Improve Vigilance Levels -
Whereas AV might be a component more related to the arousal mechanisms of attention, EV might be rather considered as a goal-directed component of vigilance. In this vein, prior research has shown that AV is particularly related to the phasic alertness state Luna, Roca, et al.
In line with these findings, the present outcomes seem to support that EV is relatively more associated with the executive control decrement than AV, but future studies should more deeply examine this critical issue.
It is worth noting that, although the modulation observed by executive control performance across time-on-task over the EV decrement seems to fit well with the resource-control theory, the effect sizes observed in the present experiment are rather small.
Furthermore, note that even participants in the group who did not show any decrement in executive control across time-on-task some of them showed in fact a progressive increment still showed a significant decrement in EV. All this evidence points to the fact that the outcomes observed from the present data, which—importantly—were gathered from a large sample size, seem to explain only a small part of the full variance.
This is an important aspect of the present results since the resource-control theory highlights the importance of the decrease in executive control as the main reason for the vigilance decrement. Thus, such small effect sizes are controversial when it comes to concluding evidence in favor of this theory as it is currently framed.
Conversely, the present results raise the need to consider other unknown factors apart from the decrease in executive control as contributing to the vigilance decrement. One of the potential variables to consider could be motivation Hockey, In this vein, Reteig et al.
Introducing a motivational manipulation during the performance of ANTI-Vea could shed light on this question. Furthermore, it should be noted that no causal inference can be derived from the present results.
Given that the resource-control theory explains the vigilance decrement as a consequence of the executive control depletion, future studies should test this theory in a causal manner, for instance, using noninvasive stimulation techniques such as transcranial direct current stimulation or transcranial magnetic stimulation Rossini et al.
Because we reanalyzed already gathered data, no mind-wandering measures were available. Importantly, we consider the current research as a first step into testing some of the predictions of the resource-control theory, by measuring—to the best of our knowledge—for the first time both the vigilance and the executive control decrement simultaneously.
Thus, future studies should consider adding mind-wandering measures to the ANTI-Vea task to further examine the predictions derived from the resource-control theory, for instance, by introducing thought probes within the task Seli et al.
Finally, one major challenge for future studies would be to improve reliability in the score used to assess executive control change across time-on-task—which would solve some of the limitations observed in the present study—while still simultaneously measuring the vigilance decrement within the same task.
To conclude, the present study presents novel evidence regarding some of the predictions stated by the resource-control theory. In particular, using a suitable task for simultaneously measuring the vigilance decrement phenomenon and changes in executive control across blocks, we provide evidence for the first time that executive control decreases across time-on-task along with EV.
Importantly, the EV decrement was larger in those participants wherein executive control decreases than in those participants in which executive control does not decrease across time-on-task. This set of outcomes provides partial support for the resource-control predictions about the vigilance decrement phenomenon.
However, given the small effect sizes observed in datasets gathered from a large sample size, together with the fact that the relationship was consistently observed only for EV but not for AV, we can conclude that there must be additional variables, not considered by the resource-control theory, explaining the vigilance decrement.
Future research should also study causal mechanisms of the executive control decrement on the changes of EV and AV across time-on-task and the role of mind-wandering on the resource-control predictions.
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and J. Detection of vigilance decrement has been also made possible using event related potentials ERP. Long latency ERP components such as P suggested drop in vigilance Smith et al. Decrease in amplitudes of ERP components with vigilance decrement were also reported Kam et al.
Efforts have been made by researchers to understand vigilance and develop strategies to reduce vigilance decrement with the intention of enhancing the level of task performance in long duration activities that require sustained attention. Neurofeedback training using EEG and fMRI based measures is usually employed as a strategy to train individuals to reduce inattention and mind-wandering.
For example, deBettencourt et al. Neurofeedback attention training is also used in Wang et al. A monitoring task in an industrial environment was designed. The subjects were asked to identify a target amongst distractors. We used noisy visual stimulus in the form of artificially simulated rain to act as challeging stimulus during monotonous visual search.
