Pub Date : 2024-06-10DOI: 10.3389/fnrgo.2024.1397586
Kathryn A. Feltman, Johnathan F. Vogl, Aaron McAtee, Amanda M. Kelley
Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience.Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance.Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload.The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.
{"title":"Measuring aviator workload using EEG: an individualized approach to workload manipulation","authors":"Kathryn A. Feltman, Johnathan F. Vogl, Aaron McAtee, Amanda M. Kelley","doi":"10.3389/fnrgo.2024.1397586","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1397586","url":null,"abstract":"Measuring an operator's physiological state and using that data to predict future performance decrements has been an ongoing goal in many areas of transportation. Regarding Army aviation, the realization of such an endeavor could lead to the development of an adaptive automation system which adapts to the needs of the operator. However, reaching this end state requires the use of experimental scenarios similar to real-life settings in order to induce the state of interest that are able to account for individual differences in experience, exposure, and perception to workload manipulations. In the present study, we used an individualized approach to manipulating workload in order to account for individual differences in response to workload manipulations, while still providing an operationally relevant flight experience.Eight Army aviators participated in the study, where they completed two visits to the laboratory. The first visit served the purpose of identifying individual workload thresholds, with the second visit resulting in flights with individualized workload manipulations. EEG data was collected throughout both flights, along with subjective ratings of workload and flight performance.Both EEG data and workload ratings suggested a high workload. Subjective ratings were higher during the high workload flight compared to the low workload flight (p < 0.001). Regarding EEG, frontal alpha (p = 0.04) and theta (p = 0.01) values were lower and a ratio of beta/(alpha+theta) (p = 0.02) were higher in the baseline flight scenario compared to the high workload scenario. Furthermore, the data were compared to that collected in previous studies which used a group-based approach to manipulating workload.The individualized method demonstrated higher effect sizes in both EEG and subjective ratings, suggesting the use of this method may provide a more reliable way of producing high workload in aviators.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":" 506","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.3389/fnrgo.2024.1411305
L. Gehrke, Leonie Terfurth, Sezen Akman, Klaus Gramann
{"title":"Visuo-haptic prediction errors: a multimodal dataset (EEG, motion) in BIDS format indexing mismatches in haptic interaction","authors":"L. Gehrke, Leonie Terfurth, Sezen Akman, Klaus Gramann","doi":"10.3389/fnrgo.2024.1411305","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1411305","url":null,"abstract":"","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"11 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-05DOI: 10.3389/fnrgo.2024.1286586
Franziska Klein
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
{"title":"Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications","authors":"Franziska Klein","doi":"10.3389/fnrgo.2024.1286586","DOIUrl":"https://doi.org/10.3389/fnrgo.2024.1286586","url":null,"abstract":"The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"17 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141383775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-29DOI: 10.3389/fnrgo.2023.1266439
Sabrina von Au, Ingo Helmich, Simon Kieffer, H. Lausberg
Each individual touches the own body several 100 times a day. While some researchers propose a self-regulatory function of self-touch, others report that self-touching increases nervousness. This controversy appears to be caused by the fact that researchers did not define the kind of self-touch they examined and actually, referred to different types of self-touch. Thus, kinematically defining different types of self-touch, such as phasic (discrete), repetitive, and irregular, and exploring the neural correlates of the different types will provide insight into the neuropsychological function of self-touching behavior.To this aim, we assessed hemodynamic responses in prefrontal brain areas using functional near-infrared spectroscopy (fNIRS) and behavioral responses with NEUROGES®. Fifty-two participants were recorded during three specific kinematically types of self-touch (phasic, irregular, repetitive) that were to be performed on command. The recently developed toolbox Satori was used for the visualization of neuronal processes.Behaviorally, the participants did not perform irregular self-touch reliably. Neurally, the comparison of phasic, irregular and repetitive self-touch revealed different activation patterns. Repetitive self-touch is associated with stronger hemodynamic responses in the left Orbitofrontal Cortex and the Dorsolateral Prefrontal Cortex than phasic self-touch.These brain areas have been reported to be associated with self-regulatory processes. Furthermore, irregular self-touch appears to be primarily generated by implicit neural control. Thus, by distinguishing kinematically different types of self-touch, our findings shed light on the controverse discussion on the neuropsychological function of self-touch.
