Fründ Ingo, Haenel N Valentin, Wichmann Felix A
Modellierung Kognitiver Prozesse, Technische Universität Berlin and Bernstein Center for Computational Neuroscience, Berlin, Germany.
J Vis. 2011 May 23;11(6):16. doi: 10.1167/11.6.16.
Measuring sensitivity is at the heart of psychophysics. Often, sensitivity is derived from estimates of the psychometric function. This function relates response probability to stimulus intensity. In estimating these response probabilities, most studies assume stationary observers: Responses are expected to be dependent only on the intensity of a presented stimulus and not on other factors such as stimulus sequence, duration of the experiment, or the responses on previous trials. Unfortunately, a number of factors such as learning, fatigue, or fluctuations in attention and motivation will typically result in violations of this assumption. The severity of these violations is yet unknown. We use Monte Carlo simulations to show that violations of these assumptions can result in underestimation of confidence intervals for parameters of the psychometric function. Even worse, collecting more trials does not eliminate this misestimation of confidence intervals. We present a simple adjustment of the confidence intervals that corrects for the underestimation almost independently of the number of trials and the particular type of violation.
测量敏感度是心理物理学的核心。通常，敏感度是从心理测量函数的估计值中得出的。该函数将反应概率与刺激强度联系起来。在估计这些反应概率时，大多数研究假定观察者状态稳定：预期反应仅取决于所呈现刺激的强度，而不取决于其他因素，如刺激序列、实验持续时间或先前试验的反应。不幸的是，诸如学习、疲劳或注意力和动机的波动等多种因素通常会导致这一假设被违背。这些违背的严重程度尚不清楚。我们使用蒙特卡罗模拟来表明，违背这些假设会导致心理测量函数参数的置信区间被低估。更糟糕的是，收集更多试验并不能消除这种对置信区间的错误估计。我们提出了一种对置信区间的简单调整方法，该方法几乎与试验次数和违背的具体类型无关，可校正这种低估。
