Owing to the recent advances in neurotechnology and the progress in understanding of brain cognitive functions, improvements of cognitive performance or acceleration of learning process with brain enhancement systems is not out of our reach anymore, on the contrary, it is a tangible target of contemporary research. Although a variety of approaches have been proposed, we will mainly focus on cognitive training interventions, in which learners repeatedly perform cognitive tasks to improve their cognitive abilities. In this review article, we propose that the learning process during the cognitive training can be facilitated by an assistive system monitoring cognitive workloads using electroencephalography (EEG) biomarkers, and the brain connectome approach can provide additional valuable biomarkers for facilitating leaners’ learning processes. For the purpose, we will introduce studies on the cognitive training interventions, EEG biomarkers for cognitive workload, and human brain connectome. As cognitive overload and mental fatigue would reduce or even eliminate gains of cognitive training interventions, a real-time monitoring of cognitive workload can facilitate the learning process by flexibly adjusting difficulty levels of the training task. Moreover, cognitive training interventions should have effects on brain sub-networks, not on a single brain region, and graph theoretical network metrics quantifying topological architecture of the brain network can differentiate with respect to individual cognitive states as well as to different individuals’ cognitive abilities, suggesting that the connectome is a valuable approach for tracking the learning progress. Although only a few studies have exploited the connectome approach for studying alterations of the brain network induced by cognitive training interventions so far, we believe that it would be a useful technique for capturing improvements of cognitive functions.

Generally, the training evaluation methods consist in experts supervision and qualitative check of the operator's skills improvement by asking them to perform specific tasks and by verifying the final performance. The aim of this work is to find out if it is possible to obtain quantitative information about the degree of the learning process throughout the training period by analyzing neuro-physiological signals, such as the electroencephalogram, the electrocardiogram and the electrooculogram. In fact, it is well known that such signals correlate with a variety of cognitive processes, e.g. attention, information processing, and working memory. A group of 10 subjects have been asked to train daily with the NASA multi-attribute-task-battery. During such training period the neuro-physiological, behavioral and subjective data have been collected. In particular, the neuro-physiological signals have been recorded on the first (T1), on the third (T3) and on the last training day (T5), while the behavioral and subjective data have been collected every day. Finally, all these data have been compared for a complete overview of the learning process and its relations with the neuro-physiological parameters. It has been shown how the integration of brain activity, in the theta and alpha frequency bands, with the autonomic parameters of heart rate and eyeblink rate could be used as metric for the evaluation of the learning progress, as well as the final training level reached by the subjects, in terms of request of cognitive resources.

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The analysis of the topology and organization of brain networks is known to greatly benefit from network measures in graph theory. However, to evaluate dynamic changes of brain functional connectivity, more sophisticated quantitative metrics characterizing temporal evolution of brain topological features are required. To simplify conversion of time-varying brain connectivity to a static graph representation is straightforward but the procedure loses temporal information that could be critical in understanding the brain functions. To extend the understandings of functional segregation and integration to a dynamic fashion, we recommend dynamic graph metrics to characterise temporal changes of topological features of brain networks. This study investigated functional segregation and integration of brain networks over time by dynamic graph metrics derived from EEG signals during an experimental protocol: performance of complex flight simulation tasks with multiple levels of difficulty. We modelled time-varying brain functional connectivity as multi-layer networks, in which each layer models brain connectivity at time window t + Δt. Dynamic graph metrics were calculated to quantify temporal and topological properties of the network. Results show that brain networks under the performance of complex tasks reveal a dynamic small-world architecture with a number of frequently connected nodes or hubs, which supports the balance of information segregation and integration in brain over time. The results also show that greater cognitive workloads caused by more difficult tasks induced a more globally efficient but less clustered dynamic small-world functional network. Our study illustrates that task-related changes of functional brain network segregation and integration can be characterized by dynamic graph metrics.

ObjectivesIt is commonly believed that individuals make choices based upon their preferences and have access to the reasons for their choices. Recent studies in several areas suggest that this is not always the case. In choice blindness paradigms, two-alternative forced-choice in which chosen-options are later replaced by the unselected option, individuals often fail to notice replacement of their chosen option, confabulate explanations for why they chose the unselected option, and even show increased preferences for the unselected-but-replaced options immediately after choice (seconds). Although choice blindness has been replicated across a variety of domains, there are numerous outstanding questions. Firstly, we sought to investigate how individual- or trial-factors modulated detection of the manipulations. Secondly, we examined the nature and temporal duration (minutes vs. days) of the preference alterations induced by these manipulations.MethodsParticipants performed a computerized choice blindness task, selecting the more attractive face between presented pairs of female faces, and providing a typewritten explanation for their choice on half of the trials. Chosen-face cue manipulations were produced on a subset of trials by presenting the unselected face during the choice explanation as if it had been selected. Following all choice trials, participants rated the attractiveness of each face individually, and rated the similarity of each face pair. After approximately two weeks, participants re-rated the attractiveness of each individual face online.ResultsParticipants detected manipulations on only a small proportion of trials, with detections by fewer than half of participants. Detection rates increased with the number of prior detections, and detection rates subsequent to first detection were modulated by the choice certainty. We show clear short-term modulation of preferences in both manipulated and non-manipulated explanation trials compared to choice-only trials (with opposite directions of effect). Preferences were altered in the direction that subjects were led to believe they selected.