1/fNeural Noise and Electrophysiological Indices of Contextual Prediction in Normative Aging

Prediction during language comprehension has increasingly been suggested to play a substantive role in efficient language processing. Emerging models have postulated that predictive mechanisms are enhanced when neural networks fire synchronously, but to date, this relationship has been investigated primarily through oscillatory activity in narrow frequency bands. A recently-developed measure proposed to reflect broadband neural activity – and thereby synchronous neuronal firing – is 1/ f neural noise extracted from EEG spectral power. Previous research (Voytek et al., 2015) has indicated that this measure of 1/ f neural noise changes across the lifespan, and these neural changes predict age-related behavioral impairments in visual working memory. Using a cross-sectional sample of young and older adults, we examined age-related changes in 1/ f neural noise and whether this measure would predict ERP correlates of successful lexical prediction during discourse comprehension. 1/ f neural noise across two different language tasks revealed high within-subject correlations, indicating that this measure can provide a reliable index of individualized patterns of neural activation. In addition to age, 1/ f noise was a significant predictor of N400 effects of successful lexical prediction, but noise did not mediate age-related declines in other ERP effects. We discuss broader implications of these findings for theories of predictive processing, as well as potential applications of 1/ f noise across research populations.