In our previous studies, we found that noisy visual stimulus will increase mental workload Bodala et al. Hence, we used noisy visual stimulus rain to create challenge in this study. EEG and eye tracking data were collected and analyzed to study the changes in vigilance levels due to challenge integration.
The details of the experiment task and data collection are discussed in the next section. In Section 3, the results obtained from the analysis of EEG and eye tracking data were summarized and correlation analyses between significant EEG and eye tracking measures were performed.
In addition, the eye tracking data obtained from a small cohort of 4 subjects was analyzed to compare the changes between sessions with and without challenge integration.
The findings regarding the effect of challenge integration on the task and future directions are discussed in detail in Section 4 followed by concluding remarks in Section 5. Four additional subjects 1 female with similar demographics were recruited to participate in the control study.
The study was approved by the Institutional Review Board IRB of the National University of Singapore. Written informed consent was obtained from each participant before the beginning of the experiment.
All participants received monetary compensation for their time upon completion of the experiment. The experimental setup is shown in Figure 1.
com was used to record EEG data. Extra electrodes were used to collect electrocardiogram ECG and horizontal and vertical electrooculogram hEOG and vEOG.
Nonetheless, the subjects were instructed not to make any drastic head movements. A marker was placed on the forehead of the subjects to correct for the changes in head position automatically by the tracker. The monitoring scene was presented to the subject on the stimulus monitor.
The control PC with MATLAB and Psychtoolbox was used for running the codes to synchronize data collection and send event markers to eye tracker and EEG amplifier using a parallel port connection. The experiment was carried out in a quiet room with controlled level of luminance. Figure 1.
The experimental setup showing EEG and eye tracking data acquisition. Subject wearing EEG cap is seated in front of stimulus monitor and eye tracking camera. Before the experiment, the subjects underwent a color blindness test. After signing the consent form, the subject was seated such that the distance between the eyes and the monitor is approximately 50 cm corresponding to a visual angle of 40 × 30°.
Before the start of the experiment, the eye tracking system was calibrated. Each experiment lasted for approximately 90 min including subject preparation and the task with simultaneous data collection. The experiment paradigm was designed to test our hypothesis regarding challenge integration with the actual monitoring function.
In this experiment, the target was selected to be an intruder wearing a military uniform Figure 2C appearing in different locations in an industrial plant or warehouse. At the beginning, the subject received sufficient training ~2—3 min to differentiate between the target and the distractors Figure 2D.
The subject was then asked to watch various activities on the screen and hit letter Q whenever the target appeared on the screen. The sequence of events in this experiment was divided into three phases.
In phase 1, the target appeared at an interval of once in 60—90 s at 20 places in the scene in a random manner Figure 2A. This phase lasted for 15 min and was intended to trigger vigilance decrement.
Then phase 2 was activated, where surprising occurrence of challenge rain commenced as shown in Figure 2B. Rain is expected to make target detection more challenging and requires additional cognitive effort. Phase 2 can be divided into four intervals of 5 min each with the first 3 min with rain challenge and the last 2 min without rain.
The target appearance rate was maintained as in phase 1. The final phase of the experiment was similar to phase 1, a scene without challenge with target appearance at the rate of once in every 60—90 s for 10 min.
Figure 2. Simulated industrial plant or warehouse A without challenge rain and B with challenge rain showing the C target intruder [circled red in A,B ] and D distractors [circled blue in A,B ].
We collected data from four subjects who underwent the experiment twice—with and without challenging stimulus in phase 2. This was intended to compare the effect of challenge integration on the whole task. Two of the subjects randomly selected performed the experiment with challenge first while the other two performed the experiment without challenge first.
Saccade velocity of these subjects is analyzed and compared for the sessions with and without challenge. The results of the control study are detailed in Section 3. The eye tracking data comprised of samples with time stamp, pupil size, eye position and eye velocity recorded at a sampling rate of Hz.
The Eyelink system parses data samples into meaningful states saccades, fixation, and blinks. Velocity and acceleration based algorithms are used by the tracker to detect saccades, the movement of the eye from one point to another on the screen.
