Extended attention span training system

Attention Deficit Disorder (ADD) is a behavioral disorder characterized by the inability to sustain attention long enough to perform activities such as schoolwork or organized play. Treatments for this disorder include medication and brainwave biofeedback training. Brainwave biofeedback training systems feed back information to the trainee showing him how well he is producing the brainwave pattern that indicates attention. The Extended Attention Span Training (EAST) system takes the concept a step further by making a video game more difficult as the player's brainwaves indicate that attention is waning. The trainee can succeed at the game only by maintaining an adequate level of attention. The EAST system is a modification of a biocybernetic system that is currently being used to assess the extent to which automated flight management systems maintain pilot engagement. This biocybernetic system is a product of a program aimed at developing methods to evaluate automated flight deck designs for compatibility with human capabilities. The EAST technology can make a contribution in the fields of medical neuropsychology and neurology, where the emphasis is on cautious, conservative treatment of youngsters with attention disorders

ERPs and their brain sources in perceptual and conceptual prospective memory tasks: commonalities and differences between the two tasks

The present study examined whether Event-Related Potential (ERP) components and their neural generators are common to perceptual and conceptual prospective memory (PM) tasks or specific to the form of PM cue involved. We used Independent Component Analysis (ICA) to study the contributions of brain source activities to scalp ERPs across the different phases of two event-based PM-tasks: (1) holding intentions during a delay (monitoring) (2) detecting the correct context to perform the delayed intention (cue detection) and (3) carrying out the action (realisation of delayed intentions). Results showed that monitoring for both perceptual and conceptual PM-tasks was characterised by an enhanced early occipital negativity (N200). In addition the conceptual PM-task showed a long-lasting effect of monitoring significant around 700 ms. Perceptual PM-task cues elicited an N300 enhancement associated with cue detection, whereas a midline N400-like response was evoked by conceptual PM-task cues. The Prospective Positivity associated with realisation of delayed intentions was observed in both conceptual and perceptual tasks. A common frontal-midline brain source contributed to the Prospective Positivity in both tasks and a strong contribution from parieto-frontal brain sources was observed only for the perceptually cued PM-task. These findings support the idea that: (1) The enhanced N200 can be understood as a neural correlate of a ‘retrieval mode’ for perceptual and conceptual PM-tasks, and additional strategic monitoring is implemented according the nature of the PM task; (2) ERPs associated with cue detection are specific to the nature of the PM cues; (3) Prospective Positivity reflects a general PM process, but the specific brain sources contributing to it depend upon the nature of the PM task

Brainatwork: Logging Cognitive Engagement and Tasks in the Workplace Using Electroencephalography

Today's workplaces are dynamic and complex. Digital data sources such as email and video conferencing aim to support workers but also add to their burden of multitasking. Psychophysiological sensors such as Electroencephalography (EEG) can provide users with cues about their cognitive state. We introduce BrainAtWork, a workplace engagement and task logger which shows users their cognitive state while working on different tasks. In a lab study with eleven participants working on their own real-world tasks, we gathered 16 hours of EEG and PC logs which were labeled into three classes: central, peripheral and meta work. We evaluated the usability of BrainAtWork via questionnaires and interviews. We investigated the correlations between measured cognitive engagement from EEG and subjective responses from experience sampling probes. Using random forests classification, we show the feasibility of automatically labeling work tasks into work classes. We discuss how BrainAtWork can support workers on the long term through encouraging reflection and helping in task scheduling

A new perspective for the training assessment: Machine learning-based neurometric for augmented user's evaluation

Inappropriate training assessment might have either high social costs and economic impacts, especially in high risks categories, such as Pilots, Air Traffic Controllers, or Surgeons. One of the current limitations of the standard training assessment procedures is the lack of information about the amount of cognitive resources requested by the user for the correct execution of the proposed task. In fact, even if the task is accomplished achieving the maximum performance, by the standard training assessment methods, it would not be possible to gather and evaluate information about cognitive resources available for dealing with unexpected events or emergency conditions. Therefore, a metric based on the brain activity (neurometric) able to provide the Instructor such a kind of information should be very important. As a first step in this direction, the Electroencephalogram (EEG) and the performance of 10 participants were collected along a training period of 3 weeks, while learning the execution of a new task. Specific indexes have been estimated from the behavioral and EEG signal to objectively assess the users' training progress. Furthermore, we proposed a neurometric based on a machine learning algorithm to quantify the user's training level within each session by considering the level of task execution, and both the behavioral and cognitive stabilities between consecutive sessions. The results demonstrated that the proposed methodology and neurometric could quantify and track the users' progresses, and provide the Instructor information for a more objective evaluation and better tailoring of training programs. © 2017 Borghini, Aricò, Di Flumeri, Sciaraffa, Colosimo, Herrero, Bezerianos, Thakor and Babiloni

