title: Small-sample characterization of stochastic approximation staircases in forced-choice adaptive threshold estimation.
abstract: Despite the widespread use of up-down staircases in adaptive threshold estimation, their efficiency and usability in forced-choice experiments has been recently debated. In this study, simulation techniques were used to determine the small-sample convergence properties of stochastic approximation (SA) staircases as a function of several experimental parameters. We found that satisfying some general requirements (use of the accelerated SA algorithm, clear suprathreshold initial stimulus intensity, large initial step size) the convergence was accurate independently of the spread of the underlying psychometric function. SA staircases were also reliable for targeting percent-correct levels far from the midpoint of the psychometric function and performed better than classical up-down staircases with fixed step size. These results prompt the utilization of SA staircases in practical forced-choice estimation of sensory thresholds.
authors.affiliation: Dipartimento di Fisica, Università di Trento, Povo, Trento, Italy. faes@science.unitn.it
authors.forename: Luca
authors.initials: L
authors.lastname: Faes
authors.forename: Giandomenico
authors.initials: G
authors.lastname: Nollo
authors.forename: Flavia
authors.initials: F
authors.lastname: Ravelli
authors.forename: Leonardo
authors.initials: L
authors.lastname: Ricci
authors.forename: Massimo
authors.initials: M
authors.lastname: Vescovi
authors.forename: Massimo
authors.initials: M
authors.lastname: Turatto
authors.forename: Francesco
authors.initials: F
authors.lastname: Pavani
authors.forename: Renzo
authors.initials: R
authors.lastname: Antolini
mesh_terms: D002755:Choice Behavior; D003068:Coercion; D004056:Differential Threshold; D006801:Humans; D011584:Psychology / Q000706:statistics & numerical data; D012677:Sensation / Q000502:physiology*; D013269:Stochastic Processes