Fixations are necessary to grasp the visual content at a specific point in the scene while saccades are necessary to understand the content of the scene and to select task relevant regions to attend to. Eye blinks are also detected by the eye tracker. The raw data file in. edf format is converted to.
We used Matlab to extract various measures related to different types of eye movements between the required intervals. A moving average filter of 60 s was used to obtain an average of these measures across time. The measures are then normalized within each subject before averaging them across all the subjects.
In our experiment with monitoring task, artifacts related to eye movements are not avoidable and needed to be removed. We employed least mean squares LMS algorithm to remove the influence of eye movements from EEG based on reference to the two EOG channels: horizontal EOG and vertical EOG He et al.
The LMS method of artifact removal is automatic and stable on long duration EEG recordings as present in this study. However, for techniques with lower computational costs and more robust artifact removal, readers may also refer to Puthusserypady and Ratnarajah and Plöchl et al. Then, canonical correlation analysis CCA was utilized to remove muscular artifacts by projecting EEG onto a few maximally auto-correlated components De Clercq et al.
All channels were then referenced to the values averaged over all channels at each time point. Short time Fourier transform STFT was used to convert time series data into spectral power representation. Non-overlapping time window of length of 10 s was used for STFT.
Let P i, j be the spectral power at time point i and frequency j. Each band power can be calculated by. Where t 1 and t N are the starting and ending time points of the partitioned data, respectively.
f m 1 and f m 2 are boundary frequencies of the desired power band, respectively. Total power is obtained by summing up the five band powers of interest δ 0. In this section we present the results from the analysis of EEG and eye tracking data and compare the measures obtained from the data across different phases of the experiment.
We analyzed various eye tracking measures related to fixations, saccades and blinks to estimate the vigilance levels at different phases. The ANOVA analysis was performed across the last 10 min of phase 1, challenge period of phase 2 and phase 3 for the normalized average measures.
We wanted to compare the vigilance enhancement level during the challenge phase with vigilance decrement period in phase 1. Since vigilance decrement is not significant in the first 5 min of the experiment we excluded this period from the ANOVA analysis. Variation of these measures with time are shown in Figure 3.
The saccade amplitude and velocity are found to decrease with time whereas blink rate increases with time during monotonous target detection in phase 1. In phase 2, rising peaks are observed during the challenge stimulation in the case of saccade amplitude and saccade velocity while blink rate falls during challenge stimulation.
However in phase 3, these are maintained relatively constant. Figure 3. Variation of the normalized average of A saccade amplitude, B saccade velocity and C blink frequency with time across all the subjects. We analyzed EEG band power indices related to vigilance decrement that have been well accepted in the literature to estimate vigilance changes in our experiment.
The relative delta power is averaged across all subjects for the three phases. The relative power differences between phase 2 and other two phases are shown in Figure 4. Figure 4 clearly shows that relative delta power on the frontal extended to the parietal cortex and occipital cortices decreased for the challenging condition phase 2 compared to monotonous conditions phase 1 and phase 3.
The relative delta power during phase 2 and phase 1 on an illustrative channel, FC3, for each subject are listed in Figure 5. Figure 4. Relative delta power differences between phase 2 and phase 1 and phase 2 and phase 3.
Blue and red colors on difference topographies stand for the lower and higher powers in phase 2 respectively. Figure 5. Relative delta power comparisons between phase 1 and phase 2 for each subject on an illustrative channel, FC3. Majority of subjects 9 out of 12 present lower relative delta power at phase 2.
The dynamic of relative delta power for an illustrative channel, FC3 is shown in Figure 6. The changes in relative delta power with time clearly exhibit that challenging events modulate the power.
At the beginning, the power was relatively low and then it increased with time. When challenging events were presented, the power was suppressed and kept at a relatively low level for almost all the time during phase 2.
In the phase 3, the power rebounded to the same level as of the end of phase 1. Figure 6. The dynamic of relative delta power on an illustrative channel, FC3. Red, green, and blue colors correspond to the phases 1, 2, and 3 respectively.
