An evaluation of an adaptive automation system using a cognitive vigilance task

Freeman, Frederick G.; Mikulka, Peter J.; Scerbo, Mark W.; Scott, Lorissa A.

doi:10.1016/j.biopsycho.2004.01.002

Cited by 66 publications

(54 citation statements)

References 16 publications

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“…Thus, Pope, Bogart and Bartoleme 7 developed an engagement index that computes the PSD of three bands: beta / (alpha + theta). This ratio was validated in other studies on engagement 16,17,18 . To characterize cognitive engagement over time, a fast Fourier transform (FFT) converts the EEG signal from each active site (F3, F4, O1, O2) into a power spectrum.…”

Section: Gathering Synchronized Data On Behavioral Emotional and Cosupporting

confidence: 63%

“…During these pauses, subjects had to relax and to close their eyes. As seen in other studies 4,9,16,17,18 , these pauses should induce significant variations in the collected signal: the two eye pupil dots in eye-tracking immediately disappear (behavioral engagement) and an immediate drop in cognitive engagement (EEG signal) is observed. These specific components of the signal are used to evaluate the general validity of the synchronization.…”

Section: Gathering Synchronized Data On Behavioral Emotional and Cosupporting

confidence: 59%

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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Charland

Léger

Sénécal

et al. 2015

JoVE

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In a recent theoretical synthesis on the concept of engagement, Fredricks, Blumenfeld and Paris 1 defined engagement by its multiple dimensions: behavioral, emotional and cognitive. They observed that individual types of engagement had not been studied in conjunction, and little information was available about interactions or synergy between the dimensions; consequently, more studies would contribute to creating finely tuned teaching interventions. Benefiting from the recent technological advances in neurosciences, this paper presents a recently developed methodology to gather and synchronize data on multidimensional engagement during learning tasks. The technique involves the collection of (a) electroencephalography, (b) electrodermal, (c) eye-tracking, and (d) facial emotion recognition data on four different computers. This led to synchronization issues for data collected from multiple sources. Post synchronization in specialized integration software gives researchers a better understanding of the dynamics between the multiple dimensions of engagement. For curriculum developers, these data could provide informed guidelines for achieving better instruction/learning efficiency. This technique also opens up possibilities in the field of brain-computer interactions, where adaptive learning or assessment environments could be developed. Video LinkThe video component of this article can be found at

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Section: Gathering Synchronized Data On Behavioral Emotional and Cosupporting

confidence: 63%

Section: Gathering Synchronized Data On Behavioral Emotional and Cosupporting

confidence: 59%

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Charland

Léger

Sénécal

et al. 2015

JoVE

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“…In [16], three self-induced emotional states were recognized with an accuracy of 68%. Other works tried to infer operator engagement, fatigue, and workload by using EEG signals in order to adapt the complexity of a task [17]- [21]. To our knowledge, however, this paper is the first to report on the use of EEG signals for emotion assessment in a gaming paradigm.…”

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confidence: 94%

“…This feature is known to be related to cognitive processes like workload, engagement, attention, and fatigue [20], which are cognitive states of interest in our paper. In many studies, the EEG_W feature is computed from only three to four electrodes [17], [18], [20]. However, there is high discrepancy among studies in the electrodes used.…”

Section: ) Eeg Featuresmentioning

confidence: 99%

“…In this paper, 19 electrodes were positioned on the skull of the participants according to the 10-20 system [26]. As demonstrated in other studies, EEGs can help in assessing emotional states and is also useful in providing an index of task engagement and workload [17]- [20]. Peripheral and EEG signals were recorded at a 256-Hz sampling rate using the Biosemi Active 2 acquisition system.…”

Section: A Acquisition Protocolmentioning

confidence: 99%

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Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

Chanel

Rebetez

Bétrancourt

et al. 2011

IEEE Trans. Syst., Man, Cybern. A

345

234

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Abstract-This paper proposes to maintain player's engagement by adapting game difficulty according to player's emotions assessed from physiological signals. The validity of this approach was first tested by analyzing the questionnaire responses, electroencephalogram (EEG) signals, and peripheral signals of the players playing a Tetris game at three difficulty levels. This analysis confirms that the different difficulty levels correspond to distinguishable emotions, and that, playing several times at the same difficulty level gives rise to boredom. The next step was to train several classifiers to automatically detect the three emotional classes from EEG and peripheral signals in a player-independent framework. By using either type of signals, the emotional classes were successfully recovered, with EEG having a better accuracy than peripheral signals on short periods of time. After the fusion of the two signal categories, the accuracy raised up to 63%.

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A laboratory model of canine search vigilance decrement, I

Aviles-Rosa

DeChant

Prada

et al. 2023

J Exper Analysis Behavior

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Previous studies have found that infrequent targets can reduce dogs' vigilance. The purpose of this study was to develop a laboratory model to evaluate the effects of infrequent targets on dogs' search behavior and performance. Dogs (n = 18) were trained to detect smokeless powder in an automated olfactometer in two distinct rooms (“operational” and “training”). During baseline, the dogs received five daily sessions at a high target odor frequency (90%) in both rooms. Subsequently, the frequency of the target odor was decreased to 10% only in the “operational” room but remained at 90% in the training room. Last, the odor prevalence was returned to 90% in both rooms. All dogs showed a significant decrement in detection performance in the operational room when the target odor frequency was decreased but simultaneusly mantained high performance in the training room. This decrement was largely due to decreases in adequate search behavior. All dogs recovered performance when the odor frequency was increased again to 90%. Trial accuracy was associated with tail position, search score, latency, and duration of environmentally directed behaviors. The data show that low target odor prevalence significantly reduced search behavior and performance and that there are behaviors that can be used by handlers to assess their dog's search state.

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An evaluation of an adaptive automation system using a cognitive vigilance task

Cited by 66 publications

References 16 publications

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Emotion Assessment From Physiological Signals for Adaptation of Game Difficulty

A laboratory model of canine search vigilance decrement, I

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