Model-free estimation of the psychometric function

Kamilazy Zychaluk, David H. Foster

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    Abstract

    A subject's response to the strength of a stimulus is described by the psychometric function, from which summary measures, such as a threshold or a slope, may be derived. Traditionally, this function is estimated by fitting a parametric model to the experimental data, usually the proportion of successful trials at each stimulus level. Common models include the Gaussian and Weibull cumulative distribution functions. This approach works well if the model is correct, but it can mislead if not. In practice, the correct model is rarely known. Here, a nonparametric approach based on local linear fitting is advocated. No assumption is made about the true model underlying the data, except that the function is smooth. The critical role of the bandwidth is identified, and its optimum value is estimated by a cross-validation procedure. As a demonstration, seven vision and hearing data sets were fitted by the local linear method and by several parametric models. The local linear method frequently performed better and never worse than the parametric ones. Supplemental materials for this article can be downloaded from app.psychonomic-journals.org/content/supplemental. © 2009 The Psychonomic Society, Inc.
    Original languageEnglish
    Pages (from-to)1414-1425
    Number of pages11
    JournalAttention, Perception, and Psychophysics
    Volume71
    Issue number6
    DOIs
    Publication statusPublished - Aug 2009

    Keywords

    • SMOOTHING PARAMETER
    • DENSITY-ESTIMATION
    • BOOTSTRAP CHOICE
    • REGRESSION
    • MONOTONICITY
    • FREQUENCY
    • RETINA
    • MODELS

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