Predictive coding provides a compelling, unified theory of neural information processing, including for language. However, there is insufficient understanding of how predictive models adapt to changing contextual and environmental demands and the extent to which such adaptive processes differ between individuals. Here, we used electroencephalography (EEG) to track prediction error responses during a naturalistic language processing paradigm. In Experiment 1, 45 native speakers of English listened to a series of short passages. Via a speaker manipulation, we introduced changing intra-experimental adjective order probabilities for two-adjective noun phrases embedded within the passages and investigated whether prediction error responses adapt to reflect these intra-experimental predictive contingencies. To this end, we calculated a novel measure of speaker-based, intra-experimental surprisal (“speaker-based surprisal”) as defined on a trial-by-trial basis and by clustering together adjectives with a similar meaning. N400 amplitude at the position of the critical second adjective was used as an outcome measure of prediction error. Results showed that N400 responses attuned to speaker-based surprisal over the course of the experiment, thus indicating that listeners rapidly adapt their predictive models to reflect local environmental contingencies (here: the probability of one type of adjective following another when uttered by a particular speaker). Strikingly, this occurs in spite of the wealth of prior linguistic experience that participants bring to the laboratory. Model adaptation effects were strongest for participants with a steep aperiodic (1/f) slope in resting EEG and low individual alpha frequency (IAF), with idea density (ID) showing a more complex pattern. These results were replicated in a separate sample of 40 participants in Experiment 2, which employed a highly similar design to Experiment 1. Overall, our results suggest that individuals with a steep aperiodic slope adapt their predictive models most strongly to context-specific probabilistic information. Steep aperiodic slope is thought to reflect low neural noise, which in turn may be associated with higher neural gain control and better cognitive control. Individuals with a steep aperiodic slope may thus be able to more effectively and dynamically reconfigure their prediction-related neural networks to meet current task demands. We conclude that predictive mechanisms in language are highly malleable and dynamic, reflecting both the affordances of the present environment as well as intrinsic information processing capabilities of the individual.

In the perceptual and sensorimotor domains, ageing is accompanied by a stronger reliance on top-down predictive model information and reduced sensory learning, thus promoting simpler, more efficient internal models in older adults. Here, we demonstrate analogous effects in higher-order language processing. One-hundred and twenty adults ranging in age from 18 to 83 years listened to short auditory passages containing manipulations of adjective order, with order probabilities varying between two speakers. As a measure of model adaptation, we examined attunement of the N400 event-related potential, a measure of precision-weighted prediction errors in language, to a trial-by-trial measure of speaker-based adjective order expectedness (``speaker-based surprisal") across the course of the experiment. Adaptation was strongest for young adults, weaker for middle-aged adults, and absent for older adults. Over and above age-related differences, we observed individual differences in model adaptation, with aperiodic (1/f) slope and intercept metrics derived from resting-state EEG showing the most pronounced modulations. We suggest that age-related changes in aperiodic slope, which have been linked to neural noise, may be associated with individual differences in the magnitude of stimulus-related prediction error signals. By contrast, changes in aperiodic intercept, which reflects aggregate population spiking, may relate to an individual's updating of inferences regarding stimulus precision. These two mechanisms jointly contribute to age-related changes in the precision-weighting of prediction errors and the degree of sensory learning.