Fixation identification: the optimum threshold for a dispersion algorithm.
It is hypothesized that the number, position, size, and duration of fixations are functions of the metric used for dispersion in a dispersion-based fixation detection algorithm, as well as of the threshold value. The sensitivity of the I-DT algorithm for the various independent variables was determined through the analysis of gaze data from chess players during a memory recall experiment. A procedure was followed in which scan paths were generated at distinct intervals in a range of threshold values for each of five different metrics of dispersion. The percentage of points of regard (PORs) used, the number of fixations returned, the spatial dispersion of PORs within fixations, and the difference between the scan paths were used as indicators to determine an optimum threshold value. It was found that a fixation radius of 1 degrees provides a threshold that will ensure replicable results in terms of the number and position of fixations while utilizing about 90% of the gaze data captured.
Department of Computer Science and Informatics, University of the Free State, Bloemfontein, South Africa. pieterb.sci@ufs.ac.za
Pieter
P
Blignaut
D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001288:Attention; D003196:Computer Graphics; D004192:Discrimination, Psychological; D005260:Female; D005403:Fixation, Ocular; D006801:Humans; D008297:Male; D008570:Memory, Short-Term; D008875:Middle Aged; D009949:Orientation; D010364:Pattern Recognition, Visual; D011597:Psychomotor Performance; D011601:Psychophysics; D011930:Reaction Time; D012438:Saccades; D012684:Sensory Thresholds; D055815:Young Adult