Pub Date : 2023-10-25DOI: 10.1177/21695067231192593
Miao Song, Jackie Ayoub, Danyang Tian, Miguel Perez, Julie McClafferty, Ehsan Moradi Pari
Drivers need to constantly communicate their intention while sharing the road with other road users to attract attention, reduce confusion, and avoid collisions. With current advancements in the transportation system, particularly the increasing penetration of various levels of automation, the need to communicate intentions has become even more demanding and complex. Thus, it is critical to investigate the limitations and consequences of the existing communication channels and examine the need for improved communication. This study focused on two representative event types: lane change/merge and stop sign-controlled intersection in the SHRP 2 NDS dataset. Communication was deemed essential to the successful navigation of these maneuvers. Through exploratory analysis of driving behavior, insights were gained into the prevalence of lack of communication (LOC) among target events. Identified LOC events were further classified based on the scenario type. Moreover, descriptive observations of the interaction between drivers in these situations were developed and categorized.
{"title":"May I, please?: Examining the Need for Improved Intention Communication on the Road Using Naturalistic Data","authors":"Miao Song, Jackie Ayoub, Danyang Tian, Miguel Perez, Julie McClafferty, Ehsan Moradi Pari","doi":"10.1177/21695067231192593","DOIUrl":"https://doi.org/10.1177/21695067231192593","url":null,"abstract":"Drivers need to constantly communicate their intention while sharing the road with other road users to attract attention, reduce confusion, and avoid collisions. With current advancements in the transportation system, particularly the increasing penetration of various levels of automation, the need to communicate intentions has become even more demanding and complex. Thus, it is critical to investigate the limitations and consequences of the existing communication channels and examine the need for improved communication. This study focused on two representative event types: lane change/merge and stop sign-controlled intersection in the SHRP 2 NDS dataset. Communication was deemed essential to the successful navigation of these maneuvers. Through exploratory analysis of driving behavior, insights were gained into the prevalence of lack of communication (LOC) among target events. Identified LOC events were further classified based on the scenario type. Moreover, descriptive observations of the interaction between drivers in these situations were developed and categorized.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"27 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135218406","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 : 2023-10-25DOI: 10.1177/21695067231192612
Manhua Wang, Ravi Parikh, Myounghoon Jeon
Ensuring a safe transition between the automation system and human operators is critical in conditionally automated vehicles. During the automation-to-human transition process, hazard avoidance plays an important role after human drivers regain the vehicle control. This study applies the multilevel Hidden Markov Model to understand the hazard avoidance processes in response to static road hazards as continuous processes. The three-state model—Approaching, Negotiating, and Recovering—had the best model fitness, compared to the four-state and five-state models. The trained model reaches an average of 66% accuracy rate on predicting hazard avoidance states on the testing data. The prediction performance reveals the possibility to use the hazard avoidance pattern to recognize driving behaviors. We further propose several improvements at the end to generalize our models into other scenarios, including the potential to model hazard avoidance as a basic driving skill across different levels of automation conditions.
{"title":"Using Multilevel Hidden Markov Models to Understand Driver Hazard Avoidance during the Takeover Process in Conditionally Automated Vehicles","authors":"Manhua Wang, Ravi Parikh, Myounghoon Jeon","doi":"10.1177/21695067231192612","DOIUrl":"https://doi.org/10.1177/21695067231192612","url":null,"abstract":"Ensuring a safe transition between the automation system and human operators is critical in conditionally automated vehicles. During the automation-to-human transition process, hazard avoidance plays an important role after human drivers regain the vehicle control. This study applies the multilevel Hidden Markov Model to understand the hazard avoidance processes in response to static road hazards as continuous processes. The three-state model—Approaching, Negotiating, and Recovering—had the best model fitness, compared to the four-state and five-state models. The trained model reaches an average of 66% accuracy rate on predicting hazard avoidance states on the testing data. The prediction performance reveals the possibility to use the hazard avoidance pattern to recognize driving behaviors. We further propose several improvements at the end to generalize our models into other scenarios, including the potential to model hazard avoidance as a basic driving skill across different levels of automation conditions.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"32 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170428","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 : 2023-10-25DOI: 10.1177/21695067231192406
Yaohan Ding, Lesong Jia, Na Du
Trust and situational awareness (SA) are crucial to the adoption and safety of automated vehicles (AVs). Appropriate design of AV explanations could promote drivers’ acceptance, trust, and SA, enabling drivers to get more benefits from the technology. This study investigated the effects of error type and information type of AV explanations on drivers’ trust and SA. We recruited 300 participants for an online video study with a 3 (information type) × 2 (error type) mixed design. Linear mixed model analyses showed that compared with false alarms, misses were associated with more trust decrease after the error and more trust decrease after the post-error recovery. Compared with why information, how information was associated with lower SA generally and risked potential over-trust in false alarms. Therefore, we recommend deploying AV decision systems that are less miss-prone and including why information in AV explanations.