Vertical gray bars indicate the periods with challenging events. To compare the effect of momentarily removing the challenge from the scene, we performed pairwise comparisons of the relative delta power between the four pairs of periods with and without challenge within phase 2. Phase 2 is divided into four 5 min periods where each 5 min period is comprised of 3 min with challenge and 2 min without challenge.
Figure 7 depicts the relative delta power in each portion on a illustrative channel, FC3, which exhibited significant difference in the comparison between phase 2 and phase 1. Figure 7 shows that 3 out of 4 pairwise comparisons between periods with challenge and without challenge are not statistically significant.
This suggest that there are no significant changes in the relative delta power even when the challenge is momentarily withdrawn. Also, portions with or without challenge do not exhibit any monotonic changes with time which shows that the effect of integrating challenge can be sustained during phase 2.
Figure 7. Relative delta power comparisons between portions with and without challenge on a typical channel, FC3 in the phase 2. Gray bars illustrate the relative delta power averaged within corresponding challenge periods, while white bars are for portions without challenge.
Figure 8 demonstrates the spatial changes in theta band power. Cortices with significant difference with respect to relative theta power are frontal and occipital regions. However, the significantly different occipital area is smaller and frontal area is larger in the case of the difference topography between phase 2 and phase 1 Phase 2— Phase 1 compared to the difference topography between phase 2 and phase 3 Phase 2— Phase 3.
In addition, there is an obvious augmentation in phase 2 compared to phase 3 on the temporal cortex. The time dynamic of average relative theta power along the frontal midline electrodes F3, F2, F4, F6, FC3, FC4, FC6 is plotted in Figure 9.
Frontal midline theta decreased during the initial monotonous phase and increased significantly during challenge integrated periods. Figure 8. Difference topographies of grand relative theta power averaged across subjects. The left column shows difference topographies between phase 2 and phase 1, while the right column is for between phase 2 and phase 3.
Figure 9. The dynamic of frontal midline theta averaged across all subjects. We also investigated the ratio of frontal theta power F3, F2, F4, F6, FC3, FC4, FC6 to parietal alpha power P3, P4, P5, P6, P7, P8.
Figure 10 demonstrates the time dynamic of the frontal theta to parietal alpha ratio averaged across all subjects. Figure The dynamic of frontal theta to parietal alpha ratio averaged across all subjects. The correlation analysis between EEG measures—relative delta power, frontal midline theta and frontal theta to parietal alpha ratio and the eye tracking measures—saccade velocity, saccade amplitude, and blink rate are shown in Figure We found that saccade amplitude and saccade velocity are negatively correlated with relative delta power whereas blink rate is positively correlated.
Conversely, saccade amplitude and saccade velocity are positively correlated with frontal midline theta and frontal theta to parietal alpha ratio whereas blink rate is negatively correlated. These results suggest that saccade measures saccade amplitude and saccade velocity of eye tracking and frontal midline theta and frontal theta to parietal alpha ratio of EEG correlate positively with vigilance level while blink rate from eye tracking and relative delta power from EEG correlate negatively with vigilance level.
The correlations are similar to the observations made by the studies described in the literature in Section 1. Correlation analysis between EEG relative delta power, frontal midline theta and frontal theta to parietal alpha ratio and eye tracking saccade velocity, saccade amplitude and blink rate measures.
The correlation coefficient r and significance value p are also shown in each case. All the correlations are statistically significant. The saccade velocity for the sessions with and without challenge integration was analyzed of all the four subjects who participated in the control study.
Variation of saccade velocity of all the four subjects is presented in Figure The saccade velocity of a subject during without challenge session is compared against saccade velocity during challenge session of that subject. The mean saccade velocity of the last 5 min of phase 1 is taken as the baseline in each session for each subject.
The baselines of both the sessions are also shown. These baselines indicate the level of vigilance before the beginning of phase 2. For 3 out of 4 control subjects controls 2, 3, and 4 , the saccade velocity is relatively higher for the challenge session than the session without challenge during phase 2.
