Durrant Simon J, Cairney Scott A, Lewis Penelope A
School of Psychology, University of Lincoln, Lincoln, United Kingdom.
Department of Psychology, University of York, United Kingdom.
Cortex. 2016 May;78:85-99. doi: 10.1016/j.cortex.2016.02.011. Epub 2016 Feb 27.
Extracting regularities from a sequence of events is essential for understanding our environment. However, there is no consensus regarding the extent to which such regularities can be generalised beyond the modality of learning. One reason for this could be the variation in consolidation intervals used in different paradigms, also including an opportunity to sleep. Using a novel statistical learning paradigm in which structured information is acquired in the auditory domain and tested in the visual domain over either 30 min or 24 h consolidation intervals, we show that cross-modal transfer can occur, but this transfer is only seen in the 24 h group. Importantly, the extent of cross-modal transfer is predicted by the amount of slow wave sleep (SWS) obtained. Additionally, cross-modal transfer is associated with the same pattern of decreasing medial temporal lobe and increasing striatal involvement which has previously been observed to occur across 24 h in unimodal statistical learning. We also observed enhanced functional connectivity after 24 h in a network of areas which have been implicated in cross-modal integration including the precuneus and the middle occipital gyrus. Finally, functional connectivity between the striatum and the precuneus was also enhanced, and this strengthening was predicted by SWS. These results demonstrate that statistical learning can generalise to some extent beyond the modality of acquisition, and together with our previously published unimodal results, support the notion that statistical learning is both domain-general and domain-specific.
从一系列事件中提取规律对于理解我们的环境至关重要。然而,对于这些规律能够在学习方式之外进行多大程度的泛化,目前尚无共识。造成这种情况的一个原因可能是不同范式中使用的巩固间隔存在差异,其中也包括睡眠的机会。我们采用了一种新颖的统计学习范式,即在听觉领域获取结构化信息,并在30分钟或24小时的巩固间隔后在视觉领域进行测试,结果表明跨模态转移是可以发生的,但这种转移仅在24小时组中出现。重要的是,跨模态转移的程度由获得的慢波睡眠(SWS)量预测。此外,跨模态转移与内侧颞叶减少和纹状体参与增加的相同模式相关,这一模式先前在单模态统计学习的24小时过程中也有观察到。我们还观察到,在24小时后,包括楔前叶和枕中回在内的与跨模态整合相关的区域网络中的功能连接增强。最后,纹状体与楔前叶之间的功能连接也增强了,并且这种增强由慢波睡眠预测。这些结果表明,统计学习可以在一定程度上超越获取方式进行泛化,并且与我们之前发表的单模态结果一起,支持了统计学习既是领域通用又是领域特定的观点。