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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 time-lagged 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 subject’s 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.
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1 Trinity College Dublin, School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Dublin 2, Ireland (GRID:grid.8217.c) (ISNI:0000 0004 1936 9705)
2 PSL University, CNRS, Laboratoire des Systèmes Perceptifs, Département d’études Cognitives, École Normale Supérieure, Paris, France (GRID:grid.4444.0) (ISNI:0000 0001 2112 9282)
3 University of Rochester, Department of Biomedical Engineering, Rochester, USA (GRID:grid.16416.34) (ISNI:0000 0004 1936 9174); University of Rochester, Department of Neuroscience, and Del Monte Institute for Neuroscience, Rochester, USA (GRID:grid.16416.34) (ISNI:0000 0004 1936 9174)
4 University of Groningen, Department of Neurolinguistics and Language Development, Groningen, The Netherlands (GRID:grid.4830.f) (ISNI:0000 0004 0407 1981)
5 Trinity College Dublin, School of Engineering, Trinity Centre for Bioengineering and Trinity College Institute of Neuroscience, Dublin 2, Ireland (GRID:grid.8217.c) (ISNI:0000 0004 1936 9705); University of Rochester, Department of Biomedical Engineering, Rochester, USA (GRID:grid.16416.34) (ISNI:0000 0004 1936 9174); University of Rochester, Department of Neuroscience, and Del Monte Institute for Neuroscience, Rochester, USA (GRID:grid.16416.34) (ISNI:0000 0004 1936 9174)