The Effects Of Modulating Accommodative-Vergence Stress Within The Context Of Operator Performance On Automated System Tasks

Automated systems (e.g., self-driving cars, autopilot) can reduce an operator’s (i.e., driver, pilot, baggage screener) task engagement, which can result in mind wandering, distraction, and loss of concentration. Consequently, unfavorable performance outcomes, such as missed critical signals and slow responses to emergency events, can occur. Because automation reverts the operator to a “visual monitoring” role, the oculomotor accommodative-vergence responses (the oculomotor responses that maintain a single focused image on the retina) may play a vital role in human-automation interactions. Prior research has shown that individuals with deficits in the accommodative-vergence responses can exhibit inattentive symptoms (e.g., poor concentration) characteristic of attention-deficit/hyperactivity disorder (ADHD) while performing prolonged close work (e.g., reading). Given the behavioral symptoms present in those experiencing accommodative-vergence stress, automated systems may exacerbate these negative effects. The current study examined the effects of accommodative-vergence stress in combination with automation on aspects of operator task engagement. Participants (N = 95) under accommodative-vergence stress wearing -2.0 diopter lenses or normal viewing conditions completed a 40 min flight simulation task either with or without automation. Physiological dependent measures included electroencephalographic (EEG) parietal-occipital alpha power spectral density (PSD), an EEG multivariate metric of engagement, and pupil diameter. Self-report measures of task engagement, cognitive fatigue, and visual fatigue symptoms were also collected along with oculomotor measurements (accommodation and convergence) and flight simulation task performance. Multivariate analyses indicated that the application of -2.0 diopter lenses did not significantly alter oculomotor measurements or subjective reports of visual fatigue. Oculomotor stress modestly affected task performance and tended to result in increased EEG measures of engagement, while subsequently increasing feelings of fatigue, potentially indicating a compensatory effort response. Participants performing the simulation with automation exhibited significantly lower task engagement, as indicated by greater parietal-occipital alpha PSD, less multivariate EEG engagement, smaller pupil diameter, and lower self-reported engagement. Overall, oculomotor stress and automation did not interact synergistically to affect task engagement and associated performance outcomes. Automation and time on task were the main determinants of task engagement. These results underscore the negative effects automation can have on underlying operator cognitive states and the associated need to carefully design automation to combat reduced task engagement. Applications for system design and the use of EEG in augmented cognition systems involving automation are discussed

Electrophysiological indices of anterior cingulate cortex function reveal changing levels of cognitive effort and reward valuation that sustain task performance

Successful execution of goal-directed behaviors often requires the deployment of cognitive control, which is thought to require cognitive effort. Recent theories have proposed that anterior cingulate cortex (ACC) regulates control levels by weighing the reward-related benefits of control against its effort-related costs. However, given that the sensations of cognitive effort and reward valuation are available only to introspection, this hypothesis is difficult to investigate empirically. We have proposed that two electrophysiological indices of ACC function, frontal midline theta and the reward positivity (RewP), provide objective measures of these functions. To explore this issue, we recorded the electroencephalogram (EEG) from participants engaged in an extended, cognitively-demanding task. Participants performed a time estimation task for 2 h in which they received reward and error feedback according to their task performance. We observed that the amplitude of the RewP, a feedback-locked component of the event related brain potential associated with reward processing, decreased with time-on-task. Conversely, frontal midline theta power, which consists of 4-8 Hz EEG oscillations associated with cognitive effort, increased with time-on-task. We also explored how these phenomena changed over time by conducting within-participant multi-level modeling analyses. Our results suggest that extended execution of a cognitively-demanding task is characterized by an early phase in which high control levels foster rapid improvements in task performance, and a later phase in which high control levels were necessary to maintain stable task performance, perhaps counteracting waning reward valuation

