Object Unity.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Instruments and Devices
;
Brain-Computer Interfaces
;
Devices
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Physiological Computing Systems
Abstract:
In response to a simulated retinal image of an object, the recurrent input to a richly connected artificial
neural network organizes into a connected open set (COS) of ionic conductance values, which models the
continuity and unity of a visual object. As the density of light dots on a dark background increases and then
decreases, a COS appears at a density that is higher than that at which it disappears (hysteresis). This
experiment tested the hypothesis that humans will show hysteresis similar to that of the simulation. In
addition, the effect of dot lightness on the perception of a unified visual object was also tested.