Levels Of Working Memory Load Research Articles

Working Memory Load Impact on Effective Connectivity: a Dynamic Causal Modeling Study

This study aims to examine the roles of the brain regions in working memory (WM) processing and the modulatory effects of WM load. Electroencephalogram (EEG) signals were recorded when participants perform a letter-version n-back task with three different levels of WM load. The directional causal connections between brain regions were estimated using Dynamic Causal Modeling (DCM). The directions and strengths of the connections were compared for different WM load conditions. The results showed a right-lateralized, backward-only connection pattern for the high WM load condition. The results also showed changes in the roles of the brain regions when the WM load increases. These findings of the modulatory effects of WM load may be utilized in measuring cognitive states, and designing adaptive automation in augmented cognition programs.

Evaluating the Effect of Increasing Working Memory Load on EEG-Based Functional Brain Networks

Purpose: Working Memory (WM) plays a crucial role in many cognitive functions of the human brain. Examining how the inter-regional connectivity and characteristics of functional brain networks modulate with increasing WM load could lead to a more in-depth understanding of the WM system.
 Materials and Methods: To investigate the effect of WM load alterations on the inter-regional synchronization and functional network characteristics, we used Electroencephalogram (EEG) data recorded from 21 healthy participants during an n-back task with three load levels (0-back, 2-back, and 3-back). The networks were constructed based on the weighted Phase Lag Index (wPLI) in the theta, alpha, beta, low-gamma, and high-gamma frequency bands. After constructing the fully connected, weighted, and undirected networks, the node-to-node connections, graph-theory metrics consisting of mean Clustering coefficient (C), characteristic path Length (L), and node strength were analyzed by statistical tests.
 Results: It was revealed that in the presence of WM load (2- and 3-back tasks) compared with the WM-free condition (0-back task) within the alpha range, the Inter-Regional Functional Connectivity (IRFC), functional integration, functional segregation, and node strength in channels located at the frontal, parietal and occipital regions were significantly reduced. In the high-gamma band, IRFC was significantly higher in the difficult task (3-back) compared to the easy and moderate tasks (0- and 2-back). Besides, locally clustered connections were significantly increased in 3-back relative to the 2-back task.
 Conclusion: Inter-regional alpha synchronization and alpha-band network metrics can distinguish between the WM and WM-free tasks. In contrast, phase synchronization of high-gamma oscillations can differentiate between the levels of WM load, which demonstrates the potential of the phase-based functional connectivity and brain network metrics for predicting the WM load level.

Taking a Deeper Look at the Brain: Predicting Visual Perceptual and Working Memory Load From High-Density fNIRS Data.

Predicting workload using physiological sensors has taken on a diffuse set of methods in recent years. However, the majority of these methods train models on small datasets, with small numbers of channel locations on the brain, limiting a model's ability to transfer across participants, tasks, or experimental sessions. In this paper, we introduce a new method of modeling a large, cross-participant and cross-session set of high density functional near infrared spectroscopy (fNIRS) data by using an approach grounded in cognitive load theory and employing a Bi-Directional Gated Recurrent Unit (BiGRU) incorporating attention mechanism and self-supervised label augmentation (SLA). We show that our proposed CNN-BiGRU-SLA model can learn and classify different levels of working memory load (WML) and visual processing load (VPL) across participants. Importantly, we leverage a multi-label classification scheme, where our models are trained to predict simultaneously occurring levels of WML and VPL. We evaluate our model using leave-one-participant-out (LOOCV) as well as 10-fold cross validation. Using LOOCV, for binary classification (off/on), we reached an F1-score of 0.9179 for WML and 0.8907 for VPL across 22 participants (each participant did 2 sessions). For multi-level (off, low, high) classification, we reached an F1-score of 0.7972 for WML and 0.7968 for VPL. Using 10-fold cross validation, for multi-level classification, we reached an F1-score of 0.7742 for WML and 0.7741 for VPL.

