title: Measuring, estimating, and understanding the psychometric function: a commentary.
abstract: The psychometric function, relating the subject's response to the physical stimulus, is fundamental to psychophysics. This paper examines various psychometric function topics, many inspired by this special symposium issue of Perception & Psychophysics: What are the relative merits of objective yes/no versus forced choice tasks (including threshold variance)? What are the relative merits of adaptive versus constant stimuli methods? What are the relative merits of likelihood versus up-down staircase adaptive methods? Is 2AFC free of substantial bias? Is there no efficient adaptive method for objective yes/no tasks? Should adaptive methods aim for 90% correct? Can adding more responses to forced choice and objective yes/no tasks reduce the threshold variance? What is the best way to deal with lapses? How is the Weibull function intimately related to the d' function? What causes bias in the likelihood goodness-of-fit? What causes bias in slope estimates from adaptive methods? How good are nonparametric methods for estimating psychometric function parameters? Of what value is the psychometric function slope? How are various psychometric functions related to each other? The resolution of many of these issues is surprising.
authors.affiliation: School of Optometry, University of California, Berkeley 94720-2020, USA.
authors.forename: S A
authors.initials: SA
authors.lastname: Klein
mesh_terms: D015982:Bias; D006801:Humans; D011594:Psychometrics / Q000379:methods*; D011601:Psychophysics / Q000706:statistics & numerical data*; D012684:Sensory Thresholds