However, for control 1 Figure 9A , the variation of saccade velocity appears to be similar for both the sessions, except that the saccade velocity peaks are comparatively higher for the challenge session than for the session without challenge. Variation of saccade velocity across time for all the subjects A—D of the control study.
Solid red line indicates saccade velocity during challenge session and solid blue line indicates saccade velocity during no challenge session. Dashed red line indicates baseline for challenge session and dashed blue line indicates baseline for no challenge session.
Gray bars indicate challenge stimulation periods. We observed that eye movements and rhythmic oscillations in brain activity are modulated by challenging stimuli. From the eye tracking data, we found that challenging stimuli cause increasing peaks in saccade measures saccade velocity and saccade amplitude and suppression in blink rate See Figure 3.
Di Stasi et al. Blink rate suppression in the case of interesting tasks is also observed in Yamada Hence, the reverse change in the variation of these measures due to challenging events suggest an increase in vigilance. Similarly, challenging stimuli caused suppression of relative delta power and augmentation in frontal midline theta and frontal theta to parietal alpha ratio in phase 2 showing an enhancement in the vigilance level.
Delta power as investigated by Chuang et al. Suppression of relative delta power due to challenge integration therefore suggests fatigue inhibition and increase in vigilance levels.
Frontal midline theta also increases due to challenge integration indicating vigilance enhancement Yamada, The increase in frontal theta to parietal alpha ratio suggests cortical arousal as studied by Smith and Gevins and Gevins et al. Significant correlations between the EEG and eye tracking measures confirms the evidence of vigilance enhancement.
The variation of saccade velocity of the subjects from control study are shown in Figure 12 for both challenge and no challenge sessions. Since saccade velocity is shown to decrease with vigilance decrement and vice versa, the results imply that the vigilance levels are higher for the sessions with challenge stimulation for 3 out of 4 subjects controls 2, 3, and 4 during phase 2.
Also, for the challenge session indicated in red , the saccade velocity in phase 2 peaks higher than the indicated baseline dashed red line which suggests that the challenge stimulation leads to an increase in the vigilance levels of the subjects. This is consistent with the observations made in Figure 3 with respect to saccade velocity.
However no such observations were made in the case of control 1. The reason for this behavior in control 1 may be due to subject to subject variation toward the challenge and needs further investigation.
With respect to eye tracking data, blink suppression and increase in saccade velocity and amplitude are observed. As discussed in Di Stasi et al. This main sequence is found to be affected by time-on-task. However, the modulation of this relation due to the challenge integration has to be studied yet.
In our task, the challenge integration results in a reverse change in delta power which suggests fatigue inhibition and improved attention. Increase in vigilance levels due to challenge integration suggests increased engagement of attentional resources due to increase in mental workload as supported by underload hypothesis of vigilance decrement.
Fatigue and vigilance decrement mechanisms are compared and debated by several studies. Fatigue is believed to be an accumulated effect over time and can be ameliorated through breaks or rest during the tasks. However, vigilance decrement can be a result of either lack of motivation or increase of fatigue or both Langner et al.
It has been found that fatigue is accompanied by an increase in delta oscillations Lal and Craig, ; Chuang et al. It could be that vigilance is a short-term state that can be altered by external stimulation while fatigue is a long-term state that can be temporarily altered but reverts back even though external stimulation can cause a transient adjustment in attention.
This may be the reason why some subjects do not show a clear enhancement effect during challenge stimulation. Therefore, it might be necessary to inhibit fatigue in addition to providing external stimulation to improve sustained attention for performance enhancement.
This aspect will be explored in detail in our future experiments where we plan to study the after effects of the challenge stimulation.
The effect of challenge stimulation to sustain performance and inhibit increasing fatigue after the withdrawal of challenge has to be investigated further.
The strategies of enhancing vigilance should also tend to increase the whole stimulus-response chain rather than simply enhancing perceptual sensibility of the stimulus. Hence in our future studies we plan to build a closed loop cognition enhancing strategies to take into account the enhancement of both stimulus perception and response to the stimulus.
