Rinderknecht Mike D, Ranzani Raffaele, Popp Werner L, Lambercy Olivier, Gassert Roger
Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Lengghalde 5, 8092, Zurich, Switzerland.
Balgrist University Hospital, Spinal Cord Injury Center, Forchstrasse 340, 8008, Zurich, Switzerland.
Atten Percept Psychophys. 2018 Aug;80(6):1629-1645. doi: 10.3758/s13414-018-1521-z.
Psychophysical procedures are applied in various fields to assess sensory thresholds. During experiments, sampled psychometric functions are usually assumed to be stationary. However, perception can be altered, for example by loss of attention to the presentation of stimuli, leading to biased data, which results in poor threshold estimates. The few existing approaches attempting to identify non-stationarities either detect only whether there was a change in perception, or are not suitable for experiments with a relatively small number of trials (e.g., [Formula: see text] 300). We present a method to detect inattention periods on a trial-by-trial basis with the aim of improving threshold estimates in psychophysical experiments using the adaptive sampling procedure Parameter Estimation by Sequential Testing (PEST). The performance of the algorithm was evaluated in computer simulations modeling inattention, and tested in a behavioral experiment on proprioceptive difference threshold assessment in 20 stroke patients, a population where attention deficits are likely to be present. Simulations showed that estimation errors could be reduced by up to 77% for inattentive subjects, even in sequences with less than 100 trials. In the behavioral data, inattention was detected in 14% of assessments, and applying the proposed algorithm resulted in reduced test-retest variability in 73% of these corrected assessments pairs. The novel algorithm complements existing approaches and, besides being applicable post hoc, could also be used online to prevent collection of biased data. This could have important implications in assessment practice by shortening experiments and improving estimates, especially for clinical settings.
心理物理学程序被应用于各个领域以评估感觉阈值。在实验过程中，采样的心理测量函数通常被假定为平稳的。然而，感知可能会发生改变，例如由于对刺激呈现的注意力丧失，导致数据有偏差，从而产生较差的阈值估计。现有的少数试图识别非平稳性的方法，要么只能检测感知是否发生了变化，要么不适用于试验次数相对较少的实验（例如，[公式：见正文]300次）。我们提出了一种逐次试验检测注意力不集中时段的方法，目的是在使用顺序测试参数估计（PEST）自适应采样程序的心理物理学实验中改进阈值估计。该算法的性能在模拟注意力不集中的计算机模拟中进行了评估，并在20名中风患者的本体感觉差异阈值评估行为实验中进行了测试，中风患者群体很可能存在注意力缺陷。模拟结果表明，即使在试验次数少于100次的序列中，注意力不集中的受试者的估计误差也可降低多达77%。在行为数据中，在14%的评估中检测到了注意力不集中，应用所提出的算法后，在这些校正后的评估对中，73%的测试 - 重测变异性降低。这种新算法补充了现有方法，并且除了可事后应用外，还可在线使用以防止收集有偏差的数据。这对于缩短实验和改进估计，特别是在临床环境中，在评估实践中可能具有重要意义。
