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The endeavour to understand human cognition has largely relied upon investigation of task-related brain activity. However, resting-state brain activity can also offer insights into individual information processing and performance capabilities. Previous research has identified electroencephalographic resting-state characteristics (most prominently: the individual alpha frequency; IAF) that predict cognitive function. However, it has largely overlooked a second component of electrophysiological signals: aperiodic 1/f activity. The current study examined how both oscillatory and aperiodic resting-state EEG measures, alongside traditional cognitive tests, can predict performance in a dynamic and complex, semi-naturalistic cognitive task. Participants’ resting-state EEG was recorded prior to engaging in a Target Motion Analysis (TMA) task in a simulated submarine control room environment (CRUSE), which required participants to integrate dynamically changing information over time. We demonstrated that the relationship between IAF and cognitive performance extends from simple cognitive tasks (e.g., digit span) to complex, dynamic measures of information processing. Further, our results showed that individual 1/f parameters (slope and intercept) differentially predicted performance across practice and testing sessions, whereby flatter slopes were associated with improved performance during learning, while higher intercepts were linked to better performance during testing. In addition to the EEG predictors, we demonstrate a link between cognitive skills most closely related to the TMA task (i.e., spatial imagery) and subsequent performance. Overall, the current study highlights (1) how resting-state metrics – both oscillatory and aperiodic - have the potential to index higher-order cognitive capacity, while (2) emphasising the importance of examining these electrophysiological components within more dynamic settings and over time.

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 alpha frequency during wake (∼ 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 wake-derived IAF and sleep neurophysiology interact to influence rule learning in a sample of 35 healthy adults (16 males; 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 indicate that sleep neurophysiology and wake-derived IAF do not interact but modulate memory for complex linguistic rules separately. 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 rapid eye-movement (REM) sleep theta power at posterior regions interacts with IAF to predict rule learning and proportion of time in REM sleep predicts rule learning differentially depending on grammatical rule type. Taken together, the current study provides behavioural and electrophysiological evidence for a complex role of NREM and REM sleep neurophysiology and wake-derived IAF in the consolidation of rule-based information.

Study ObjectivesCurrent 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 non-performance-based measures of EDS. One promising non-performance-based measure of EDS is the aperiodic component of electroencephalography (EEG). Aperiodic (non-oscillatory) 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.MethodsWe 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).ResultsWhile 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.ConclusionsOur 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.

International audience; Despite increased awareness of the lack of gender equity in academia and a growing number of initiatives to address issues of diversity, change is slow, and inequalities remain. A major source of inequity is gender bias, which has a substantial negative impact on the careers, work-life balance, and mental health of underrepresented groups in science. Here, we argue that gender bias is not a single problem but manifests as a collection of distinct issues that impact researchers’ lives. We disentangle these facets and propose concrete solutions that can be adopted by individuals, academic institutions, and society

Memory formation involves the synchronous firing of neurons in task-relevant networks, with recent models postulating that a decrease in low frequency oscillatory activity underlies successful memory encoding and retrieval. To date, this relationship has predominantly been investigated using objects (e.g., faces, natural scenes); however, considerably less is known about the oscillatory correlates of complex rule learning (e.g., language acquisition). Further, recent work has shown that aperiodic (non-oscillatory) 1/ f activity is functionally and behaviourally relevant, yet its interaction with oscillatory activity during complex rule learning remains virtually unknown. Using spectral decomposition and power-law exponent estimation of human EEG data, we show for the first time that 1/ f and oscillatory activity jointly influence the learning of different word order rules of a miniature language system. Fixed word order rules were associated with an increased power-law exponent (i.e. steeper 1/ f slope) compared to flexible word order rules. We also show that stronger anterior beta synchronisation predicts fixed word order rule learning and subsequent behavioural performance, while stronger theta/alpha synchronisation is associated with the learning of flexible word order rules. These results also revealed nonlinear differences between word order rules as a function of time and sensor space. Moreover, we demonstrated that inter-individual variations in spectral power across the learning task interacted with aperiodic activity to influence subsequent behavioural performance. Together, these results suggest that 1/ f activity plays an important role in higher-order cognition, including language processing, and that grammar learning is modulated by different word order permutations, which manifest in distinct oscillatory profiles.

