Shedding light on the prefrontal correlates of mental workload in simulated driving: a functional near-infrared spectroscopy study

Assessment of mental workload states in monitors using functional near-infrared spectroscopy

BackgroundAs technology develops rapidly and complex systems become more widespread, the study of mental workload (MWL) for monitors has become increasingly important.ObjectiveTo explore the signaling features of functional near-infrared spectroscopy (fNIRS) in different MWL states.MethodsTwenty participants performed the N-back task and fNIRS data were collected to measure changes in blood oxygenation levels in the prefrontal cortex. Additionally, performance metrics and NASA Task Load Index (NASA-TLX) questionnaire results were recorded.ResultsNASA-TLX scores increased progressively from 0-back (M = 8.750) to 1-back (M = 34.792), 2-back (M = 52.292), and 3-back (M = 75.625). Significant differences were observed in oxygenated hemoglobin (HbO) concentrations (F3, 57 = 10.557, p < 0.001) across these tasks. HbO increased from 0-back (M = -0.008) to 1-back (M = -0.002) and 2-back (M = -0.001), but decreased to 3-back (M = -0.002). Channels CH16, CH17, and CH18, located in the left dorsolateral prefrontal cortex (DLPFC), were significantly activated in the 1-back, 2-back, and 3-back conditions. CH1 and CH2, which are located in the right DLPFC, showed significant activation in both the 2-back and 3-back tasks.ConclusionsOur results showed that different levels of the N-back tasks triggered different MWL states. Significant differences in HbO concentrations and activated channels were observed across tasks with varying MWLs. HbO followed an "inverted U" pattern, increasing as the MWL shifted from underload to normal, and subsequently decreasing as it reached overload. Additionally, the left DLPFC was activated earlier than the right DLPFC, with the latter gradually engaging as the MWL increased. This study provides valuable insights into the assessment of MWL states.

Neural architecture of social punishment: Insights from a queue-jumping scenario

Punishment in social settings is crucial for maintaining collective interests, yet the underlying mechanisms remain unclear. To address this, we developed a paradigm, the queue-jumping task, where participants imagine experiencing a queue-jumping event through vivid pictorial scenarios. Behavioral findings revealed that individuals prioritized collective interests over personal ones when punishing, highlighting the altruistic nature of social punishment. Neuroimaging results demonstrated that social punishment activated multiple neural circuits associated with social norms (e.g., fusiform gyrus and posterior cingulate cortex), self-related processing (e.g., ventromedial prefrontal cortex and middle cingulate cortex), and punishment implementation (e.g., anterior dorsolateral prefrontal cortex and middle temporal gyrus). Brain network analyses uncovered a social punishment network whose efficacy in information transmission forecasts individuals' tendency to punish. This study provides valuable insights into the cognitive and neural mechanisms involved in social punishment. The current paradigm closely reflects real-life queue-jumping situations and daily punitive behaviors, demonstrating its generalizability and validity.

Neuromodulation on the ground and in the clouds: a mini review of transcranial direct current stimulation for altering performance in interactive driving and flight simulators

Transcranial direct current stimulation (tDCS) has emerged as a promising tool for cognitive enhancement, especially within simulated virtual environments that provide realistic yet controlled methods for studying human behavior. This mini review synthesizes current research on the application of tDCS to improve performance in interactive driving and flight simulators. The existing literature indicates that tDCS can enhance acute performance for specific tasks, such as maintaining a safe distance from another car or executing a successful plane landing. However, the effects of tDCS may be context-dependent, indicating a need for a broader range of simulated scenarios. Various factors, including participant expertise, task difficulty, and the targeted brain region, can also influence tDCS outcomes. To further strengthen the rigor of this research area, it is essential to address and minimize different forms of research bias to achieve true generalizability. This comprehensive analysis aims to bridge the gap between theoretical understanding and practical application of neurotechnology to study the relationship between the brain and behavior, ultimately providing insights into the effectiveness of tDCS in transportation settings.

