Identifying steep psychometric function slope quickly in clinical applications.
Knowledge of an observer's psychometric function slope is potentially useful in clinical visual psychophysics (for example, perimetry), however, the short test times necessary in a clinical setting typically prevent slope estimation. We explore, using computer simulation, the performance of several possible procedures for estimating psychometric function slope within limited presentations (aiming for approximately 30 or 140 trials). Procedures were based on either adaptive staircase or Bayesian techniques, and performance was compared to a Method of Constant Stimuli. An adaptation of the Ψ algorithm was best performing, being able to reliably identify steep from flat psychometric functions in less than 30 presentations, however reliable quantification of shallow psychometric functions was not possible.
Turpin
Andrew
A
School of Computer Science and Information Technology, RMIT University Melbourne, Vic, Australia.
Jankovic
Darko
D
McKendrick
Allison
A
D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D011594:Psychometrics / Q000379:methods*; D012684:Sensory Thresholds; D014796:Visual Perception / Q000502:physiology*