Pinto Danna, Prior Anat, Zion Golumbic Elana
The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel.
Department of Learning Disabilities, University of Haifa, Haifa, Israel.
Neurobiol Lang (Camb). 2022 Feb 16;3(2):214-234. doi: 10.1162/nol_a_00061. eCollection 2022.
Statistical learning (SL) is hypothesized to play an important role in language development. However, the measures typically used to assess SL, particularly at the level of individual participants, are largely indirect and have low sensitivity. Recently, a neural metric based on frequency-tagging has been proposed as an alternative measure for studying SL. We tested the sensitivity of frequency-tagging measures for studying SL in individual participants in an artificial language paradigm, using non-invasive electroencephalograph (EEG) recordings of neural activity in humans. Importantly, we used carefully constructed controls to address potential acoustic confounds of the frequency-tagging approach, and compared the sensitivity of EEG-based metrics to both explicit and implicit behavioral tests of SL. Group-level results confirm that frequency-tagging can provide a robust indication of SL for an artificial language, above and beyond potential acoustic confounds. However, this metric had very low sensitivity at the level of individual participants, with significant effects found only in 30% of participants. Comparison of the neural metric to previously established behavioral measures for assessing SL showed a significant yet weak correspondence with performance on an implicit task, which was above-chance in 70% of participants, but no correspondence with the more common explicit 2-alternative forced-choice task, where performance did not exceed chance-level. Given the proposed ubiquitous nature of SL, our results highlight some of the operational and methodological challenges of obtaining robust metrics for assessing SL, as well as the potential confounds that should be taken into account when using the frequency-tagging approach in EEG studies.
统计学习（SL）被认为在语言发展中起着重要作用。然而，通常用于评估SL的方法，尤其是在个体参与者层面，大多是间接的，且灵敏度较低。最近，一种基于频率标记的神经指标被提出作为研究SL的替代方法。我们在人工语言范式中，使用人类神经活动的非侵入性脑电图（EEG）记录，测试了频率标记方法在研究个体参与者的SL时的灵敏度。重要的是，我们使用精心构建的对照来解决频率标记方法潜在的声学混淆问题，并将基于EEG的指标的灵敏度与SL的显性和隐性行为测试进行了比较。组水平的结果证实，频率标记可以为人工语言的SL提供有力的指示，超越潜在的声学混淆。然而，该指标在个体参与者层面的灵敏度非常低，仅在30%的参与者中发现了显著影响。将神经指标与先前建立的评估SL的行为测量方法进行比较，结果显示与一项隐性任务的表现存在显著但微弱的对应关系，该任务在70%的参与者中表现高于机会水平，但与更常见的显性二选一强制选择任务没有对应关系，该任务的表现未超过机会水平。鉴于SL被认为具有普遍存在的性质，我们的结果突出了获得用于评估SL的可靠指标的一些操作和方法挑战，以及在EEG研究中使用频率标记方法时应考虑的潜在混淆因素。