Neuroanatomical correlates of distracted straight driving performance: a driving simulator MRI study across the lifespan

Background

Driving is the preferred mode of transportation for adults across the healthy age span. However, motor vehicle crashes are among the leading causes of injury and death, especially for older adults, and under distracted driving conditions. Understanding the neuroanatomical basis of driving may inform interventions that minimize crashes. This exploratory study examined the neuroanatomical correlates of undistracted and distracted simulated straight driving.

Methods

One-hundred-and-thirty-eight participants (40.6% female) aged 17-85 years old (mean and SD = 58.1 ± 19.9 years) performed a simulated driving task involving straight driving and turns at intersections in a city environment using a steering wheel and foot pedals. During some straight driving segments, participants responded to auditory questions to simulate distracted driving. Anatomical T1-weighted MRI was used to quantify grey matter volume and cortical thickness for five brain regions: the middle frontal gyrus (MFG), precentral gyrus (PG), superior temporal cortex (STC), posterior parietal cortex (PPC), and cerebellum. Partial correlations controlling for age and sex were used to explore relationships between neuroanatomical measures and straight driving behavior, including speed, acceleration, lane position, heading angle, and time speeding or off-center. Effects of interest were noted at an unadjusted p-value threshold of 0.05.

Results

Distracted driving was associated with changes in most measures of straight driving performance. Greater volume and cortical thickness in the PPC and cerebellum were associated with reduced variability in lane position and heading angle during distracted straight driving. Cortical thickness of the MFG, PG, PPC, and STC were associated with speed and acceleration, often in an age-dependent manner.

Conclusion

Posterior regions were correlated with lane maintenance whereas anterior and posterior regions were correlated with speed and acceleration, especially during distracted driving. The regions involved and their role in straight driving may change with age, particularly during distracted driving as observed in older adults. Further studies should investigate the relationship between distracted driving and the aging brain to inform driving interventions.

Turning the Light Switch on Binding: Prefrontal Activity for Binding and Retrieval in Action Control

According to action control theories, responding to a stimulus leads to the binding of response and stimulus features into a common representation, that is, an event file. Repeating any component of an event file retrieves all previously bound information, leading to performance costs for partial repetitions measured in so-called binding effects. Although otherwise robust and stable, binding effects are typically completely absent in "localization tasks," in which participants localize targets with spatially compatible responses. Yet, it is possible to observe binding effects in such when location features have to be translated into response features. We hypothesized that this modulation of binding effects is reflected in task involvement of the dorsolateral pFC (DLPFC). Participants localized targets with either direct (i.e., spatially compatible key) or translated (i.e., diagonally opposite to the spatially compatible key) responses. We measured DLPFC activity with functional near-infrared spectroscopy. On the behavioral level, we observed binding effects in the translated response condition, but not in the direct response condition. Importantly, prefrontal activity was also higher in the translated mapping condition. In addition, we found some evidence for the strength of the difference in binding effects in behavioral data being correlated with the corresponding effects in prefrontal activity. This suggests that activity in the DLPFC reflects the amount of executive control needed for translating location features into responses. More generally, binding effects seem to emerge only when the task at hand involves DLPFC recruitment.

A systematic review on the influence factors, measurement, and effect of driver workload