The effect of working memory load on feedback processing: Evidence from an event-related potentials (ERP) study

<p id=C2>Feedback processing plays an important role in behavior modification and knowledge acquisition. Previous research has explored the neurophysiological basis and psychological functions of feedback processing and proposed corresponding theoretical models, but little is known about how working memory (WM) load affects feedback processing. Studies have reported electrophysiological indicators, such as the reward positivity (RewP) and the related theta and delta oscillations, the P3 and the late positive potential (LPP), during brain processing feedback. This study will further examine how WM load modulates these electrophysiological components and their corresponding cognitive functions. <break/>In the present study, we used a dual-task paradigm to investigate feedback processing under different WM load conditions. This study included 25 healthy college students and used a 3 (WM load: baseline vs. low WM load vs. high WM load) by 2 (feedback valence: positive vs. negative) within-participant factorial design. During the experiment, participants were asked to perform a simple gambling task and a spatial memory task simultaneously, and the magnitude of the WM load included three conditions: baseline, low WM load and high WM load. The RewP generated in the early stage of feedback processing and the LPP generated in the late stage of feedback processing, as well as the delta and theta oscillations related to feedback evaluation, were analyzed. <break/>The behavioral results showed that the accuracy of the low WM load condition was significantly higher than that of the high WM load condition. The electrophysiological results showed that the amplitudes of the RewP were sensitive to feedback valence, with positive feedback evoking larger RewP than negative feedback, but the RewP was not affected by the WM load. There was no difference in the P3 amplitude under the different WM load conditions. For the LPP, there was a significant interaction between the WM load and feedback valence. Further analysis revealed that, in the high WM load condition, the LPP amplitude was larger for positive feedback than for negative feedback. The theta power differences between negative feedback and positive feedback were larger in the low WM load condition than in the high WM load condition. For delta oscillation, the power was increased after positive feedback compared to after negative feedback, but there was no difference at different WM load levels. <break/>The RewP results indicate that the participants process feedback valence information well under all three WM load conditions in the experiment. The LPP results suggest that the participants assigned additional emotional motivation to the feedback outcome as a result of their cognitive efforts under high WM load conditions. The ERP results for the time domain dimension showed that the effect of the WM load on feedback processing was most noticeable in the later stages of feedback processing. Moreover, these observations support the argument that the RewP and theta power reflect distinct cognitive phenomena; namely, the RewP reflects the processing of feedback valence in the anterior cingulate cortex (ACC), whereas theta oscillations reflect the role of the ACC in cognitive control. The WM load selectively modulates the cognitive control process in the ACC.

More flexible brain activation underlies cognitive reserve in older adults

The goal of this study was to identify the brain mechanisms underlying cognitive reserve using a parametric n-back working memory (WM) task in a sample of healthy older adults. We first identified the WM-related activations associated with years of education and then tested whether these activations mitigated the detrimental impact of age on cognition. Thirty-nine older adults received a magnetic resonance imaging examination while completing an n-back task with different levels of WM load (0-, 1- vs. 2-back). Results show that more education is associated with lower activation of the left medial superior frontal gyrus (BA8) in the 1-back condition and a greater activation of the right caudate nucleus in the 2-back condition. The caudate and frontal activations are task-positive and task-negative regions, respectively. Moderation analyses indicate that the effect of age on performance is less detrimental in participants with higher caudate activation in the 2-back condition. Overall, these results suggest that cognitive reserve is explained by a superior ability to flexibly engage greater or novel activation as cognitive demand increases.

Working memory load modulates oscillatory activity and the distribution of fast frequencies across frontal theta phase during working memory maintenance

Working memory (WM) is a keystone of our cognitive abilities. Increasing load has been shown to dampen its performance and affect oscillatory neural activity in different frequency bands. Nevertheless, mixed results regarding fast frequencies activity and a lack of research on WM load modulations of cross-frequency phase-amplitude coupling mechanisms preclude a better understanding of the impact of increased WM load levels on brain activity as well as inter-regional communication and coordination supporting WM processes. Hence, we analyzed the EEG activity of 25 participants while performing a delayed-matching-to-sample (DMS) WM task with three WM load levels. Current density power and distribution at the source level for theta, beta, and gamma frequencies during the task's delay period were compared for each pair of WM load conditions. Results showed maximal increases of theta activity in frontal areas and of fast frequencies' activity in posterior regions with WM load, showing the involvement of frontal theta activity in WM maintenance and the control of attentional resources and visual processing by beta and gamma activity. To study whether WM load modulates communication between cortical areas, posterior beta and gamma amplitudes distribution across frontal theta phase was also analysed for those areas showing the largest significant WM load modulations. Higher beta activity amplitude at bilateral cuneus and right middle occipital gyrus, and higher gamma activity amplitude at bilateral posterior cingulate were observed during frontal theta phase peak in low than high memory load conditions. Moreover, greater fast beta amplitude at the right postcentral gyrus was observed during theta phase trough at right middle frontal gyrus in high than low memory load conditions. These results show that WM load modulates whether interregional communication occurs during theoretically optimal or non-optimal time windows, depending on the demands of frontal control of posterior areas required to perform the task successfully.