Effective teams are essential for optimally functioning societies. However, little is known regarding the neural basis of two or more individuals engaging cooperatively in real-world tasks, such as in operational training environments. In this exploratory study, we recruited forty individuals paired as twenty dyads and recorded dual-EEG at rest and during realistic training scenarios of increasing complexity using virtual simulation systems. We estimated markers of intrinsic brain activity (i.e., individual alpha frequency and aperiodic activity), as well as task-related theta and alpha oscillations. Using nonlinear modelling and a logistic regression machine learning model, we found that resting-state EEG predicts performance and can also reliably differentiate between members within a dyad. Task-related theta and alpha activity during easy training tasks predicted later performance on complex training to a greater extent than prior behaviour. These findings complement laboratory-based research on both oscillatory and aperiodic activity in higher-order cognition and provide evidence that theta and alpha activity play a critical role in complex task performance in team environments.

Background Current assessment of excessive daytime somnolence (EDS) requires subjective measurements like the Epworth Sleepiness Scale (ESS), and/or resource heavy sleep laboratory investigations. Electroencephalographic (EEG) measures index intrinsic properties of the central nervous system. One such component is aperiodic neural activity which is thought to reflect excitation/inhibition ratios of neural populations and is altered in various states of consciousness. From this perspective, resting-state aperiodic activity may be a potential biomarker for hypersomnolence. We aim to analyse retrospective EEG data from patients who underwent a Multiple Sleep Latency Test (MSLT) and determine if aperiodic activity is predictive of subjective and objective measures of EDS. Methods Participants having undergone laboratory polysomnogram (PSG) and next day MSLT will be grouped into those with and without sleepiness (mean sleep latency (MSL) of < 8min and > 10min respectively). Forty patients in each group (n=80) will be assessed. The primary objective is to compare the aperiodic slope between these groups, and secondary objectives comparing aperiodic activity with ESS and time of day. Data will be analysed using linear mixed-effect models. Simple linear regressions will be performed between the aperiodic slope and MSL and ESS, with R2 values used to estimate of effect size. Progress: Formal ethics approval has been submitted and is pending. Intended Outcome and Impact In this exploratory study we hypothesise that EDS is associated with a lower aperiodic exponent/flatter slope, and hope to provoke further investigation of this metric as a novel biomarker for sleepiness.

Artificial grammar learning (AGL) paradigms are used extensively to characterise (neuro)cognitive bases of language learning. However, despite their effectiveness in characterising the capacity to learn complex structured sequences, AGL paradigms lack ecological validity and typically do not account for cross-linguistic differences in sentence comprehension. Here, we describe a new modified miniature language paradigm – Mini Pinyin – that mimics natural language as it is based on an existing language (Mandarin Chinese) and includes both structure and meaning. Mini Pinyin contains a number of cross-linguistic elements, including varying word orders and classifier-noun rules. To evaluate the effectiveness of Mini Pinyin, 76 (mean age = 24.9; 26 female) monolingual native English speakers completed a learning phase followed by a sentence acceptability judgement task. Generalised mixed effects modelling revealed that participants attained a moderate degree of accuracy on the judgement task, with performance scores ranging from 25% to 100% accuracy depending on the word order of the sentence. Further, sentences compatible with the canonical English word order were learned more efficiently than non-canonical word orders. We controlled for inter-individual differences in statistical learning ability, which accounted for ~20% of the variance in performance on the sentence judgement task. We provide stimuli and statistical analysis scripts as open-source resources and discuss how future research can utilise this paradigm to study the neurobiological basis of language learning. Mini Pinyin affords a convenient tool for improving the future of language learning research by building on the parameters of traditional AGL or existing miniature language paradigms. Refereed/Peer-reviewed

An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to description of action coordination and performance. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of non-target stimuli selection. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d’. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

This chapter introduces electroencephalography (EEG), a measure of neurophysiological activity, as a critical method for investigating individual and team decision-making and cognition. EEG is a useful tool for expanding the theoretical and research horizons in organizational cognitive neuroscience, with a lower financial cost and higher portability than other neuroimaging methods (e.g., functional magnetic resonance imaging). This chapter briefly reviews past work that has applied cognitive neuroscience methods to investigate cognitive processes and outcomes. The focus is on describing contemporary EEG measures that reflect individual cognition and compare them to complementary measures in the field of psychology and management. The authors discuss how neurobiological measures of cognition relate to and may predict both individual cognitive performance and team cognitive performance (decision-making). This chapter aims to assist scholars in the field of managerial and organizational cognition in understanding the complementarity between psychological and neurophysiological methods, and how they may be combined to develop new hypotheses in the intersection of these research fields.

