Non-invasive neurophysiological measures of learning: A meta-analysis

Neurophysiological markers of successful learning in healthy aging

The capacity to learn and memorize is a key determinant for the quality of life but is known to decline to varying degrees with age. However, neural correlates of memory formation and the critical features that determine the extent to which aging affects learning are still not well understood. By employing a visual sequence learning task, we were able to track the behavioral and neurophysiological markers of gradual learning over several repetitions, which is not possible in traditional approaches that utilize a remember vs. forgotten comparison. On a neurophysiological level, we focused on two learning-related centro-parietal event-related potential (ERP) components: the expectancy-driven P300 and memory-related broader positivity (BP). Our results revealed that although both age groups showed significant learning progress, young individuals learned faster and remembered more stimuli than older participants. Successful learning was directly linked to a decrease of P300 and BP amplitudes. However, young participants showed larger P300 amplitudes with a sharper decrease during the learning, even after correcting for an observed age-related longer P300 latency and increased P300 peak variability. Additionally, the P300 amplitude predicted learning success in both age groups and showed good test-retest reliability. On the other hand, the memory formation processes, reflected by the BP amplitude, revealed a similar level of engagement in both age groups. However, this engagement did not translate into the same learning progress in the older participants. We suggest that the slower and more variable timing of the stimulus identification process reflected in the P300 means that despite the older participants engaging the memory formation process, there is less time for it to translate the categorical stimulus location information into a solidified memory trace. The results highlight the important role of the P300 and BP as a neurophysiological marker of learning and may enable the development of preventive measures for cognitive decline.

Simultaneous fMRI and tDCS for Enhancing Training of Flight Tasks

There is a gap in our understanding of how best to apply transcranial direct-current stimulation (tDCS) to enhance learning in complex, realistic, and multifocus tasks such as aviation. Our goal is to assess the effects of tDCS and feedback training on task performance, brain activity, and connectivity using functional magnetic resonance imaging (fMRI). Experienced glider pilots were recruited to perform a one-day, three-run flight-simulator task involving varying difficulty conditions and a secondary auditory task, mimicking real flight requirements. The stimulation group (versus sham) received 1.5 mA high-definition HD-tDCS to the right dorsolateral prefrontal cortex (DLPFC) for 30 min during the training. Whole-brain fMRI was collected before, during, and after stimulation. Active stimulation improved piloting performance both during and post-training, particularly in novice pilots. The fMRI revealed a number of tDCS-induced effects on brain activation, including an increase in the left cerebellum and bilateral basal ganglia for the most difficult conditions, an increase in DLPFC activation and connectivity to the cerebellum during stimulation, and an inhibition in the secondary task-related auditory cortex and Broca's area. Here, we show that stimulation increases activity and connectivity in flight-related brain areas, particularly in novices, and increases the brain's ability to focus on flying and ignore distractors. These findings can guide applied neurostimulation in real pilot training to enhance skill acquisition and can be applied widely in other complex perceptual-motor real-world tasks.

Visual-spatial and visuomotor functioning in adults with neurofibromatosis type 1

BACKGROUND

Neurofibromatosis type 1 (NF1) is a neurodevelopmental genetic disorder associated with visual-spatial and visuomotor deficits, which have not been studied well in adults with NF1.

METHODS

In 22 adults with NF1 and 31 controls, visuomotor functioning was assessed by measuring eye latency, hand latency and hand accuracy during visuomotor tasks. Visual-spatial functioning was assessed by measuring eye movement responses during the Visual Threshold Task.

RESULTS

The NF1 group had a significantly shorter eye latency than the control group and was less accurate in their hand movements during specific visuomotor tasks. The groups showed no differences in eye movement responses during the Visual Threshold Task and in hand latency during the visuomotor tasks.

CONCLUSIONS

In contrast to studies in children with NF1, we found no alterations in visual-spatial information processing in adults. Impairments in eye latency and hand accuracy during specific visuomotor tasks may indicate deficits in visuomotor functioning in adults with NF1.