Driver workload (DWL) is an important factor that needs to be considered in the study of traffic safety. The research focus on DWL has undergone certain shifts with the rapid development of scientific and technological advancements in the field of transportation in recent years. This study aims to grasp the state of research on DWL by both bibliometric analysis and individual critical literature review. The knowledge structure and development trend are described using bibliometric analysis. The knowledge mapping method is applied to mine the available literature in depth. It is discovered that one of the current research focus on DWL has shifted towards investigating its application in the field of autonomous driving. Subjective questionnaires and experimental tests (including both simulation technology and field study) are the main approaches to analyze DWL. An individual critical literature review of the influencing factors, measurement, and performance of DWL is provided. Research findings have shown that DWL was highly impacted by both intrinsic (e.g., age, temperament, driving experience) and external factors (e.g., vehicles, roads, tasks, environments). Scholars are actively exploring the combined effects of various factors and the level of vehicle automation on DWL. In addition to assess DWL by using subjective measures or physiological parameter measures separately, studies have started to improve classification accuracy by combining multiple measurement methods. Safety thresholds of DWL are not sufficiently studied due to the various interference items corresponding to different scenarios, but it is expected to quantify the DWL and find the threshold by establishing assessment models considering these intrinsic and external multiple-factors simultaneously. Driver or vehicle performance indicators are controversial to measure DWL directly, but they were suitable to reflect the impact of DWL in different driving conditions.

Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS

For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.

A functional near-infrared spectroscopy study on the prefrontal correlates of cognitive offloading via a personal knowledge assistant

The saving of previously encoded information boosts both memory for subsequent information (saving-enhanced memory; SEM) as well as cognitive performance in general (saving-enhanced performance; SEP). These findings have been replicated in a setting that involves the assistance by an intelligent software that automatically structures and saves work content in an interactive sidebar. It is assumed that beneficial effects on cognitive performance due to (automatic) saving are caused by a reduction in current workload by means of cognitive offloading. We tested this assumption by measuring neural activity in the dorsolateral prefrontal cortex (DLPFC) via functional near infrared spectroscopy (fNIRS)-once after saving and once after deleting of previously collected information that had to be recalled later-on. On a behavioral level, there was a brief benefit of saving. However, cognitive offloading became most apparent on a neural level: after saving, participants showed significantly lower activation in the right DLPFC. Also, the more participants benefited from cognitive offloading, the more they were able to re-access previously collected, saved information. Thus, fNIRS results indicated reduced mental load after saving, confirming the assumption that saving triggers cognitive offloading.

Assessing the development of mental fatigue during simulated flights with concurrent EEG-fNIRS measurement

Mental fatigue (MF) can impair pilots' performance and reactions to unforeseen events and is therefore an important concept within aviation. The physiological measurement of MF, especially with EEG and, in recent years, fNIRS, has gained much attention. However, a systematic investigation and comparison of the measurements is seldomly done. We induced MF via time on task during a 90-min simulated flight task and collected concurrent EEG-fNIRS, performance and self-report data from 31 participants. While their subjective MF increased linearly, the participants were able to keep their performance stable over the course of the experiment. EEG data showed an early increase and levelling in parietal alpha power and a slower, but steady increase in frontal theta power. No consistent trend could be observed in the fNIRS data. Thus, more research on fNIRS is needed to understand its possibilities and limits for MF assessment, and a combination with EEG is advisable to compare and validate results. Until then, EEG remains the better choice for continuous MF assessment in cockpit applications because of its high sensitivity to a transition from alert to fatigued, even before performance is impaired.

Lifestyle Matters: Effects of Habitual Physical Activity on Driving Skills in Older Age

Research on multitasking driving has suggested age-related deterioration in driving performance. It has been shown that physical and cognitive functioning, which are related to driving performance and decline with aging, are positively associated with physical activity behavior. This study aimed to explore whether driving performance decline becomes severe with advancing age and whether physical activity behavior modifies age-related deterioration in driving performance. A total of one hundred forty-one healthy adults were categorized into three groups based on their age; old-old (74.21 ± 2.33 years), young-old (66.53 ± 1.50 years), and young adults (23.25 ± 2.82 years). Participants completed a realistic multitasking driving task. Physical activity and cardiorespiratory fitness levels were evaluated. Older groups drove more slowly and laterally than young adults, and old-old adults drove slower than young-old ones across the whole driving course. Physical activity level did not interact with the aging effect on driving performance, whereas cardiovascular fitness interacted. Higher-fitness young-old and young adults drove faster than higher-fitness old-old adults. Higher-fitness old adults drove more laterally than higher-fitness young adults. The present study demonstrated a gradual decline in driving performance in old adults, and cardiorespiratory fitness interacted with the aging effect on driving performance. Future research on the interaction of aging and physical activity behavior on driving performance in different age groups is of great value and may help deepen our knowledge.