Spatiotemporal Neurodynamics Underlying Internally and Externally Driven Temporal Prediction: A High Spatial Resolution ERP Study

Temporal prediction (TP) is a flexible and dynamic cognitive ability. Depending on the internal or external nature of information exploited to generate TP, distinct cognitive and brain mechanisms are engaged with the same final goal of reducing uncertainty about the future. In this study, we investigated the specific brain mechanisms involved in internally and externally driven TP. To this end, we employed an experimental paradigm purposely designed to elicit and compare externally and internally driven TP and a combined approach based on the application of a distributed source reconstruction modeling on a high spatial resolution electrophysiological data array. Specific spatiotemporal ERP signatures were identified, with significant modulation of contingent negative variation and frontal late sustained positivity in external and internal TP contexts, respectively. These different electrophysiological patterns were supported by the engagement of distinct neural networks, including a left sensorimotor and a prefrontal circuit for externally and internally driven TP, respectively

Differentiating Active And Passive Fatigue States With The Use Of Electroencephalography

With advances in automation technology, it is becoming essential to understand how automation affects human operators. A concern for the implementation of automation technology is the interactive effects it has with operator cognitive fatigue. Desmond and Hancock (2001) proposed that two types of fatigue can arise depending on the nature of the task: active and passive. Active fatigue results when operators must make constant perceptual-motor adjustments during high task demands, while passive fatigue results from operators executing little or no perceptual-motor adjustments during low task demands, similar to when automation is employed. The purpose of this study was to use electroencephalographic (EEG) indices of workload, engagement, and a candidate marker of strain under fatigue in conjunction with performance and subjective measures to differentiate active and passive fatigue states. Participants (N = 84) performed a generalized flight simulator for 62 min either under active, passive, or control conditions. Passive fatigue was characterized by reduced EEG engagement and initially elevated and stable ratios of Fz theta to POz alpha power compared to active fatigue. Subjective measure results indicated that passive fatigue was characterized by reduced ratings of alertness and workload compared to active fatigue. No performance differences were observed between fatigue conditions; however, an overall speed-accuracy trade-off was observed from pre to post fatigue induction. This study demonstrated that different fatigue states produce different effects on EEG indices. These results have potential applications for developing augmented cognition technologies that deliver appropriate fatigue countermeasures in automated operational environments

Behavioral and electrophysiological correlates of cognitive control in ex-obese adults

Impaired cognitive control functions have been documented in obesity. It remains unclear whether these functions normalize after weight reduction. We compared ex-obese individuals, who successfully underwent substantial weight loss after bariatric surgery, to normal weight participants on measures of resistance to interference, cognitive flexibility and response inhibition, obtained from the completion of two Stroop tasks, a Switching task and a Go/NoGo task, respectively. To elucidate the underlying brain mechanisms, event-related potentials (ERPs) in the latter two tasks were examined. As compared to controls, patients were more susceptible to the predominant but task-irrelevant stimulus dimension (i.e., they showed a larger verbal Stroop effect), and were slower in responding on trials requiring a task-set change rather than a task-set repetition (i.e., they showed a larger switch cost). The ERP correlates revealed altered anticipatory control mechanisms (switch positivity) and an exaggerated conflict monitoring response (N2). The results suggest that cognitive control is critical even in ex-obese individuals and should be monitored to promote weight loss maintenance

Internet and gaming addiction: a systematic literature review of neuroimaging studies

In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction. Internet addiction has been considered as a serious threat to mental health and the excessive use of the Internet has been linked to a variety of negative psychosocial consequences. The aim of this review is to identify all empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging studies offer an advantage over traditional survey and behavioral research because with this method, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. A systematic literature search was conducted, identifying 18 studies. These studies provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet and gaming addiction, on a variety of levels. On the molecular level, Internet addiction is characterized by an overall reward deficiency that entails decreased dopaminergic activity. On the level of neural circuitry, Internet and gaming addiction led to neuroadaptation and structural changes that occur as a consequence of prolonged increased activity in brain areas associated with addiction. On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their cognitive functioning in various domains. The paper shows that understanding the neuronal correlates associated with the development of Internet and gaming addiction will promote future research and will pave the way for the development of addiction treatment approaches