Classification of working memory loads using hybrid EEG and fNIRS in machine learning paradigm

Single modality brain–computer interface (BCI) systems often mislabel the electroencephalography (EEG) signs as a command, even though the participant is not executing some task. In this Letter, the classification of different working memory load levels is presented using a hybrid BCI system. N-back cognitive tasks such as 0-back, 2-back, and 3-back are used to create working memory load on participants while recording EEG and functional near-infrared spectroscopy (fNIRS) signals simultaneously. A combination of statistically significant features obtained from EEG and fNIRS corresponding to frontal region channels are used to classify different N-back commands. Kernel-based support vector machine (SVM) classifiers are employed with and without cross-validation schemes. Classification accuracy of 100% is achieved for binary classification of 0-back against 2-back and 0-back against 3-back using linear SVM, quadratic SVM, and cubic SVM under holdout data division protocol.

Working memory load-dependent changes in cortical network connectivity estimated by machine learning

Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to characterize the interactions among cortical networks that are sensitive to working memory load since such interactions can also hint at the impaired dynamics in patients with poor working memory performance. Functional connectivity is a powerful tool used to investigate coordinated activity among local and distant brain regions. Here, we identified connectivity footprints that differentiate task states representing distinct working memory load levels. We employed linear support vector machines to decode working memory load from task-based functional connectivity matrices in 177 healthy adults. Using neighborhood component analysis, we also identified the most important connectivity pairs in classifying high and low working memory loads. We found that between-network coupling among frontoparietal, ventral attention and default mode networks, and within-network connectivity in ventral attention network are the most important factors in classifying low vs. high working memory load. Task-based within-network connectivity profiles at high working memory load in ventral attention and default mode networks were the most predictive of load-related increases in response times. Our findings reveal the large-scale impact of working memory load on the cerebral cortex and highlight the complex dynamics of intrinsic brain networks during active task states.

Math anxiety and executive function: Neural influences of task switching on arithmetic processing.

Math anxiety (MA) is associated with negative thoughts and emotions when encountering mathematics, often resulting in under-performance on math tasks. One hypothesized mechanism by which MA affects performance is through anxiety-related increases in working memory (WM) load, diverting resources away from mathematical computations. We examined whether this effect is specific to WM or whether the impact of MA extends to an overall depletion of executive function (EF) resources. In this fMRI experiment, we manipulated two separate factors known to impact EF demands-task-switching (TS) and increased WM load-in order to evaluate how MA relates to behavioral performance and neural activity related to mathematical calculations. Relative to a difficult non-math task (analogies), we observed MA-related deficits in math performance and reduced neural activity in a network of regions in the brain associated with arithmetic processing. In response to TS demands, higher levels of math anxiety were associated with a pattern of avoidance and disengagement. When switching from the control task, high math anxiety (HMA) was associated with disengagement from math trials, speeding through these trials, and exhibiting reduced neural activity in regions associated with arithmetic processing. The effects of math anxiety and WM were most pronounced at the lowest levels of WM load. Overall, the results of this study indicate that the effects of MA are broader than previously demonstrated and provide further insight into how EF deficits in MA might impact recruitment of neural resources that are important for successful math computations.

Demonstrating Brain-Level Interactions Between Visuospatial Attentional Demands and Working Memory Load While Driving Using Functional Near-Infrared Spectroscopy.