{"title":"Designing for Trust and Situational Awareness in Automated Vehicles: Effects of Information Type and Error Type","authors":"Yaohan Ding, Lesong Jia, Na Du","doi":"10.1177/21695067231192406","DOIUrl":"https://doi.org/10.1177/21695067231192406","url":null,"abstract":"Trust and situational awareness (SA) are crucial to the adoption and safety of automated vehicles (AVs). Appropriate design of AV explanations could promote drivers’ acceptance, trust, and SA, enabling drivers to get more benefits from the technology. This study investigated the effects of error type and information type of AV explanations on drivers’ trust and SA. We recruited 300 participants for an online video study with a 3 (information type) × 2 (error type) mixed design. Linear mixed model analyses showed that compared with false alarms, misses were associated with more trust decrease after the error and more trust decrease after the post-error recovery. Compared with why information, how information was associated with lower SA generally and risked potential over-trust in false alarms. Therefore, we recommend deploying AV decision systems that are less miss-prone and including why information in AV explanations.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"2676 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135170615","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 : 2023-10-25DOI: 10.1177/21695067231192648
Abbie Hutton, Bill Bui, Valerie Hubener
The Holographic Augmented Reality Visualization Interface for Exploration (HARVIE) was developed for the 2022 NASA SUITS (Spacesuit User Interface for Students) challenge. HARVIE assists astronauts with elevated demands of the lunar surface through navigation, terrain sensing, and an optimal display of suit status elements (e.g., oxygen, battery, and heart rate). Considering environmental constraints, the system architecture promotes efficient cross modal communication between the mission control center, other astronauts, and the user interface. Currently, the system utilizes a hands-free modality such as speech recognition. Throughout the design process, we conducted heuristic evaluations on a low-fidelity prototype. Then, we implemented HARVIE into a high-fidelity prototype on the HoloLens 2 and utilized the Rapid Iterative Testing & Evaluation (RITE) method for human-in-the-loop testing. Lastly, we evaluated our final design at NASA Johnson Space Center. Our interface serves as a novel approach to enhance how astronauts navigate on missions using augmented reality.
{"title":"Holographic Augmented Reality Visualization Interface for Exploration (HARVIE)","authors":"Abbie Hutton, Bill Bui, Valerie Hubener","doi":"10.1177/21695067231192648","DOIUrl":"https://doi.org/10.1177/21695067231192648","url":null,"abstract":"The Holographic Augmented Reality Visualization Interface for Exploration (HARVIE) was developed for the 2022 NASA SUITS (Spacesuit User Interface for Students) challenge. HARVIE assists astronauts with elevated demands of the lunar surface through navigation, terrain sensing, and an optimal display of suit status elements (e.g., oxygen, battery, and heart rate). Considering environmental constraints, the system architecture promotes efficient cross modal communication between the mission control center, other astronauts, and the user interface. Currently, the system utilizes a hands-free modality such as speech recognition. Throughout the design process, we conducted heuristic evaluations on a low-fidelity prototype. Then, we implemented HARVIE into a high-fidelity prototype on the HoloLens 2 and utilized the Rapid Iterative Testing & Evaluation (RITE) method for human-in-the-loop testing. Lastly, we evaluated our final design at NASA Johnson Space Center. Our interface serves as a novel approach to enhance how astronauts navigate on missions using augmented reality.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"15 11","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112773","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 : 2023-10-25DOI: 10.1177/21695067231194985
Jiaxin Li, Robyn Soh, Ji-Eun Kim
Stress is a common concern in modern workplaces. However, traditional stress measurements such as selfreported questionnaires have limited application in real-world settings. In this exploratory study, we collected physiological signals via a wristband and an eye tracker from five participants while they were executing a stress-inducing task. Our mixed-effect model revealed that several physiological responses, including electrodermal activity, skin temperature, and average pupil diameter, can be used as indicators of perceived stress levels. Our findings suggest the potential of using physiological sensors to monitor individuals’ perceived stress in real-world scenarios and thus facilitate workplace stress management and intervention.
