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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.

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Episodic memory is reconstructive and is thus prone to false memory formation. Although false memories are proposed to develop via associative processes, the nature of their neural representations, and the effect of sleep on false memory processing is currently under debate. The present research employed the Deese-Roediger-McDermott (DRM) paradigm and a daytime nap to determine whether semantic false memories and true memories could be differentiated using event-related potentials (ERPs). We also sought to illuminate the role of sleep in memory formation and learning, with a daytime nap. Healthy participants (N = 34, 28F, mean age = 23.23, range = 18-33) completed the DRM task with the learning and recognition phases separated by either a 2hr daytime nap or an equivalent wake period. Linear mixed modelling revealed larger LPC amplitudes for true memories in contrast to false memories, and larger P300 amplitudes for false compared to true memories. Those in the nap group also exhibited larger LPC and P300 amplitudes than participants in the wake group. Additionally, larger P300 amplitudes at delayed recognition (following a consolidation opportunity) were associated with increased true memory accuracy. These findings are argued to be reflective of sleeps ability to promote pattern separation and pattern completion, with true memories arising from distinct memory traces, and false memories arising from thematic extraction and overlap in neural representations. The present research supports the perspective that both true and false memories are reflective of adaptive memory processes, whilst also suggesting that P300 amplitude affects episodic memory accuracy.

The present study aimed to investigate the extent of prediction in language, by reanalysing Nieuwland and colleagues′ (2018) replication of DeLong et al. (2005). Participants (n = 356) viewed sentences containing articles and nouns of varying predictability, while their electroencephalogram (EEG) was recorded. We measured pre-stimulus and N400 event-related activity in the Nieuwland et al. (2018) data and calculated lexical surprisal using Generative Pre-trained Transformer-2 (GPT-2), to capture word predictability. Our results demonstrate increases in N400 amplitude as article surprisal increased, supporting DeLong et al.′s (2005) findings. N400 amplitudes following surprising articles were also reduced when prior word surprisal was high, suggesting that surprising information may impair the brain′s ability to form precise predictions about upcoming words. These findings have important implications for existing understandings of prediction, as they provide evidence for prediction during language comprehension and support the proposal that prediction is an adaptive and unified mechanism of cognition.