{"title":"Phasic and repetitive self-touch differ in hemodynamic response in the prefrontal cortex–An fNIRS study","authors":"Sabrina von Au, Ingo Helmich, Simon Kieffer, H. Lausberg","doi":"10.3389/fnrgo.2023.1266439","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1266439","url":null,"abstract":"Each individual touches the own body several 100 times a day. While some researchers propose a self-regulatory function of self-touch, others report that self-touching increases nervousness. This controversy appears to be caused by the fact that researchers did not define the kind of self-touch they examined and actually, referred to different types of self-touch. Thus, kinematically defining different types of self-touch, such as phasic (discrete), repetitive, and irregular, and exploring the neural correlates of the different types will provide insight into the neuropsychological function of self-touching behavior.To this aim, we assessed hemodynamic responses in prefrontal brain areas using functional near-infrared spectroscopy (fNIRS) and behavioral responses with NEUROGES®. Fifty-two participants were recorded during three specific kinematically types of self-touch (phasic, irregular, repetitive) that were to be performed on command. The recently developed toolbox Satori was used for the visualization of neuronal processes.Behaviorally, the participants did not perform irregular self-touch reliably. Neurally, the comparison of phasic, irregular and repetitive self-touch revealed different activation patterns. Repetitive self-touch is associated with stronger hemodynamic responses in the left Orbitofrontal Cortex and the Dorsolateral Prefrontal Cortex than phasic self-touch.These brain areas have been reported to be associated with self-regulatory processes. Furthermore, irregular self-touch appears to be primarily generated by implicit neural control. Thus, by distinguishing kinematically different types of self-touch, our findings shed light on the controverse discussion on the neuropsychological function of self-touch.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139210923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-27DOI: 10.3389/fnrgo.2023.1136362
Jihyun Cha, Hyung-Su Kim, Gusang Kwon, Si-Young Cho, Jae-Myoung Kim
The benefits of long-term consumption of green tea on the brain are well known. However, among many ingredients of green tea, the acute effects of (–)-gallocatechin gallate-rich green tea extract (GCG-GTE), have received comparatively less attention. Herein, we investigated the acute effects of oral ingestion of green tea with GCG-GTE, which contains close replicas of the ingredients of hot green tea, on task-dependent hemodynamics in the prefrontal cortex of healthy adult human brains.In this randomized, double-blind, placebo-controlled, parallel group trial, 35 healthy adults completed computerized cognitive tasks that demand activation of the prefrontal cortex at baseline and 1 h after consumption of placebo and 900 mg of GCG-GTE extract supplement. During cognitive testing, hemodynamic responses (change in HbO2 concentration) in the prefrontal cortex were assessed using functional near-infrared spectroscopy (fNIRS).In fNIRS data, significant group x session interactions were found in the left (p = 0.035) and right (p = 0.036) dorsolateral prefrontal cortex (DLPFC). In behavioral data, despite the numerical increase in the GCG-GTE group and the numerical decrease in the Placebo group, no significant differences were observed in the cognitive performance measure between the groups.The result suggests a single dose of orally administered GCG-GTE can reduce DLPFC activation in healthy humans even with increased task demand. GCG-GTE is a promising functional material that can affect neural efficiency to lower mental workload during cognitively demanding tasks. However, further studies are needed to verify this.
{"title":"Acute effects of (–)-gallocatechin gallate-rich green tea extract on the cerebral hemodynamic response of the prefrontal cortex in healthy humans","authors":"Jihyun Cha, Hyung-Su Kim, Gusang Kwon, Si-Young Cho, Jae-Myoung Kim","doi":"10.3389/fnrgo.2023.1136362","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1136362","url":null,"abstract":"The benefits of long-term consumption of green tea on the brain are well known. However, among many ingredients of green tea, the acute effects of (–)-gallocatechin gallate-rich green tea extract (GCG-GTE), have received comparatively less attention. Herein, we investigated the acute effects of oral ingestion of green tea with GCG-GTE, which contains close replicas of the ingredients of hot green tea, on task-dependent hemodynamics in the prefrontal cortex of healthy adult human brains.In this randomized, double-blind, placebo-controlled, parallel group trial, 35 healthy adults completed computerized cognitive tasks that demand activation of the prefrontal cortex at baseline and 1 h after consumption of placebo and 900 mg of GCG-GTE extract supplement. During cognitive testing, hemodynamic responses (change in HbO2 concentration) in the prefrontal cortex were assessed using functional near-infrared spectroscopy (fNIRS).In fNIRS data, significant group x session interactions were found in the left (p = 0.035) and right (p = 0.036) dorsolateral prefrontal cortex (DLPFC). In behavioral data, despite the numerical increase in the GCG-GTE group and the numerical decrease in the Placebo group, no significant differences were observed in the cognitive performance measure between the groups.The result suggests a single dose of orally administered GCG-GTE can reduce DLPFC activation in healthy humans even with increased task demand. GCG-GTE is a promising functional material that can affect neural efficiency to lower mental workload during cognitively demanding tasks. However, further studies are needed to verify this.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139233319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-23DOI: 10.3389/fnrgo.2023.1274730
M. Vukelić, Michael Bui, Anna Vorreuther, Katharina Lingelbach
Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.