In this study, we investigated the possibility of enhancing vigilance in a monotonous task using challenge integration. Challenge integration is achieved using noisy visual stimuli.
The results obtained from EEG relative delta power, frontal midline theta and frontal theta to parietal alpha ratio and eye tracking measures saccade velocity, saccade amplitude and blink rate , demonstrate with statistical significance, an increase in vigilance level due to challenge integration.
Furthermore, we found strong correlations between EEG and eye tracking measures used to measure vigilance. Saccade velocity, saccade amplitude, frontal midline theta and frontal theta to parietal alpha ratio correlate positively while blink rate and relative delta power correlate negatively with vigilance levels.
Therefore, challenge integration lead to increase in saccade velocity, saccade amplitude, frontal midline theta and frontal theta to parietal alpha ratio and suppression in blink rate and relative delta power.
This study should find its application in fields like military surveillance, health monitoring and industrial watch-keeping where the primary task can be integrated with challenging stimuli to reduce vigilance decrement and enhance task performance.
IB, HA, and NT have conceptualized the study and designed the experiments. IB performed the experiments. IB and JL performed the data analysis. All the authors made vital contributions in drafting the manuscript and have approved the final version.
The authors thank the National University of Singapore for supporting the Cognitive Engineering Group at the Singapore Institute for Neurotechnology SINAPSE under grant R This work was also partially supported by the Ministry of Education of Singapore under the grant MOET The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This research is supported by NGS IB and SINAPSE all authors , National University of Singapore. Anguera, J. Video game training enhances cognitive control in older adults. Nature , 97— doi: PubMed Abstract CrossRef Full Text Google Scholar.
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Thank you for visiting Vigilnace. You Customizable meal planner using a browser iVgilance Customizable meal planner limited support for CSS. Imprrove obtain the best experience, we Weight management techniques you use a more up Improve Vigilance Levels date Vihilance or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Current theoretical accounts on the oscillatory nature of sustained attention predict that entrainment via transcranial alternating current stimulation tACS at alpha and theta frequencies on specific areas of the prefrontal cortex could prevent the drops in vigilance across time-on-task. Nonetheless, most previous studies have neglected both the fact that vigilance comprises two dissociable components i.Video
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Vigilnce this light, several experimental studies that were designed to study vigilance decrement and enhancement during naturalistic tasks are examined. Key directions for future work in vigilance enhancement research are also proposed.
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: Improve Vigilance Levels| The present study | Doran, S. Smith, M. Brain Research Reviews, 35 2 , — Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. Therefore, the AV trials are suitable for measuring the AV component, as these trials represent the embedded RT subtask. Brain Res. When the subjects have not been deprived of sleep, noise degrades their performance since their activation levels exceed the optimum level. |
| Event Abstract | We observed a significant negative—albeit small—Pearson correlation between the linear slopes of IE score for executive control and hits in EV, which was also supported by Bayesian correlational analyses as strong evidence in favor of the existence of a correlation. We wanted to compare the vigilance enhancement level during the challenge phase with vigilance decrement period in phase 1. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. View author publications. Human cortical excitability increases with time awake. Figure 4. |
| Vigilance Enhancement using Challenge Integration in a Naturalistic Surveillance Task | Subjects performing vigilance tasks exhibit elevated levels of epinephrine and norepinephrine, consistent with high stress levels and indicative of a significant mental workload. Large individual differences in monitoring task performance have been reported in a number of vigilance studies. For a given task, however, the vigilance decrement between subjects is generally consistent over time, such that individuals exhibiting relatively higher levels of performance for a given task maintain that level of performance over time. An individual exhibiting no significant decrement while performing a counting monitoring task may exhibit a significant decrement during a clock test. Relative performance between subjects may also vary based on the nature of the task. Conversely, subjects performing similar monitoring tasks, such as radar versus sonar target detection, can be expected to exhibit similar patterns of task performance. Levine et al. propose that individual