{"title":"Sensor-based Stress Level Monitoring: An Exploratory Study","authors":"Jiaxin Li, Robyn Soh, Ji-Eun Kim","doi":"10.1177/21695067231194985","DOIUrl":"https://doi.org/10.1177/21695067231194985","url":null,"abstract":"Stress is a common concern in modern workplaces. However, traditional stress measurements such as selfreported questionnaires have limited application in real-world settings. In this exploratory study, we collected physiological signals via a wristband and an eye tracker from five participants while they were executing a stress-inducing task. Our mixed-effect model revealed that several physiological responses, including electrodermal activity, skin temperature, and average pupil diameter, can be used as indicators of perceived stress levels. Our findings suggest the potential of using physiological sensors to monitor individuals’ perceived stress in real-world scenarios and thus facilitate workplace stress management and intervention.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112840","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 : 2023-10-25DOI: 10.1177/21695067231192294
Mengyao Li, Sofia I. Noejovich, Ernest V. Cross, John D. Lee
When people experience the same automation, their trust in automation can diverge. Prior research has used individual differences—trust propensity and complacency—to explain this divergence. We argue that bifurcation as an outcome of a dynamic system better explains trust divergence. Linear mixed-effect models were used to identify features to predict trust (i.e., individual differences, automation reliability, and exposure). Individual differences associated with trust propensity and complacency increases the R 2 of the baseline model by 0.01, from R 2 = 0.40 to 0.41. Furthermore, the Best Linear Unbiased Predictors (BLUPS) for random effect of participants were uncorrelated with trust propensity and complacency. In contrast, modeling trust divergence from a dynamic perspective, which considers the interaction between reliability and exposure along with the individual by-reliability variability fit the data well ( R 2 = 0.84). These results suggest dynamic interaction with automation produce trust divergence and design should focus on state dependence and responsivity.
{"title":"Explaining Trust Divergence: Bifurcations in a Dynamic System","authors":"Mengyao Li, Sofia I. Noejovich, Ernest V. Cross, John D. Lee","doi":"10.1177/21695067231192294","DOIUrl":"https://doi.org/10.1177/21695067231192294","url":null,"abstract":"When people experience the same automation, their trust in automation can diverge. Prior research has used individual differences—trust propensity and complacency—to explain this divergence. We argue that bifurcation as an outcome of a dynamic system better explains trust divergence. Linear mixed-effect models were used to identify features to predict trust (i.e., individual differences, automation reliability, and exposure). Individual differences associated with trust propensity and complacency increases the R 2 of the baseline model by 0.01, from R 2 = 0.40 to 0.41. Furthermore, the Best Linear Unbiased Predictors (BLUPS) for random effect of participants were uncorrelated with trust propensity and complacency. In contrast, modeling trust divergence from a dynamic perspective, which considers the interaction between reliability and exposure along with the individual by-reliability variability fit the data well ( R 2 = 0.84). These results suggest dynamic interaction with automation produce trust divergence and design should focus on state dependence and responsivity.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"IA-21 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112937","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 : 2023-10-25DOI: 10.1177/21695067231192929
Haroula M. Tzamaras, Hang-Ling Wu, Jason Z. Moore, Scarlett R. Miller
Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factors in healthcare. While wearable mobile eye trackers have become more readily available, there are no existing analysis methods for accurately and efficiently mapping dynamic gaze data on dynamic areas of interest (AOIs), which limits their utility in human factors research. The purpose of this paper was to outline a proposed framework for automating the analysis of dynamic areas of interest by integrating computer vision and machine learning (CVML). The framework is then tested using a use-case of a Central Venous Catheterization trainer with six dynamic AOIs. While the results of the validity trial indicate there is room for improvement in the CVML method proposed, the framework provides direction and guidance for human factors researchers using dynamic AOIs.