Driving is a complex task concurrently drawing on multiple cognitive resources. Yet, there is a lack of studies investigating interactions at the brain-level among different driving subtasks in dual-tasking. This study investigates how visuospatial attentional demands related to increased driving difficulty interacts with different working memory load (WML) levels at the brain level. Using multichannel whole-head high density functional near-infrared spectroscopy (fNIRS) brain activation measurements, we aimed to predict driving difficulty level, both separate for each WML level and with a combined model. Participants drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. In half of the time, the course led through a construction site with reduced lane width, increasing visuospatial attentional demands. Concurrently, participants performed a modified version of the n-back task with five different WML levels (from 0-back up to 4-back), forcing them to continuously update, memorize, and recall the sequence of the previous ‘n’ speed signs and adjust their speed accordingly. Using multivariate logistic ridge regression, we were able to correctly predict driving difficulty in 75.0% of the signal samples (1.955 Hz sampling rate) across 15 participants in an out-of-sample cross-validation of classifiers trained on fNIRS data separately for each WML level. There was a significant effect of the WML level on the driving difficulty prediction accuracies [range 62.2–87.1%; χ2(4) = 19.9, p < 0.001, Kruskal–Wallis H test] with highest prediction rates at intermediate WML levels. On the contrary, training one classifier on fNIRS data across all WML levels severely degraded prediction performance (mean accuracy of 46.8%). Activation changes in the bilateral dorsal frontal (putative BA46), bilateral inferior parietal (putative BA39), and left superior parietal (putative BA7) areas were most predictive to increased driving difficulty. These discriminative patterns diminished at higher WML levels indicating that visuospatial attentional demands and WML involve interacting underlying brain processes. The changing pattern of driving difficulty related brain areas across WML levels could indicate potential changes in the multitasking strategy with level of WML demand, in line with the multiple resource theory.

Working Memory-Related Effective Connectivity in Huntington's Disease Patients.

Huntington’s disease (HD) is a genetically caused neurodegenerative disorder characterized by heterogeneous motor, psychiatric, and cognitive symptoms. Although motor symptoms may be the most prominent presentation, cognitive symptoms such as memory deficits and executive dysfunction typically co-occur. We used functional magnetic resonance imaging (fMRI) and task fMRI-based dynamic causal modeling (DCM) to evaluate HD-related changes in the neural network underlying working memory (WM). Sixty-four pre-symptomatic HD mutation carriers (preHD), 20 patients with early manifest HD symptoms (earlyHD), and 83 healthy control subjects performed an n-back fMRI task with two levels of WM load. Effective connectivity was assessed in five predefined regions of interest, comprising bilateral inferior parietal cortex, left anterior cingulate cortex, and bilateral dorsolateral prefrontal cortex. HD mutation carriers performed less accurately and more slowly at high WM load compared with the control group. While between-group comparisons of brain activation did not reveal differential recruitment of the cortical WM network in mutation carriers, comparisons of brain connectivity as identified with DCM revealed a number of group differences across the whole WM network. Most strikingly, we observed decreasing connectivity from several regions toward right dorsolateral prefrontal cortex (rDLPFC) in preHD and even more so in earlyHD. The deterioration in rDLPFC connectivity complements results from previous studies and might mirror beginning cortical neural decline at premanifest and early manifest stages of HD. We were able to characterize effective connectivity in a WM network of HD mutation carriers yielding further insight into patterns of cognitive decline and accompanying neural deterioration.

A continuous time-resolved measure decoded from EEG oscillatory activity predicts working memory task performance

