Identifying steep psychometric function slope quickly in clinical applications |
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Authors: | Andrew Turpin Darko Jankovic |
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Institution: | a Department of Optometry & Vision Sciences, The University of Melbourne Carlton, Vic 3053, Australia b School of Computer Science and Information Technology, RMIT University Melbourne, Vic, Australia c Department of Computer Science and Software Engineering, The University of Melbourne, Parkville, Vic 3010, Australia |
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Abstract: | 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. |
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Keywords: | Psychophysical methods Threshold estimates Adaptive procedures Simulation techniques |
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