{"title":"Combining brain-computer interfaces with deep reinforcement learning for robot training: a feasibility study in a simulation environment","authors":"M. Vukelić, Michael Bui, Anna Vorreuther, Katharina Lingelbach","doi":"10.3389/fnrgo.2023.1274730","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1274730","url":null,"abstract":"Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"44 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139242812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-23DOI: 10.3389/fnrgo.2023.1233722
Guillermo I. Gallegos Ayala, David Haslacher, L. R. Krol, S. Soekadar, T. Zander
Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.
{"title":"Assessment of mental workload across cognitive tasks using a passive brain-computer interface based on mean negative theta-band amplitudes","authors":"Guillermo I. Gallegos Ayala, David Haslacher, L. R. Krol, S. Soekadar, T. Zander","doi":"10.3389/fnrgo.2023.1233722","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1233722","url":null,"abstract":"Brain-computer interfaces (BCI) can provide real-time and continuous assessments of mental workload in different scenarios, which can subsequently be used to optimize human-computer interaction. However, assessment of mental workload is complicated by the task-dependent nature of the underlying neural signals. Thus, classifiers trained on data from one task do not generalize well to other tasks. Previous attempts at classifying mental workload across different cognitive tasks have therefore only been partially successful. Here we introduce a novel algorithm to extract frontal theta oscillations from electroencephalographic (EEG) recordings of brain activity and show that it can be used to detect mental workload across different cognitive tasks. We use a published data set that investigated subject dependent task transfer, based on Filter Bank Common Spatial Patterns. After testing, our approach enables a binary classification of mental workload with performances of 92.00 and 92.35%, respectively for either low or high workload vs. an initial no workload condition, with significantly better results than those of the previous approach. It, nevertheless, does not perform beyond chance level when comparing high vs. low workload conditions. Also, when an independent component analysis was done first with the data (and before any additional preprocessing procedure), even though we achieved more stable classification results above chance level across all tasks, it did not perform better than the previous approach. These mixed results illustrate that while the proposed algorithm cannot replace previous general-purpose classification methods, it may outperform state-of-the-art algorithms in specific (workload) comparisons.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139243741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.3389/fnrgo.2023.1215497
Oliver A Guidetti, C. Speelman, Peter Bouhlas
Vigilance decrement refers to a psychophysiological decline in the capacity to sustain attention to monotonous tasks after prolonged periods. A plethora of experimental tasks exist for researchers to study vigilance decrement in classic domains such as driving and air traffic control and baggage security; however, the only cyber vigilance tasks reported in the research literature exist in the possession of the United States Air Force (USAF). Moreover, existent cyber vigilance tasks have not kept up with advances in real-world cyber security and consequently no longer accurately reflect the cognitive load associated with modern network defense. The Western Australian Cyber Defense Task (WACDT) was designed, engineered, and validated. Elements of network defense command-and-control consoles that influence the trajectory of vigilance can be adjusted within the WACDT. These elements included cognitive load, event rate, signal salience and workload transitions. Two forms of the WACDT were tested. In static trials, each element was adjusted to its maximum level of processing difficulty. In dynamic trials, these elements were set to increase from their minimum to their maximum values. Vigilance performance in static trials was shown to improve over time. In contrast, dynamic WACDT trials were characterized by vigilance performance declines. The WACDT provides the civilian human factors research community with an up-to-date and validated vigilance task for network defense accessible to civilian researchers.
