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儿童心理物理学:评估4至15岁儿童中最大似然估计器的使用(QUEST+)。

Psychophysics with children: Evaluating the use of maximum likelihood estimators in children aged 4-15 years (QUEST+).

作者信息

Farahbakhsh Mahtab, Dekker Tessa M, Jones Pete R

机构信息

Child Vision Lab, Institute of Ophthalmology, University College London (UCL), London, UK.

UCL Division of Psychology and Language Sciences, London, UK.

出版信息

J Vis. 2019 Jun 3;19(6):22. doi: 10.1167/19.6.22.

Abstract

Maximum Likelihood (ML) estimators such as QUEST+ allow complex psychophysical measurements to be made more quickly and precisely than traditional staircase techniques. They could therefore be useful for quantifying sensory function in populations with limited attention spans, such as children. To test this, the present study empirically evaluated the performance of an ML estimator (QUEST+) versus a traditional Up-Down Weighted Staircase in children and adults. Seventy-one children (4.7-14.7 years) and 43 adults (18.1-29.6 years) completed a typical psychophysical procedure: Contrast Sensitivity Function (CSF) determination. Some participants were tested twice with the same method (QUEST+ or Staircase), allowing test-retest repeatability to be quantified. Others were tested once each with either method (QUEST+ and Staircase), allowing accuracy to be quantified. The results showed that QUEST+ was more efficient: In both children and adults, approximately half the number of ML trials were required to attain comparable levels of accuracy and reliability as a traditional Staircase paradigm, and plausible CSF estimates could be made in even the youngest children. The ML procedure was also as robust as the Staircase to lapses in concentration, and its performance did not depend on prespecifying correct model priors. The results show that ML estimators could greatly improve our ability to study sensory processes and detect impairments in children, although important practical considerations for-and-against their use are discussed.

摘要

诸如QUEST+之类的最大似然(ML)估计器能让复杂的心理物理学测量比传统的阶梯法更快、更精确地完成。因此,它们对于量化注意力持续时间有限的人群(如儿童)的感觉功能可能很有用。为了验证这一点,本研究通过实证评估了ML估计器(QUEST+)与传统的上下加权阶梯法在儿童和成人中的表现。71名儿童(4.7 - 14.7岁)和43名成人(18.1 - 29.6岁)完成了一项典型的心理物理学程序:对比敏感度函数(CSF)测定。一些参与者用相同的方法(QUEST+或阶梯法)进行了两次测试,从而可以量化重测的可重复性。其他参与者分别用两种方法(QUEST+和阶梯法)各测试了一次,从而可以量化准确性。结果表明,QUEST+更有效:在儿童和成人中,达到与传统阶梯范式相当的准确性和可靠性水平所需的ML试验次数大约只有一半,而且即使是最小的儿童也能得出合理的CSF估计值。ML程序在注意力不集中方面也与阶梯法一样稳健,其性能不依赖于预先设定正确的模型先验。结果表明,ML估计器可以大大提高我们研究儿童感觉过程和检测损伤的能力,不过也讨论了支持和反对使用它们的重要实际考虑因素。

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