Investigating mental workload-induced changes in cortical oxygenation and frontal theta activity during simulated flights

Monitoring pilots' cognitive states becomes increasingly important in aviation. Physiological measurement can detect increased mental workload (MWL) even before performance declines. Yet, changes in MWL are rarely varied systematically and few studies control for confounding effects of other cognitive states. The present study targets these shortcomings by analysing the effects of stepwise increased MWL on cortical activation, while controlling for mental fatigue (MF). 35 participants conducted a simulated flight with an incorporated adapted n-back and monitoring task. We recorded cortical activation with concurrent EEG and fNIRS measurement, performance, self-reported MWL and MF. Our results show the successful manipulation of MWL without confounding effects of MF. Higher task difficulty elicited higher subjective MWL ratings, performance decline, higher frontal theta activity and reduced frontal deoxyhaemoglobin (Hbr) concentration. Using both EEG and fNIRS, we could discriminate all induced MWL levels. fNIRS was more sensitive to tasks with low difficulty, and EEG to tasks with high difficulty. Our findings further suggest a plateau effect for high MWL that could present an upper boundary to individual cognitive capacity. Our results highlight the benefits of physiological measurement in aviation, both for assessment of cognitive states and as a data source for adaptive assistance systems.

fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees

Prosthetic arms are designed to assist amputated individuals in the performance of the activities of daily life. Brain machine interfaces are currently employed to enhance the accuracy as well as number of control commands for upper limb prostheses. However, the motion prediction for prosthetic arms and the rehabilitation of amputees suffering from transhumeral amputations is limited. In this paper, functional near-infrared spectroscopy (fNIRS)-based approach for the recognition of human intention for six upper limb motions is proposed. The data were extracted from the study of fifteen healthy subjects and three transhumeral amputees for elbow extension, elbow flexion, wrist pronation, wrist supination, hand open, and hand close. The fNIRS signals were acquired from the motor cortex region of the brain by the commercial NIRSport device. The acquired data samples were filtered using finite impulse response (FIR) filter. Furthermore, signal mean, signal peak and minimum values were computed as feature set. An artificial neural network (ANN) was applied to these data samples. The results show the likelihood of classifying the six arm actions with an accuracy of 78%. The attained results have not yet been reported in any identical study. These achieved fNIRS results for intention detection are promising and suggest that they can be applied for the real-time control of the transhumeral prosthesis.

Illuminating the prefrontal neural correlates of action sequence disassembling in response-response binding

Execution of two independent actions in quick succession results in transient binding of these two actions. Subsequent repetition of any of these actions automatically retrieves the other. This process is probably fundamental for developing complex action sequences. However, rigid bindings between two actions are not always adaptive. Sometimes, it is necessary to repeat only one of the two previously executed actions. In such situations, stored action sequences must be disassembled, for the sake of flexibility. Exact mechanisms that allow for such an active unbinding of actions remain largely unknown, but it stands to reason, that some form of prefrontal executive control is necessary. Building on prior neuronal research that explored other forms of binding (e.g. between distractors and responses and abstract representations and responses), we explored middle and superior frontal correlates of -response binding in a sequential classification task with functional near-infrared spectroscopy. We found that anterior dorsolateral prefrontal cortex activity varied as a function of response-repetition condition. Activity in the right anterior dorsolateral prefrontal cortex correlated with changes in reaction times due to response-response binding. Our results indicate that the right anterior dorsolateral prefrontal cortex dismantles bindings between consecutive actions, whenever such bindings interfere with current action goals.