Generating customised experimental stimuli for visual search using Genetic Algorithms shows evidence for a continuum of search efficiency.
Here we present a new empirically validated computational paradigm that generates on demand stimuli with user-defined levels of salience for use in visual search tasks. Combining a Genetic Algorithm with a biologically motivated model for image saliency we are able to breed a range of customised texture elements for use in psychophysical experiments. We review these psychophysical studies, showing for the first time that an explicit and predictable continuum of search efficiency exists in human visual search.
Verma
Milan
M
Queen Mary University of London, School of Electronic Engineering and Computer Science, London, UK. milan@dcs.qmul.ac.uk
McOwan
Peter W
PW
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001288:Attention; D005260:Female; D005364:Field Dependence-Independence; D006801:Humans; D008297:Male; D008960:Models, Psychological; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation / Q000379:methods*; D011601:Psychophysics; D055815:Young Adult