Objective. Working memory (WM), crucial for successful behavioral performance in most of our everyday activities, holds a central role in goal-directed behavior. As task demands increase, inducing higher WM load, maintaining successful behavioral performance requires the brain to work at the higher end of its capacity. Because it is depending on both external and internal factors, individual WM load likely varies in a continuous fashion. The feasibility to extract such a continuous measure in time that correlates to behavioral performance during a working memory task remains unsolved. Approach. Multivariate pattern decoding was used to test whether a decoder constructed from two discrete levels of WM load can generalize to produce a continuous measure that predicts task performance. Specifically, a linear regression with L2-regularization was chosen with input features from EEG oscillatory activity recorded from healthy participants while performing the n-back task, . Main results. The feasibility to extract a continuous time-resolved measure that correlates positively to trial-by-trial working memory task performance is demonstrated (r = 0.47, p < 0.05). It is furthermore shown that this measure allows to predict task performance before action (r = 0.49, p < 0.05). We show that the extracted continuous measure enables to study the temporal dynamics of the complex activation pattern of WM encoding during the n-back task. Specifically, temporally precise contributions of different spectral features are observed which extends previous findings of traditional univariate approaches. Significance. These results constitute an important contribution towards a wide range of applications in the field of cognitive brain–machine interfaces. Monitoring mental processes related to attention and WM load to reduce the risk of committing errors in high-risk environments could potentially prevent many devastating consequences or using the continuous measure as neurofeedback opens up new possibilities to develop novel rehabilitation techniques for individuals with degraded WM capacity.

Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS

Demonstrating brain-level interactions between working memory load and driving demand level using fNIRS

Demonstrating brain-level interactions between working memory load and frustration while driving using functional near-infrared spectroscopy

Demonstrating brain-level interactions between working memory load and frustration while driving using functional near-infrared spectroscopy

Affective priming and cognitive load: Event-related potentials suggest an interplay of implicit affect misattribution and strategic inhibition.

Prior research suggests that the affective priming effect denoting prime-congruent evaluative judgments about neutral targets preceded by affective primes increases when the primes are processed less deeply. This has been taken as evidence for greater affect misattribution. However, no study so far has combined an experimental manipulation of the depth of prime processing with the benefits of ERPs. Forty-seven participants made like/dislike responses about Korean ideographs following 800-ms affective prime words while 64-channel EEG was recorded. In a randomized within-subject design, three levels of working-memory load were applied specifically during prime processing. Affective priming was significant for all loads and even tended to decrease over loads, although efficiency of the load manipulation was confirmed by reduced amplitudes of posterior attention-sensitive prime ERPs. Moreover, ERPs revealed greater explicit affective discrimination of the prime words as load increased, with strongest valence effects on central/centroparietal N400 and on the parietal/parietooccipital late positive complex under high load. This suggests that (a) participants by default tried to inhibit the processing of the prime's affect, and (b) inhibition more often failed under cognitive load, thus causing emotional breakthrough that resulted in a binding of affect to the prime and, hence, reduced affect misattribution to the target. As a correlate of affective priming in the target ERP, medial-frontal negativity, a well-established marker of (low) stimulus value, increased with increasing negative affect of the prime. Findings support implicit prime-target affect transfer as a major source of affective priming, but also point to the role of strategic top-down processes.

Lie construction affects information storage under high memory load condition

Previous studies indicate that lying consumes cognitive resources, especially working memory (WM) resources. Considering the dual functions that WM might play in lying: holding the truth-related information and turning the truth into lies, the present study examined the relationship between the information storage and processing in the lie construction. To achieve that goal, a deception task based on the old/new recognition paradigm was designed, which could manipulate two levels of WM load (low-load task using 4 items and high-load task using 6 items) during the deception process. The analyses based on the amplitude of the contralateral delay activity (CDA), a proved index of the number of representations being held in WM, showed that the CDA amplitude was lower in the deception process than that in the truth telling process under the high-load condition. In contrast, under the low-load condition, no CDA difference was found between the deception and truth telling processes. Therefore, we deduced that the lie construction and information storage compete for WM resources; when the available WM resources cannot meet this cognitive demand, the WM resources occupied by the information storage would be consumed by the lie construction.

Prefrontal-parietal effective connectivity during working memory in older adults

Theoretical models and preceding studies have described age-related alterations in neuronal activation of frontoparietal regions in a working memory (WM) load-dependent manner. However, to date, underlying neuronal mechanisms of these WM load-dependent activation changes in aging remain poorly understood. The aim of this study was to investigate these mechanisms in terms of effective connectivity by application of dynamic causal modeling with Bayesian Model Selection. Eighteen healthy younger (age: 20–32 years) and 32 older (60–75 years) participants performed an n-back task with 3 WM load levels during functional magnetic resonance imaging (fMRI). Behavioral and conventional fMRI results replicated age group by WM load interactions. Importantly, the analysis of effective connectivity derived from dynamic causal modeling, indicated an age- and performance-related reduction in WM load-dependent modulation of connectivity from dorsolateral prefrontal cortex to inferior parietal lobule. This finding provides evidence for the proposal that age-related WM decline manifests as deficient WM load-dependent modulation of neuronal top-down control and can integrate implications from theoretical models and previous studies of functional changes in the aging brain.

