Rapid adaptation of predictive models during language comprehension: Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating

Abstract

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.

Effects of neural noise on predictive model updating across the adult lifespan

“…We thus utilised ERP estimates derived from the oddball paradigm as individual differences measures for the language processing task, supplementing resting-state aperiodic slope and intercept as our main individual differences measures of interest. We also examined peak alpha frequency (Individual Alpha Frequency, IAF) and Idea Density (ID), to allow for comparisons with our previous work on young adults (Bornkessel-Schlesewsky et al, 2022), as well as measures of cognitive ability and language proficiency.…”

“…Participants performed a naturalistic auditory language comprehension task, in which we measured the adaptation of individual brain responses to speakers’ word order idiosyncrasies using the trial-by-trial-based measure of speaker-based surprisal at the level of adjective clusters (cf. Bornkessel-Schlesewsky et al, 2022). As hypothesised, the N400 ERP response’s degree of attunement to speaker-based surprisal was reduced with increasing age: young adults showed a strong alignment between N400 amplitude and speaker-based surprisal by the end of the experimental session; this was reduced for middle-aged adults and absent for older adults.…”

“…A possible implication of these neurophysiological correlates of ageing that has hitherto not been examined is that they may be accompanied by systematic age-related changes in predictive model updating. Recent work on individual differences in predictive processing in young adults suggests that individuals with steeper resting aperiodic slopes adapt their predictive models more rapidly to novel input during language comprehension (Bornkessel-Schlesewsky et al, 2022). Steeper on-task aperiodic slopes have likewise been argued to accompany stronger predictive language processing in both younger and older adults (Dave et al, 2018) and to be conducive for language learning in adulthood (Cross et al, 2022).…”

“…Here, we test the hypothesis that older adults adapt their predictive models more slowly to novel input environments and examine how this relates to aperiodic activity. We employed the same design as in Bornkessel-Schlesewsky et al (2022, Experiment 2) with a sample of 120 participants spanning the adult lifespan (18 to 83 years of age). Participants listened to 150 short (4-5 sentence) passages recorded by two male speakers.…”

“…“huge grey elephant”), with one speaker producing a higher (70:30%) ratio of expected-to-unexpected adjective orders (“huge grey” vs. “grey huge”) and the ratio being reversed for the other speaker. Using a novel measure of “speaker-based surprisal” first introduced in Bornkessel-Schlesewsky et al (2022), we tracked the expectedness of adjective orders within the experiment as they unfolded on a trial-by-trial basis. As in our previous study, our primary outcome measure was the N400 event-related potential (ERP) as a presumed index of precision-weighted prediction error in language processing (Bornkessel- Schlesewsky & Schlesewsky, 2019).…”