differences in task performance may be influenced by task demands. For example, some tasks may require rapid comparisons or "perceptual speed", while others may require "flexibility of closure", such as detection of some predefined object within a cluttered scene. Considerable research has been devoted to the reduction of the vigilance decrement. As noted above, the addition of non-target signals can improve task performance over time if the signals are similar to the target signals. Additionally, practice, performance feedback, amphetamines and rest are believed to moderate temporal performance decline without reducing sensitivity. Beginning in the mids research was conducted to determine whether amphetamines could reduce or counteract the vigilance decrement. Mackworth analyzed detection and false alarm rates to determine d', the measure of sensitivity. Participants dosed with amphetamine exhibited no increased sensitivity but did exhibit a highly significant reduction in vigilance decrement. In feedback trials, sensitivity increased while the performance decline was significantly reduced. In trials where both amphetamine and feedback were given, sensitivity was increased and there was no significant vigilance decrement. Training and practice significantly reduce the vigilance decrement, reduce the false alarm rate, and may improve sensitivity for many sustained attention tasks. Changes in strategy or bias may improve task performance. Improvements based on such a criterion shift would be expected to occur early in the training process. Training improvements may also occur due to the reduced mental workload associated with task automaticity. In pilotage and airport security screening experiments, trained or expert subjects exhibit better detection of low salience targets, a reduction in false alarms, improved sensitivity, and a significantly reduced vigilance decrement. In some cases the vigilance decrement was eliminated or not apparent. Vigilance research conducted with subjects across a range of ages conflict regarding the ability to maintain alertness and sustained attention with age. In , Parasuraman and Giambra reported a trend towards lower detection rates and higher false alarm rates with age when comparing groups between 19 and 27, 40 and 55, and 70 and 80 years old. Early theories of vigilance explained the reduction of electrophysiological activity over time associated with the vigilance decrement as a result of neural habituation. Under passive conditions, when no task is performed, participants exhibit attenuated N Event Related Potentials ERP that indicate neural habituation, and it was assumed that habituation was also responsible for the vigilance decrement. More recent ERP studies indicate that when performance declines during a vigilance task, N amplitude was not diminished. These results indicate that vigilance decrement is not the result of boredom or a reduction in neurological sensitivity. Contents move to sidebar hide. Article Talk. Read Edit View history. Tools Tools. What links here Related changes Upload file Special pages Permanent link Page information Cite this page Get shortened URL Download QR code Wikidata item. Download as PDF Printable version. A London "lollipop lady" with St. Paul's Cathedral in the background. Vigilance decrement [ edit ] Vigilance decrement is defined as "deterioration in the ability to remain vigilant for critical signals with time, as indicated by a decline in the rate of the correct detection of signals". Vigilance Taxonomy: discrimination type and event rate [ edit ] Mental workload, or cognitive load , based on task differences can significantly affect the degree of vigilance decrement. Individual differences in performance [ edit ] Large individual differences in monitoring task performance have been reported in a number of vigilance studies. Reducing the vigilance decrement with amphetamines [ edit ] Considerable research has been devoted to the reduction of the vigilance decrement. Practice and sustained attention [ edit ] Training and practice significantly reduce the vigilance decrement, reduce the false alarm rate, and may improve sensitivity for many sustained attention tasks. Lack of habituation [ edit ] Early theories of vigilance explained the reduction of electrophysiological activity over time associated with the vigilance decrement as a result of neural habituation. Vigilance requires hard mental work and is stressful. Human factors, 50 3 , Cognitive Psychology. Belmont: CA: Wadworth Cengage Learning. The breakdown of vigilance during prolonged visual search, Quarterly Journal of Experimental Psychology, 1, Vigilance, Monitoring and Search In J. Boff, L. Thomas Eds. Handbook of Human Perception and Performance, Vol. New York, Wiley. The detection of a simple visual signal as a function of time on watch. Human Factors 16, The abbreviated vigilance task and cerebral hemodynamics. Journal of Clinical and Experimental Neuropsychology, 29, Mitchell, D. Frontal midline theta from the perspective of hippocampal theta. Di Stasi, L. Saccadic velocity as an arousal index in naturalistic tasks. McIntire, L. Detection of vigilance performance using eye blinks. Yu, K. Cognitive workload modulation