Scientific ReportsNeurosciencePeer-reviewed journal article
Dissociable electrophysiological measures of natural language processing reveal differences in comprehension strategy in healthy ageing
Michael P. BroderickiD, Giovanni M. Di LibertoiD, Andrew James AndersoniD, Adrià RofesiD, Edmund C. LaloriD
Abstract
Healthy ageing leads to changes in the brain that impact upon sensory and cognitive processing. It is not fully clear how these changes affect the processing of everyday spoken language. Prediction is thought to play an important role in language comprehension, where information about upcoming words is pre-activated across multiple representational levels. However, evidence from electrophysiology suggests differences in how older and younger adults use context-based predictions, particularly at the level of semantic representation. We investigate these differences during natural speech comprehension by presenting older and younger subjects with continuous, narrative speech while recording their electroencephalogram. We use linear regression to test how distinct computational measures of 1) semantic dissimilarity and 2) lexical surprisal are processed in the brains of both groups. Our results reveal dissociable neural correlates of these two measures that suggest differences in how younger and older adults successfully comprehend speech. Specifically, our results suggest that, while younger and older subjects both employ context-based lexical predictions, older subjects are significantly less likely to pre-activate the semantic features relating to upcoming words. Furthermore, across our group of older adults, we show that the weaker the neural signature of this semantic pre-activation mechanism, the lower a subjects semantic verbal fluency score. We interpret these findings as prediction playing a generally reduced role at a semantic level in the brains of older listeners during speech comprehension and that these changes may be part of an overall strategy to successfully comprehend speech with reduced cognitive resources.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 72
- FWCI
- 6.71
- Field percentile
- top 2%
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