Electrophysiological indices of individual differences in adult language learning

Nalaye, Halima; Cross, Zachariah R.; Schlesewsky, Matthias; Bornkessel-­Schlesewsky, Ina

doi:10.1101/2022.06.07.495229

Cited by 4 publications

(4 citation statements)

References 93 publications

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“…More recent results (e.g., Ociepka et al, 2022) indicate that the association between high IAF and greater performance on a variety of tasks is not as common as once believed. In fact, in another reanalysis of Cross et al, (2021) examining a different linguistic manipulation, Nalaye et al, (2022) found a negative relationship between IAF and performance on the Mini Pinyin grammaticality judgement task, with lower-IAF individuals performing better. In combination with our results, it appears that high IAF is not beneficial in every context.…”

Section: Discussionmentioning

confidence: 99%

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Richter¹,

Cross²,

Bornkessel-Schlesewsky³

2022

Preprint

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Memory is critical for many cognitive functions, from remembering facts, to learning complex environmental rules. While memory encoding occurs during wake, memory consolidation is associated with sleep-related neural activity. Further, research suggests that individual differences in the alpha frequency band (∼ 7 – 13 Hz) modulate memory processes, with higher individual alpha frequency (IAF) associated with greater memory performance. However, the relationship between wake-related EEG individual differences, such as IAF, and sleep-related neural correlates of memory consolidation has been largely unexplored, particularly in a complex rule-based memory context. Here, we aimed to investigate whether IAF and sleep neurophysiology interact to influence rule learning in a sample of 36 healthy adults (16 male; mean age = 25.4, range: 18 – 40). Participants learned rules of a modified miniature language prior to either 8hrs of sleep or wake, after which they were tested on their knowledge of the rules in a grammaticality judgement task. Results indicated that sleep and IAF modulate memory for complex linguistic rules. Phase-amplitude coupling between slow oscillations and spindles during non-rapid eye-movement (NREM) sleep also promoted memory for rules that were analogous to the canonical English word order. As an exploratory analysis, we found that theta activity during rapid eye-movement (REM) sleep predicted rule learning. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and IAF in the consolidation of rule-based information.

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Section: Discussionmentioning

confidence: 99%

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Richter¹,

Cross²,

Bornkessel-Schlesewsky³

2022

Preprint

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“…We also replicated the analyses using the IAPF power, which yielded similar results to those obtained with standard alpha band power (see Supplementary Materials, p. 5). In the language domain, it seems that overall, a lower IAPF is associated with more efficient linguistic processing (e.g., Bornkessel‐Schlesewsky et al, 2022; Nalaye et al, 2022; for further considerations, see the Supplementary Materials, p. 15). This suggests that properties of alpha oscillations other than power, such as IAPF speed, might be predictive of post‐stimulus cognitive processes and electrophysiological responses.…”

Section: Discussionmentioning

confidence: 99%

Trial‐by‐trial fluctuations of pre‐stimulus alpha power predict language ERPs

Lago,

Pezzetta,

Gastaldon

et al. 2023

Psychophysiology

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Anticipatory mechanisms are known to play a key role in language, but they have been mostly investigated with violation paradigms, which only consider what happens after predictions have been (dis)confirmed. Relatively few studies focused on the pre‐stimulus interval and found that stronger expectations are associated with lower pre‐stimulus alpha power. However, alpha power also fluctuates spontaneously, in the absence of experimental manipulations; and in the attention and perception domains, spontaneously low pre‐stimulus power is associated with better behavioral performance and with event‐related potential (ERPs) with shorter latencies and higher amplitudes. Importantly, little is known about the role of alpha fluctuations in other domains, as it is in language. To this aim, we investigated whether spontaneous fluctuations in pre‐stimulus alpha power modulate language‐related ERPs in a semantic congruence task. Electrophysiology data were analyzed using Generalized Additive Mixed Models to model nonlinear interactions between pre‐stimulus alpha power and EEG amplitude, at the single‐trial level. We found that the N400 and the late posterior positivity/P600 were larger in the case of lower pre‐stimulus alpha power. Still, while the N400 was observable regardless of the level of pre‐stimulus power, a late posterior positivity/P600 effect was only observable for low pre‐stimulus alpha power. We discuss these findings in light of the different, albeit connected, functional interpretations of pre‐stimulus alpha and the ERPs according to both a nonpredictive interpretation focused on attentional mechanisms and under a predictive processing framework.

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“…Previous work on the localization of targets moving in space revealed an advantage for individuals with a lower IAF (Howard et al, 2017), with the authors suggesting that this result could be due to the longer timescales involved in the task (movement was between 2 and 4 s in length) in comparison to the transient stimuli used, for example, by Samaha and Postle (2015). In the language domain, Nalaye et al (2022) recently found that lower-IAF individuals outperformed their higher-IAF counterparts when learning a modified miniature language based on Mandarin Chinese. Akin to the study by Howard et al (2017), this paradigm involved learning regularities on timescales of multiple seconds.…”

Section: Stronger Model Adaptation For Individuals With Lower Individ...mentioning

confidence: 96%

“…IAF decreases with age from young adulthood onwards (Köpruner et al, 1984;Klimesch, 1999), thus accompanying the wellknown decline of many cognitive abilities in older adulthood (e.g., Hedden and Gabrieli, 2004;Salthouse, 2011). Previous work also indicates that language processing and language learning strategies differ between high-and low-IAF individuals (Bornkessel et al, 2004;Bornkessel-Schlesewsky et al, 2015a;Kurthen et al, 2020;Nalaye et al, 2022).…”

Section: Individual Alpha Frequencymentioning

confidence: 97%

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

Bornkessel-Schlesewsky

Sharrad

Howlett

et al. 2022

Front. Psychol.

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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.

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Electrophysiological indices of individual differences in adult language learning

Cited by 4 publications

References 93 publications

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Individual differences in information processing during sleep and wake predict sleep-based memory consolidation of complex rules

Trial‐by‐trial fluctuations of pre‐stimulus alpha power predict language ERPs

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

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