{"title":"Shifting Perspectives: A proposed framework for analyzing head-mounted eye-tracking data with dynamic areas of interest and dynamic scenes","authors":"Haroula M. Tzamaras, Hang-Ling Wu, Jason Z. Moore, Scarlett R. Miller","doi":"10.1177/21695067231192929","DOIUrl":"https://doi.org/10.1177/21695067231192929","url":null,"abstract":"Eye-tracking is a valuable research method for understanding human cognition and is readily employed in human factors research, including human factors in healthcare. While wearable mobile eye trackers have become more readily available, there are no existing analysis methods for accurately and efficiently mapping dynamic gaze data on dynamic areas of interest (AOIs), which limits their utility in human factors research. The purpose of this paper was to outline a proposed framework for automating the analysis of dynamic areas of interest by integrating computer vision and machine learning (CVML). The framework is then tested using a use-case of a Central Venous Catheterization trainer with six dynamic AOIs. While the results of the validity trial indicate there is room for improvement in the CVML method proposed, the framework provides direction and guidance for human factors researchers using dynamic AOIs.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112938","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 : 2023-10-25DOI: 10.1177/21695067231193678
Gabriela Flores-Cruz, Sean D. Hinkle, Nelson A. Roque, Mustapha Mouloua
The purpose of this study was to investigate participants’ perceived attitudes and usability with OpenAI’s ChatGPT AI chatbot. Participants were asked to watch screen recorded videos of a researcher exploring the AI’s ability to create a quantum mechanics experiment and to plan a trip to New York City. Thirty percent of participants had previously used the AI before the study. Attitudes towards the AI were in the middle of the scale, and prior use did not affect these attitudes. Additionally, ratings on usability were higher for planning a trip compared to creating an experiment, but no differences were found depending on prior use. Future research should examine attitudes and usability when participants interact with the AI chatbot directly in different scenarios. The study also emphasizes the need to examine the potential effects of AI on user experience, and safety, given the prevalence of ChatGPT in the general population.
{"title":"ChatGPT as the Ultimate Travel Buddy or Research Assistant: A Study on Perceived Attitudes and Usability","authors":"Gabriela Flores-Cruz, Sean D. Hinkle, Nelson A. Roque, Mustapha Mouloua","doi":"10.1177/21695067231193678","DOIUrl":"https://doi.org/10.1177/21695067231193678","url":null,"abstract":"The purpose of this study was to investigate participants’ perceived attitudes and usability with OpenAI’s ChatGPT AI chatbot. Participants were asked to watch screen recorded videos of a researcher exploring the AI’s ability to create a quantum mechanics experiment and to plan a trip to New York City. Thirty percent of participants had previously used the AI before the study. Attitudes towards the AI were in the middle of the scale, and prior use did not affect these attitudes. Additionally, ratings on usability were higher for planning a trip compared to creating an experiment, but no differences were found depending on prior use. Future research should examine attitudes and usability when participants interact with the AI chatbot directly in different scenarios. The study also emphasizes the need to examine the potential effects of AI on user experience, and safety, given the prevalence of ChatGPT in the general population.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113298","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 : 2023-10-25DOI: 10.1177/21695067231195827
Joonbum Lee, Hansol Rheem, John D. Lee, Joseph F. Szczerba, Akilesh Rajavenkatanarayanan, Roy Mathieu
Driver assistance technologies have rapidly advanced. However, using partially automated driving systems in urban environments is still challenging. The potential disuse of driving automation is one of the challenges that prevents users from taking full advantage of the system. To address this issue, we investigated whether sharing the vehicle’s situation awareness (SA) information could increase the proper use of driving automation in urban contexts. An Augmented Reality Head-Up Display (AR HUD) was developed to present the vehicle’s SA information, and its effect was tested using a driving simulator. We used a two-part mixed model to analyze driver reliance behavior. The results showed that sharing the vehicle’s SA information decreased override responses when the automation could handle the situation but had no significant effect on the override time. These findings suggest that providing drivers with the vehicle SA information can increase the appropriate use of driving automation in complex urban driving situations.