Age Differences in Learning-Related Neurophysiological Changes

Abstract. Research in young adults has demonstrated that neurophysiological measures are able to provide insight into learning processes. However, to date, it remains unclear whether neurophysiological changes during learning in older adults are comparable to those in younger adults. The current study addressed this issue by exploring age differences in changes over time in a range of neurophysiological outcome measures collected during visuomotor sequence learning. Specifically, measures of electroencephalography (EEG), skin conductance, heart rate, heart rate variability, respiration rate, and eye-related measures, in addition to behavioral performance measures, were collected in younger ( Mage = 27.24 years) and older adults ( Mage = 58.06 years) during learning. Behavioral responses became more accurate over time in both age groups during visuomotor sequence learning. Yet, older adults needed more time in each trial to enhance the precision of their movement. Changes in EEG during learning demonstrated a stronger increase in theta power in older compared to younger adults and a decrease in gamma power in older adults while increasing slightly in younger adults. No such differences between the two age groups were found on other neurophysiological outcome measures, suggesting changes in brain activity during learning to be more sensitive to age differences than changes in peripheral physiology. Additionally, differences in which neurophysiological outcomes were associated with behavioral performance on the learning task were found between younger and older adults. This indicates that the neurophysiological underpinnings of learning may differ between younger and older adults. Therefore, the current findings highlight the importance of taking age into account when aiming to gain insight into behavioral performance through neurophysiology during learning.

Aviation and neurophysiology: A systematic review

This paper systematically reviews 20 years of publications (N = 54) on aviation and neurophysiology. The main goal is to provide an account of neurophysiological changes associated with flight training with the aim of identifying neurometrics indicative of pilot's flight training level and task relevant mental states, as well as to capture the current state-of-art of (neuro)ergonomic design and practice in flight training. We identified multiple candidate neurometrics of training progress and workload, such as frontal theta power, the EEG Engagement Index and the Cognitive Stability Index. Furthermore, we discovered that several types of classifiers could be used to accurately detect mental states, such as the detection of drowsiness and mental fatigue. The paper advances practical guidelines on terminology usage, simulator fidelity, and multimodality, as well as future research ideas including the potential of Virtual Reality flight simulations for training, and a brain-computer interface for flight training.

Effects of unilateral dynamic handgrip on reaction time and error rate

Quick and accurate reactions to environmental stimuli are often required. Researchers have investigated ways to improve these reactions, which are critical components of perceptual-motor abilities. To optimize individual performance, different techniques, such as embodied interventions and brain stimulation, have been examined. The evidence from EEG studies shows that upper limb muscle contractions lead to changes in brain oscillations associated with changes in mental states and behavioral outcomes. Much research has been conducted on whether muscle contractions of a particular hand have a greater effect on a perceptual-motor ability, as a trigger to facilitate cortical processes (a mediator) for skilled motor performance. While previous studies have shown that left- (vs. right-) hand contractions can lead to greater alpha activation, we hypothesized that left dynamic handgrips have different impacts on motor performance, reflected by simple RT (SRT) and choice RT (CRT). We recruited 64 right-handers, for a within/between-subjects experiment consisting of performance measurements in SRT and CRT tasks after the intervention (either right or left dynamic handgrip approximately twice a second for 30 s for each hand) or assignment to paired passive control groups. We did not find left-hand contractions improve response accuracy in neither SRT nor CRT tasks. Further, left-hand contractions did not affect RTs. The findings indicate that the effects of dynamic handgrips are smaller on behavioral outcomes such as RTs than what can be inferred from published studies. More research is needed to establish the effect of dynamic handgrips on optimizing performance.

Neurofeedback and neural self-regulation: a new perspective based on allostasis

The field of neurofeedback training (NFT) has seen growing interest and an expansion of scope, resulting in a steadily increasing number of publications addressing different aspects of NFT. This development has been accompanied by a debate about the underlying mechanisms and expected outcomes. Recent developments in the understanding of psychophysiological regulation have cast doubt on the validity of control systems theory, the principal framework traditionally used to characterize NFT. The present article reviews the theoretical and empirical aspects of NFT and proposes a predictive framework based on the concept of allostasis. Specifically, we conceptualize NFT as an adaptation to changing contingencies. In an allostasis four-stage model, NFT involves (a) perceiving relations between demands and set-points, (b) learning to apply collected patterns (experience) to predict future output, (c) determining efficient set-points, and (d) adapting brain activity to the desired ("set") state. This model also identifies boundaries for what changes can be expected from a neurofeedback intervention and outlines a time frame for such changes to occur.

Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning

Functional near-infrared spectroscopy (fNIRS) is a promising technique for non-invasively assessing cortical brain activity during learning. This technique is safe, portable, and, compared to other imaging techniques, relatively robust to head motion, ocular and muscular artifacts and environmental noise. Moreover, the spatial resolution of fNIRS is superior to electroencephalography (EEG), a more commonly applied technique for measuring brain activity non-invasively during learning. Outcomes from fNIRS measures during learning might therefore be both sensitive to learning and to feedback on learning, in a different way than EEG. However, few studies have examined fNIRS outcomes in learning and no study to date additionally examined the effects of feedback. To address this apparent gap in the literature, the current study examined prefrontal cortex activity measured through fNIRS during visuomotor learning and how this measure is affected by task feedback. Activity in the prefrontal cortex decreased over the course of learning while being unaffected by task feedback. The findings demonstrate that fNIRS in the prefrontal cortex is valuable for assessing visuomotor learning and that this measure is robust to task feedback. The current study highlights the potential of fNIRS in assessing learning even under different task feedback conditions.

Rethinking Education in a Crisis: How New Is a New Common Really?

The COVID-19 pandemic has disrupted the status quo in many areas of society, including education. At all educational levels, on-site lecturing had to switch instantaneously to an online mode of instruction. This transition was so straightforward, that the argument could be made for online education to become a permanent fixture, particularly if it is more efficient, cheaper, and more effective than traditional education. Extensive meta-analyses, however, show that most online teaching practices do not lead to better educational outcomes than the on-site alternatives. Worse yet, the traditional face-to-face mode of lecturing is ineffective in the absence of personalized interactions. The proposed solutions are offered by artificial intelligence research, including virtual reality, intelligent tutoring systems, and serious games—solutions that have so far not been extensively implemented in practice. The current health crisis provides our educational professionals with an opportunity to rethink their teaching practices and focus on applying these promising new alternatives.

Non-invasive Neurophysiology in Learning and Training: Mechanisms and a SWOT Analysis

Although many scholars deem non-invasive measures of neurophysiology to have promise in assessing learning, these measures are currently not widely applied, neither in educational settings nor in training. How can non-invasive neurophysiology provide insight into learning and how should research on this topic move forward to ensure valid applications? The current article addresses these questions by discussing the mechanisms underlying neurophysiological changes during learning followed by a SWOT (strengths, weaknesses, opportunities, and threats) analysis of non-invasive neurophysiology in learning and training. This type of analysis can provide a structured examination of factors relevant to the current state and future of a field. The findings of the SWOT analysis indicate that the field of neurophysiology in learning and training is developing rapidly. By leveraging the opportunities of neurophysiology in learning and training (while bearing in mind weaknesses, threats, and strengths) the field can move forward in promising directions. Suggestions for opportunities for future work are provided to ensure valid and effective application of non-invasive neurophysiology in a wide range of learning and training settings.

Neurophysiological changes in visuomotor sequence learning provide insight in general learning processes: Measures of brain activity, skin conductance, heart rate and respiration

Prior research has shown neurophysiological measures of learning yield large effect sizes, suggesting that these measures have high potential in providing insight into learning. Yet, most literature on learning and neurophysiological measures focused on a single outcome measure, neglecting the interplay between different types of measures. Additionally, it is not yet clear which measures change robustly in a way specific to the learning process. The current study assessed implicit visuomotor sequence learning through multiple neurophysiological outcome measures. In two experiments participants were presented with an arm-movement version of the Serial Reaction Time Task with blocks in which targets were selected in a repeating sequence and blocks in which targets were selected randomly. While participants were executing this task, measures of EEG, skin conductance, heart rate (variability) and respiration, in addition to measures of behavioral performance, were collected. Although behavioral performance was sensitive to sequence learning, as demonstrated by faster responses in sequence than in random blocks, neurophysiology was not sensitive to sequence learning. However, in both experiments, skin conductance level and parietal EEG alpha and gamma power were sensitive to task induction and changed during sequence blocks in the direction of a pre-task baseline and were related to behavioral performance. In general, models including only EEG parietal gamma power were just as powerful in explaining behavioral measures during learning as models including a combination of neurophysiological outcome measures. The findings of the current study demonstrate that neurophysiology is not sensitive to implicit sequence learning specifically, but that general learning effects on a visuomotor learning task are reflected in measures of neurophysiology. Additionally, the findings highlight that a combination of neurophysiological outcome measures is not necessarily better in explaining task learning than a single measure.