TY - JOUR
T1 - Rapid adaptation of predictive models during language comprehension
T2 - Aperiodic EEG slope, individual alpha frequency and idea density modulate individual differences in real-time model updating
AU - Bornkessel-Schlesewsky, Ina
AU - Sharrad, Isabella
AU - Howlett, Caitlin A.
AU - Alday, Phillip M.
AU - Corcoran, Andrew W.
AU - Bellan, Valeria
AU - Wilkinson, Erica
AU - Kliegl, Reinhold
AU - Lewis, Richard L.
AU - Small, Steven L.
AU - Schlesewsky, Matthias
N1 - Funding Information:
The research reported here was funded by an Australian Research Council Future Fellowship awarded to IB-S (FT160100437). AC acknowledges the support of the Three Springs Foundation.
Publisher Copyright:
Copyright © 2022 Bornkessel-Schlesewsky, Sharrad, Howlett, Alday, Corcoran, Bellan, Wilkinson, Kliegl, Lewis, Small and Schlesewsky.
PY - 2022/8/26
Y1 - 2022/8/26
N2 - 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.
AB - 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.
KW - EEG
KW - N400
KW - aperiodic slope
KW - idea density
KW - individual alpha frequency (IAF)
KW - language comprehension
KW - precision
KW - predictive coding
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UR - http://www.scopus.com/inward/citedby.url?scp=85138059011&partnerID=8YFLogxK
U2 - 10.3389/fpsyg.2022.817516
DO - 10.3389/fpsyg.2022.817516
M3 - Article
C2 - 36092106
AN - SCOPUS:85138059011
SN - 1664-1078
VL - 13
JO - Frontiers in Psychology
JF - Frontiers in Psychology
M1 - 817516
ER -