Assessing the Driver's Current Level of Working Memory Load with High Density Functional Near-infrared Spectroscopy: A Realistic Driving Simulator Study.

Cognitive overload or underload results in a decrease in human performance which may result in fatal incidents while driving. We envision that driver assistive systems which adapt their functionality to the driver’s cognitive state could be a promising approach to reduce road accidents due to human errors. This research attempts to predict variations of cognitive working memory load levels in a natural driving scenario with multiple parallel tasks and to reveal predictive brain areas. We used a modified version of the n-back task to induce five different working memory load levels (from 0-back up to 4-back) forcing the participants to continuously update, memorize, and recall the previous ‘n’ speed sequences and adjust their speed accordingly while they drove for approximately 60 min on a highway with concurrent traffic in a virtual reality driving simulator. We measured brain activation using multichannel whole head, high density functional near-infrared spectroscopy (fNIRS) and predicted working memory load level from the fNIRS data by combining multivariate lasso regression and cross-validation. This allowed us to predict variations in working memory load in a continuous time-resolved manner with mean Pearson correlations between induced and predicted working memory load over 15 participants of 0.61 [standard error (SE) 0.04] and a maximum of 0.8. Restricting the analysis to prefrontal sensors placed over the forehead reduced the mean correlation to 0.38 (SE 0.04), indicating additional information gained through whole head coverage. Moreover, working memory load predictions derived from peripheral heart rate parameters achieved much lower correlations (mean 0.21, SE 0.1). Importantly, whole head fNIRS sampling revealed increasing brain activation in bilateral inferior frontal and bilateral temporo-occipital brain areas with increasing working memory load levels suggesting that these areas are specifically involved in workload-related processing.

Working memory load and automaticity in relation to mental multiplication

ABSTRACTThe authors’ aim was to examine the relations among mental multiplication, working memory load (WML), and automaticity by alternating the difficulty level of task characteristics. In Experiment 1, involving 30 fifth-grade students with mixed abilities, a 2 (WML) × 2 (automaticity) design was utilized. In Experiment 2, involving 21 high-achieving mathematics learners and 21 low-achieving mathematics learners in Grade 4, a 2 (WML) × 2 (automaticity) × 2 (achievement) design was utilized. Regardless of level of automaticity, individuals under low-WML conditions performed more accurately and faster. Regardless of level of WML, individuals under high automaticity conditions performed more accurately and faster. Group difference was significant. The simple effect of WML was bigger under the conditions with low automaticity, in comparison to the conditions with high automaticity. Alternating difficulty level simultaneously in 2 dimensions of testing conditions posed an amplified impact on the low-achieving group.

Prefrontal activation may predict working-memory training gain in normal aging and mild cognitive impairment

Cognitive training has been shown to result in improved behavioral performance in normal aging and mild cognitive impairment (MCI), yet little is known about the neural correlates of cognitive plasticity, or about individual differences in responsiveness to cognitive training. In this study, 21 healthy older adults and 14 patients with MCI received five weeks of adaptive computerized working-memory (WM) training. Before and after training, functional Near-Infrared Spectroscopy (fNIRS) was used to assess the hemodynamic response in left and right prefrontal cortex during performance of a verbal n-back task with varying levels of WM load. After training, healthy older adults demonstrated decreased prefrontal activation at high WM load, which may indicate increased processing efficiency. Although MCI patients showed improved behavioral performance at low WM load after training, no evidence was found for training-related changes in prefrontal activation. Whole-group analyses showed that a relatively strong hemodynamic response at low WM load was related to worse behavioral performance, while a relatively strong hemodynamic response at high WM load was related to higher training gain. Therefore, a ‘youth-like’ prefrontal activation pattern at older age may be associated with better behavioral outcome and cognitive plasticity.