Biology subjects

Immink, M. A.

Publications and source records attributed to Immink, M. A..

5 recordsLinked to original sources

bioRxiv2024

Augmenting complex and dynamic performance through mindfulness-based cognitive training: an investigation of on-task EEG dynamics.

Mindfulness-based cognitive training exhibits great propensity for improving cognitive performance across a range of contexts. However, the neurophysiological basis of these cognitive enhancements has remained relatively unclear. Previous studies have widely examined EEG during mindfulness practice - or made comparisons with long-term meditators and controls - but have failed to capture how EEG dynamics in subsequent cognitive testing scenarios might be altered as a function of mindfulness-based interventions. The current study therefore aimed to assess a variety of EEG dynamics (oscillatory, aperiodic, and event-related) during engagement in a dynamic and complex cognitive task, following a mindfulness-based cognitive training regime. Participants (n = 40, age range = 18 - 38) attended the lab on two separate occasions (pre- and post-a web-based one-week mindfulness intervention), where EEG was recorded during engagement in the Target Motion Analyst (TMA) task. Previous analysis of the same participants demonstrated that greater adherence to the mindfulness-based cognitive training was associated with improved performance on the TMA task (Dziego, Bornkessel-Schlesewsky, Schlesewsky et al., 2024). Here, we capitalise on these previous findings to assess whether adherence is paralleled by measurable differences in on-task EEG dynamics. Linear mixed-effects modelling demonstrated that, while main effects were observed across session, adherence to cognitive training was not directly associated with alpha power, theta power or 1/f parameters. Challenges also arose when computing event-related potentials (ERPs), illustrating the difficulties of using this technique in more complex testing environments. While these results are challenging to place within the context of previous EEG studies on meditation and cognitive performance, our findings highlight the complexities in understanding the cognitive benefits of mindfulness-based training interventions through EEG dynamics observed during subsequent cognitive testing.

neuroscience↗

bioRxiv2023

Augmenting complex and dynamic performance through mindfulness-based cognitive training: an evaluation of training adherence, trait mindfulness, personality and resting-state EEG.

Human performance applications of mindfulness-based training have demonstrated its utility in enhancing cognitive functioning. Previous studies have illustrated how these interventions can improve performance on traditional cognitive tests, however, little investigation has explored the extent to which mindfulness-based training can optimise performance in more dynamic and complex contexts. Further, from a neuroscientific perspective, the underlying mechanisms responsible for performance enhancements remain largely undescribed. With this in mind, the following study aimed to investigate how a short-term mindfulness intervention (one week) augments performance on a dynamic and complex task (target motion analyst task; TMA) in young, healthy adults (n = 40, age range = 18 - 38). Linear mixed effect modelling revealed that increased adherence to the mindfulness-based training regime (ranging from 0-21 sessions) was associated with improved performance in the second testing session of the TMA task, controlling for baseline performance. Further analyses of resting-state electroencephalographic (EEG) metrics and additional individual factors demonstrated enhancements associated with training adherence remained relatively consistent across varying levels of participants resting-state EEG metrics, personality measures (i.e., trait mindfulness, neuroticism, conscientiousness), self-reported enjoyment and timing of intervention adherence. Our results thus indicate that mindfulness-based cognitive training leads to performance enhancements in distantly related tasks, irrespective of several individual differences. We also revealed nuances in the magnitude of cognitive enhancements contingent on the timing of adherence, regardless of total volume of training. Overall, our findings suggest that mindfulness-based training could be used in a myriad of settings to elicit transferable performance enhancements.

neuroscience↗

bioRxiv2022

Neural and cognitive correlates of performance in dynamic multi-modal settings

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.

neuroscience↗

bioRxiv2021

Additive Effects of Cognitive Load, Motor Demand, and Environmental Complexity on Attention: A Real-World EEG-study

The capacity to regulate ones attention in accordance with fluctuating task demands and environmental contexts is an essential feature of adaptive behavior. Although the electrophysiological correlates of attentional processing have been extensively studied in the laboratory, relatively little is known about the way they unfold under more variable, ecologically-valid conditions. Accordingly, this study employed a real-world EEG design to investigate how attentional processing varies under increasing cognitive, motor, and environmental demands. Forty-four participants were exposed to an auditory oddball task while (1) sitting in a quiet room inside the lab, (2) walking around a sports field, and (3) wayfinding across a university campus. In each condition, participants were instructed to either count or ignore oddball stimuli. While behavioral performance was similar across the lab and field conditions, oddball count accuracy was significantly reduced in the campus condition. Moreover, event-related potential components (mismatch negativity and P3) elicited in both real-world settings differed significantly from those obtained under laboratory conditions. These findings demonstrate the impact of environmental factors on attentional processing during simultaneously-performed motor and cognitive tasks, highlighting the value of incorporating dynamic and unpredictable contexts within naturalistic designs.

neuroscience↗

bioRxiv2021

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

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.

neuroscience↗