Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning

Cross, Zachariah R.; Corcoran, Andrew W.; Schlesewsky, Matthias; Kohler, Mark

doi:10.1162/jocn_a_01878

Cited by 34 publications

(35 citation statements)

References 150 publications

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“…However, this analysis did not specifically look at the process of encoding which limits the understanding of what may be occurring during this time (for an in depth analysis of the encoding phase, see Cross et al, 2022). Despite this, analyses reported here provide further evidence for an effect of IAF on memory consolidation, consistent with previous literature (Doppelmayr et al, 2005;Klimesch, 1997Klimesch, , 1999Klimesch et al, 1994Klimesch et al, , 1999Richard Clark et al, 2004;Vogt et al, 1998).…”

Section: The Influence Of Iaf and Sleep On Memory Performance At The ...supporting

confidence: 72%

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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: The Influence Of Iaf and Sleep On Memory Performance At The ...supporting

confidence: 72%

“…However, this interpretation is speculative and requires further examination of the encoding phase (see Cross et al, 2022) to permit better understanding of how encoding processes affects subsequent rule extraction.…”

Section: Neural Mechanisms Underlying Sleep-based Consolidation Of Rulesmentioning

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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“…The (negative) exponent summarising the slope of aperiodic spectral activity was calculated by fitting a linear regression to the estimated fractal component in log-log space. For a full mathematical description of IRASA, see 37 (also see 38 ).…”

Section: Methodsmentioning

confidence: 99%

Resting-state aperiodic neural activity as a novel objective marker of daytime somnolence

Cross

Kang

Lushington

et al. 2022

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Study Objectives: Current assessment of excessive daytime somnolence (EDS) requires subjective measurements such as the Epworth Sleepiness Scale (ESS), and/or resource intensive sleep laboratory investigations. Recent work 1,2 has called for more nonperformance based measures of EDS. One promising nonperformance based measure of EDS is the aperiodic component of electroencephalography (EEG). Aperiodic (nonoscillatory) activity reflects excitation/inhibition ratios of neural populations and is altered in various states of consciousness, and thus may be a potential biomarker of hypersomnolence. Methods: We retrospectively analysed EEG data from patients who underwent a Multiple Sleep Latency Test (MSLT) and determined whether aperiodic neural activity is predictive of EDS. Participants having undergone laboratory polysomnogram and next day MSLT were grouped into MSLT+ (n = 26) and MSLT- (n = 33) groups (mean sleep latency of < 8min and > 10min, respectively) and compared against a non-clinical (Control) group of participants (n = 26). Results: While the MSLT+ and MSLT- groups did not differ in their aperiodic activity, the Control group had a significantly flatter slope and larger offset compared to both MSLT+ and MSLT- groups. Logistic regression machine learning predicted group status (i.e., symptomatic, non-symptomatic) with 90% accuracy based on the aperiodic slope while controlling for age. Slow oscillation spindle coupling was also significantly stronger in the Control group relative to MSLT+ and MSLT- groups. Conclusions: Our results provide first evidence that aperiodic neural dynamics and sleep-based cross frequency coupling is predictive of EDS, thereby providing a novel avenue for basic and applied research in the study of sleepiness.

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“…In the domain of language processing, Dave et al (2018) recently observed a modulation of prediction-related N400 effects by 1/f slope such that a steeper slope predicted more pronounced N400 effects. Further, Cross et al (2022) found that the learning of certain types of grammatical rules in an artificial language is likewise predicted by inter-individual variability in 1/f slope.…”

Section: Aperiodic ( /F) Activitymentioning

confidence: 94%

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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Oscillatory and Aperiodic Neural Activity Jointly Predict Language Learning

Cited by 34 publications

References 150 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

Resting-state aperiodic neural activity as a novel objective marker of daytime somnolence

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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