Alpha-band oscillatory activity is involved in modulating memory and attention. However, few studies have investigated individual differences in oscillatory activity during the encoding of emotional memory, particularly in sleep paradigms where sleep is thought to play an active role in memory consolidation. The current study aimed to address the question of whether individual alpha frequency (IAF) modulates the consolidation of declarative memory across periods of sleep and wake. 22 participants aged 18–41 years (mean age = 25.77) viewed 120 emotionally valenced images (positive, negative, neutral) and completed a baseline memory task before a 2hr afternoon sleep opportunity and an equivalent period of wake. Following the sleep and wake conditions, participants were required to distinguish between 120 learned (target) images and 120 new (distractor) images. This method allowed us to delineate the role of different oscillatory components of sleep and wake states in the emotional modulation of memory. Linear mixed-effects models revealed interactions between IAF, rapid eye movement sleep theta power, and slow-wave sleep slow oscillatory density on memory outcomes. These results highlight the importance of individual factors in the EEG in modulating oscillatory-related memory consolidation and subsequent behavioural outcomes and test predictions proposed by models of sleep-based memory consolidation Refereed/Peer-reviewed

Language is one of the most defining human capabilities, involving the coordination of brain networks that generalise the meaning of linguistic units of varying complexity. On a neural level, neocortical slow oscillations and thalamic spindles during sleep facilitate the reactivation of newly encoded memory traces, manifesting in distinct oscillatory activity during retrieval. However, it is currently unknown if the effect of sleep on memory extends to the generalisation of the mechanisms that subserve sentence comprehension. We address this question by analysing electroencephalogram data recorded from 35 participants during an artificial language learning task and an 8hr nocturnal sleep period. We found that a period of sleep was associated with increased alpha/beta power and improved behavioural performance. Phase amplitude coupling analyses also revealed that spindle-slow oscillation coupling predicted the consolidation of sequence-based word orders, which was associated with distinct patterns of oscillatory activity during sentence processing. Taken together, this study presents converging behavioural and neurophysiological evidence for a role of sleep in the consolidation of higher order language learning and associated oscillatory neural activity.

The interaction between attention and emotion is posited to influence long-term memory consolidation. We systematically reviewed experiments investigating the influence of attention on emotional memory to determine: (i) the reported effect of attention on memory for emotional stimuli, and (ii) whether there is homogeneity between behavioural and neuroimaging based effects. Over half of the 47 included experiments found a moderate-to-large effect of attention on emotional memory as measured behaviourally. However, eye-tracking research provide mixed support for the role of attention-related processes in facilitating emotional information into long-term memory. Similarly, modulations in sensory-related components at encoding were not predictive of long-term memory formation, whereas later components appear to differentially reflect the allocation of attention to heterogeneous emotional stimuli. This dissociation in neurophysiology is paralleled by the activation of distinct neural networks under full- and divided-attention conditions. We quantified the effects of the behavioural, eye-tracking and neuroimaging findings via meta-analysis to show that the neural substrates of attention-related emotional memory enhancement may be sensitive to specific methodological parameters.

Sleep promotes memory consolidation through unique neuromodulatory activity. However, little is known about the impact of attention during pre-sleep memory encoding on later memory performance. The current study aimed to address the question of whether attentional state prior to encoding, as indexed by alpha oscillatory activity, modulates the consolidation of images across periods of sleep and wake. 22 participants aged 18 – 41 years (mean age = 27.3) viewed 120 emotionally valenced images (positive, negative, neutral) before a 2hr afternoon sleep opportunity and an equivalent period of wake. Following the sleep and wake conditions, participants were required to distinguish between 120 previously seen (target) images and 120 new (distractor) images. Relative alpha power – adjusted according to participants’ individual alpha frequency – was computed to index attentional state prior to the learning phase. Generalised linear mixed-effects modelling revealed memory performance was modulated by attention, such that greater pre-encoding alpha power preferentially promoted memory consolidation during wake compared to sleep. There was no difference in memory performance between positive, negative and neutral stimuli. Modulations in alpha oscillatory activity may help to coordinate the flow of information between task-relevant cortical regions and a thalamo-cortical loop that preferentially subserves the formation of memory during times of wake relative to sleep.