through degraded visual stimuli: a single-trial EEG study. Neural Eng. deBettencourt, M. Closed-loop training of attention with real-time brain imaging. Engelmann, J. Combined effects of attention and motivation on visual task performance: transient and sustained motivational effects. Keywords: Vigilance enhancement, cognitive enhancement, EEG power, eye tracking, visual search. Conference: SAN Meeting, Corfu, Greece, 6 Oct - 9 Oct, Presentation Type: Oral Presentation in SAN Conference. Topic: Oral Presentations. Vigilance Enhancement using Challenge Integration in a Naturalistic Surveillance Task. Conference Abstract: SAN Meeting. doi: Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4. Such automation requires less action but more monitoring on the part of the crew who must retain the ability to act when necessary if a situation arises. This type of monitoring activity requires a great deal of attention and vigilance on the part of the crew. Attention and vigilance are key components of situational awareness. As such, the accident data displayed in the situational awareness BN are representative of data related to attention and vigilance. Definitions of attention vary slightly depending on the field from which the definition is taken. All of the definitions, however, share the central theme of attention involving the concentration of thinking cognitive processes on a single object or thought to the exclusion of other stimuli or thoughts. In simpler terms, attention is the ability to focus and maintain interest in a given task or idea while avoiding distractions. Vigilance is a concept closely related to attention; in fact, the word attention is often used when defining vigilance. One definition of vigilance describes it as the process of paying close and continuous attention. It is often described as a quality or state of alertness or watchfulness. Vigilance can also be thought of as the extent of readiness to detect, or the likelihood of detecting, a stimulus that is imperative to safety. Several concepts related to attention and vigilance have been studied, and a well-informed crew should at least be familiar with the concepts:. Attention and vigilance are relatively fragile in nature and can be adversely affected by many factors. The primary factors that affect attention and vigilance and their consequences are presented below. One of the first experiments showing the effect of time on task on attention was conducted by Mackworth in Human operators were shown to have a limited ability to maintain vigilance over extended periods of time. Another important factor that may affect performance in a vigilance task is the rest to activity ratio. Figure 1. Performance reaction time in the detection of aircraft of air traffic controllers as a function of the rest-activity cycle and, time of the day and time on task. The effects of the frequency of critical and non-critical signals on vigilance have been tested and shown to be related to performance on a vigilance task. Figure 2 shows that a low number of critical signals significantly reduces performance expressed in reaction time during a vigilance task. Figure 3 demonstrates that the lower the number of non-critical signals per minute, the higher performance will be expressed in percentage of detection. That is to say, more non-critical signals per minute results in greater distraction and greater difficulty in identifying critical signals. These results demonstrate that level of performance is significantly influenced by signal-to-noise ratio. Figure 2. Effects of the number of critical signals on vigilance. Figure 3. Effects of the number of non-critical signals on performance at a vigilance task. The effects of noise on attention are complex. The following examples illustrate these effects:. Study results showed that noise level had no significant effect on performance in the one-clock condition. In the three-clock condition, however, performance decreased significantly at the higher noise level. This result supports the idea that higher levels of noise tend to decrease the ability to share attention when several tasks are being conducted simultaneously. Figure 4. Effects of noise exposure on performance in a clock test. Another study examined the effects of noise on the simultaneous performance of a tracking task and a detection task. For the tracking task, a higher percentage of time spent on target indicated better performance. For the detection task, performance was expressed as the percentage of signals detected in a variety of positions in the visual field. Results showed that a higher level of noise helped to maintain performance on the tracking task over time. For the detection task, a higher level of noise improved performance for signals located in the center of the field, but decreased performance when a signal was in the periphery. See Figure 5. Figure 5. Effects of noise exposure on performance in a double task test. The effects of heat on the performance of a vigilance task were studied as far back as the s in work by Mackworth and Pepler The studies indicated that performance degrades significantly when temperatures reach 30°C. |
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