Cross-modal transfer of statistical information benefits from sleep. (May 2016)

• Record Type: Journal Article
• Authors: Durrant, Simon J.; Cairney, Scott A.; Lewis, Penelope A.
• Is Part Of: Cortex. Volume 78(2016)
• Page Start: 85
• Page End: 99
• Publication Date: 2016-05
• Subjects: Consolidation -- Cross-modal -- Sleep -- Abstraction -- Transfer; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2016.02.011
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: 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.