{"title":"Sharing Vehicle Situation Awareness Reduces Driver-Initiated Overrides in Urban Environments","authors":"Joonbum Lee, Hansol Rheem, John D. Lee, Joseph F. Szczerba, Akilesh Rajavenkatanarayanan, Roy Mathieu","doi":"10.1177/21695067231195827","DOIUrl":"https://doi.org/10.1177/21695067231195827","url":null,"abstract":"Driver assistance technologies have rapidly advanced. However, using partially automated driving systems in urban environments is still challenging. The potential disuse of driving automation is one of the challenges that prevents users from taking full advantage of the system. To address this issue, we investigated whether sharing the vehicle’s situation awareness (SA) information could increase the proper use of driving automation in urban contexts. An Augmented Reality Head-Up Display (AR HUD) was developed to present the vehicle’s SA information, and its effect was tested using a driving simulator. We used a two-part mixed model to analyze driver reliance behavior. The results showed that sharing the vehicle’s SA information decreased override responses when the automation could handle the situation but had no significant effect on the override time. These findings suggest that providing drivers with the vehicle SA information can increase the appropriate use of driving automation in complex urban driving situations.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"23 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113725","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 : 2023-10-25DOI: 10.1177/21695067231192635
Margaret A. Gray, Zhuorui Yong, Abhijan Wasti, Esa M. Rantanen, Jamison R. Heard
This research investigated human performance in response to task demands that may be used to convey information about the human to an artificial agent. We performed an experiment with a dynamic time-sharing task to investigate participants development of temporal awareness of the task event unfolding in time. Temporal awareness as an extension, or a special case, of situation awareness, may provide for useful measures of covert mental models applicable to numerous tasks and for input to human-aware AI agents. Temporal awareness measures may be used to classify human performance into the control modes in the contextual control model (COCOM): scrambled, opportunistic, tactical, and strategic. Twenty-one participants participated in a withinsubjects experiment with an abstract task of resetting four independent timers within their respective windows of opportunity. The results show that temporal measures of task performance are sensitive to changes in task disruptions and difficulty and therefore have promise for human-aware AI.
{"title":"Measuring Temporal Awareness for Human-Aware AI","authors":"Margaret A. Gray, Zhuorui Yong, Abhijan Wasti, Esa M. Rantanen, Jamison R. Heard","doi":"10.1177/21695067231192635","DOIUrl":"https://doi.org/10.1177/21695067231192635","url":null,"abstract":"This research investigated human performance in response to task demands that may be used to convey information about the human to an artificial agent. We performed an experiment with a dynamic time-sharing task to investigate participants development of temporal awareness of the task event unfolding in time. Temporal awareness as an extension, or a special case, of situation awareness, may provide for useful measures of covert mental models applicable to numerous tasks and for input to human-aware AI agents. Temporal awareness measures may be used to classify human performance into the control modes in the contextual control model (COCOM): scrambled, opportunistic, tactical, and strategic. Twenty-one participants participated in a withinsubjects experiment with an abstract task of resetting four independent timers within their respective windows of opportunity. The results show that temporal measures of task performance are sensitive to changes in task disruptions and difficulty and therefore have promise for human-aware AI.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113886","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}
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