Observation of time-dependent psychophysical functions and accounting for threshold drifts

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    Methods to obtain estimates of psychophysical functions are used in numerous fields, such as audiology, vision, and pain. Neurophysiological and psychological processes underlying this function are assumed to remain stationary throughout a psychophysical experiment. However, violation of this assumption (e.g., due to habituation or changing decisional factors) likely affects the estimates of psychophysical parameters. We used computer simulations to study how non-stationary processes, resulting in a time-dependent psychophysical function, affect threshold and slope estimates. Moreover, we propose methods to improve the estimation quality when stationarity is violated. A psychophysical detection experiment was modeled as a stochastic process ruled by a logistic psychophysical function. The threshold was modeled to drift over time and was defined as either a linear or nonlinear function. Threshold and slope estimates were obtained by using three estimation procedures: a static procedure assuming stationarity, a relaxed procedure accounting for linear effects of time, and a threshold tracking paradigm. For illustrative purposes, data acquired from two human subjects were used to estimate their thresholds and slopes using all estimation procedures. Threshold estimates obtained by all estimations procedures were similar to the mean true threshold. However, due to threshold drift, the slope was underestimated by the static procedure. The relaxed procedure only underestimated the slope when the threshold drifted nonlinearly over time. The tracking paradigm performed best and therefore, we recommend using the tracking paradigm in human psychophysical detection experiments to obtain estimates of the threshold and slope and to identify the mode of non-stationarity.
    Original languageEnglish
    Pages (from-to)1440-1447
    Number of pages8
    JournalAttention, perception & psychophysics
    Issue number4
    Publication statusPublished - May 2015


    • BSS-Central mechanisms underlying chronic pain
    • Slope
    • Psychometrics
    • Tracking
    • Non-stationarity
    • Threshold
    • Simulations


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