{"title":"The WACDT, a modern vigilance task for network defense","authors":"Oliver A Guidetti, C. Speelman, Peter Bouhlas","doi":"10.3389/fnrgo.2023.1215497","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1215497","url":null,"abstract":"Vigilance decrement refers to a psychophysiological decline in the capacity to sustain attention to monotonous tasks after prolonged periods. A plethora of experimental tasks exist for researchers to study vigilance decrement in classic domains such as driving and air traffic control and baggage security; however, the only cyber vigilance tasks reported in the research literature exist in the possession of the United States Air Force (USAF). Moreover, existent cyber vigilance tasks have not kept up with advances in real-world cyber security and consequently no longer accurately reflect the cognitive load associated with modern network defense. The Western Australian Cyber Defense Task (WACDT) was designed, engineered, and validated. Elements of network defense command-and-control consoles that influence the trajectory of vigilance can be adjusted within the WACDT. These elements included cognitive load, event rate, signal salience and workload transitions. Two forms of the WACDT were tested. In static trials, each element was adjusted to its maximum level of processing difficulty. In dynamic trials, these elements were set to increase from their minimum to their maximum values. Vigilance performance in static trials was shown to improve over time. In contrast, dynamic WACDT trials were characterized by vigilance performance declines. The WACDT provides the civilian human factors research community with an up-to-date and validated vigilance task for network defense accessible to civilian researchers.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.3389/fnrgo.2023.1201777
Joseph B. Lyons, Kerianne Hobbs, Steve Rogers, Scott H. Clouse
Although there is a rich history of philosophical definitions of ethics when applied to human behavior, applying the same concepts and principles to AI may be fraught with problems. Anthropomorphizing AI to have characteristics such as “ethics” may promote a dangerous, unrealistic expectation that AI can be trained to have inherent, guaranteed ethical behavior. The authors instead advocate for increased research into the ethical use of AI from initial ideation and design through operational use and sustainment. The authors advocate for five key research areas: (1) education in ethics and core AI concepts for AI developers, leaders, and users, (2) development and use of model cards or datasheets for datasets to provide transparency into the strengths, limits, and potential biases of a trained model, (3) employing human-centered design that seeks to understand human value structures within a task context and enable effective human-machine interaction through intuitive and transparent interfaces, (4) targeted use of run time assurance that monitors and modifies the inputs or outputs of a trained model when necessary to enforce ethical principles such as safety or limiting bias, and (5) developing best practices for the use of a joint human-AI co-creation and training experience to enable a shared mental model and higher performance through potential emergent behavior.
{"title":"Responsible (use of) AI","authors":"Joseph B. Lyons, Kerianne Hobbs, Steve Rogers, Scott H. Clouse","doi":"10.3389/fnrgo.2023.1201777","DOIUrl":"https://doi.org/10.3389/fnrgo.2023.1201777","url":null,"abstract":"Although there is a rich history of philosophical definitions of ethics when applied to human behavior, applying the same concepts and principles to AI may be fraught with problems. Anthropomorphizing AI to have characteristics such as “ethics” may promote a dangerous, unrealistic expectation that AI can be trained to have inherent, guaranteed ethical behavior. The authors instead advocate for increased research into the ethical use of AI from initial ideation and design through operational use and sustainment. The authors advocate for five key research areas: (1) education in ethics and core AI concepts for AI developers, leaders, and users, (2) development and use of model cards or datasheets for datasets to provide transparency into the strengths, limits, and potential biases of a trained model, (3) employing human-centered design that seeks to understand human value structures within a task context and enable effective human-machine interaction through intuitive and transparent interfaces, (4) targeted use of run time assurance that monitors and modifies the inputs or outputs of a trained model when necessary to enforce ethical principles such as safety or limiting bias, and (5) developing best practices for the use of a joint human-AI co-creation and training experience to enable a shared mental model and higher performance through potential emergent behavior.","PeriodicalId":507972,"journal":{"name":"Frontiers in Neuroergonomics","volume":"40 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139255140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-15DOI: 10.3389/fnrgo.2023.1273810
Katharina Lingelbach, Sabrina Gado, Maria Wirzberger, M. Vukelić
We investigated brain activation patterns of interacting emotional distractions and cognitive processes in a close-to-naturalistic functional near-infrared spectroscopy (fNIRS) study.Eighteen participants engaged in a monitoring-control task, mimicking common air traffic controller requirements. The scenario entailed experiencing both low and high workload, while concurrently being exposed to emotional speech distractions of positive, negative, and neutral valence.Our investigation identified hemispheric asymmetries in prefrontal cortex (PFC) activity during the presentation of negative and positive emotional speech distractions at different workload levels. Thereby, in particular, activation in the left inferior frontal gyrus (IFG) and orbitofrontal cortex (OFC) seems to play a crucial role. Brain activation patterns revealed a cross-over interaction indicating workload-dependent left hemispheric inhibition processes during negative distractions and high workload. For positive emotional distractions under low workload, we observed left-hemispheric PFC recruitment potentially associated with speech-related processes. Furthermore, we found a workload-independent negativity bias for neutral distractions, showing brain activation patterns similar to those of negative distractions.In conclusion, lateralized hemispheric processing, regulating emotional speech distractions and integrating emotional and cognitive processes, is influenced by workload levels and stimulus characteristics. These findings advance our understanding of the factors modulating hemispheric asymmetries during the processing and inhibition of emotional distractions, as well as the interplay between emotion and cognition. Moreover, they emphasize the significance of exploring emotion-cognition interactions in more naturalistic settings to gain a deeper understanding of their implications in real-world application scenarios (e.g., working and learning environments).
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