Pub Date : 2021-05-19DOI: 10.1177/15553434211017354
Nathan J. Mcneese, Mustafa Demir, Nancy J. Cooke, Manrong She
This article focuses on two fundamental human–human teamwork behaviors and seeks to understand them better in human–machine teams. Specifically, team situation awareness (TSA) and team conflict are examined in human–machine teams. There is a significant need to identify how TSA and team conflict occur during human–machine teaming, in addition to how they impact each other. In this work, we present an experiment aimed at understanding TSA and team conflict in the context of human–machine teaming in a remotely piloted aircraft system (RPAS). Three conditions were tested: (1) control: teams consisted of all humans; (2) synthetic: teams consisted of the pilot role being occupied by a computational agent based on ACT-R architecture that employed AI capabilities, with all other team roles being humans; and (3) experimenter: an experimenter playing the role of the pilot as a highly effective computational agent, with the other roles being humans. The results indicate that TSA improved over time in synthetic teams, improved and then stabilized over time in experimenter teams, and did not improve in control teams. In addition, results show that control teams had the most team conflict. Finally, in the control condition, team conflict negatively impacts TSA.
{"title":"Team Situation Awareness and Conflict: A Study of Human–Machine Teaming","authors":"Nathan J. Mcneese, Mustafa Demir, Nancy J. Cooke, Manrong She","doi":"10.1177/15553434211017354","DOIUrl":"https://doi.org/10.1177/15553434211017354","url":null,"abstract":"This article focuses on two fundamental human–human teamwork behaviors and seeks to understand them better in human–machine teams. Specifically, team situation awareness (TSA) and team conflict are examined in human–machine teams. There is a significant need to identify how TSA and team conflict occur during human–machine teaming, in addition to how they impact each other. In this work, we present an experiment aimed at understanding TSA and team conflict in the context of human–machine teaming in a remotely piloted aircraft system (RPAS). Three conditions were tested: (1) control: teams consisted of all humans; (2) synthetic: teams consisted of the pilot role being occupied by a computational agent based on ACT-R architecture that employed AI capabilities, with all other team roles being humans; and (3) experimenter: an experimenter playing the role of the pilot as a highly effective computational agent, with the other roles being humans. The results indicate that TSA improved over time in synthetic teams, improved and then stabilized over time in experimenter teams, and did not improve in control teams. In addition, results show that control teams had the most team conflict. Finally, in the control condition, team conflict negatively impacts TSA.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/15553434211017354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45469288","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 : 2021-05-16DOI: 10.1177/15553434211010573
Michael F. Schneider, Michael Miller, D. Jacques, Gilbert L. Peterson, Thomas C. Ford
Teaming permits cognitively complex work to be rapidly executed by multiple entities. As artificial agents (AAs) participate in increasingly complex cognitive work, they hold the promise of moving beyond tools to becoming effective members of human–agent teams. Coordination has been identified as the critical process that enables effective teams and is required to achieve the vision of tightly coupled teams of humans and AAs. This paper characterizes coordination on the axes of types, content, and cost. This characterization is grounded in the human and AA literature and is evaluated to extract design implications for human–agent teams. These design implications are the mechanisms, moderators, and models employed within human–agent teams, which illuminate potential AA design improvements to support coordination.
{"title":"Exploring the Impact of Coordination in Human–Agent Teams","authors":"Michael F. Schneider, Michael Miller, D. Jacques, Gilbert L. Peterson, Thomas C. Ford","doi":"10.1177/15553434211010573","DOIUrl":"https://doi.org/10.1177/15553434211010573","url":null,"abstract":"Teaming permits cognitively complex work to be rapidly executed by multiple entities. As artificial agents (AAs) participate in increasingly complex cognitive work, they hold the promise of moving beyond tools to becoming effective members of human–agent teams. Coordination has been identified as the critical process that enables effective teams and is required to achieve the vision of tightly coupled teams of humans and AAs. This paper characterizes coordination on the axes of types, content, and cost. This characterization is grounded in the human and AA literature and is evaluated to extract design implications for human–agent teams. These design implications are the mechanisms, moderators, and models employed within human–agent teams, which illuminate potential AA design improvements to support coordination.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2021-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/15553434211010573","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41399957","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 : 2021-04-17DOI: 10.1177/15553434211009024
Fjollë Novakazi, Mikael Johansson, Helena Strömberg, M. Karlsson
Extant levels of automation (LoAs) taxonomies describe variations in function allocations between the driver and the driving automation system (DAS) from a technical perspective. However, these taxonomies miss important human factors issues and when design decisions are based on them, the resulting interaction design leaves users confused. Therefore, the aim of this paper is to describe how users perceive different DASs by eliciting insights from an empirical driving study facilitating a Wizard-of-Oz approach, where 20 participants were interviewed after experiencing systems on two different LoAs under real driving conditions. The findings show that participants talked about the DAS by describing different relationships and dependencies between three different elements: the context (traffic conditions, road types), the vehicle (abilities, limitations, vehicle operations), and the driver (control, attentional demand, interaction with displays and controls, operation of vehicle), each with associated aspects that indicate what users identify as relevant when describing a vehicle with automated systems. Based on these findings, a conceptual model is proposed by which designers can differentiate LoAs from a human-centric perspective and that can aid in the development of design guidelines for driving automation.
{"title":"Levels of What?Investigating Drivers’ Understanding of Different Levels of Automation in Vehicles","authors":"Fjollë Novakazi, Mikael Johansson, Helena Strömberg, M. Karlsson","doi":"10.1177/15553434211009024","DOIUrl":"https://doi.org/10.1177/15553434211009024","url":null,"abstract":"Extant levels of automation (LoAs) taxonomies describe variations in function allocations between the driver and the driving automation system (DAS) from a technical perspective. However, these taxonomies miss important human factors issues and when design decisions are based on them, the resulting interaction design leaves users confused. Therefore, the aim of this paper is to describe how users perceive different DASs by eliciting insights from an empirical driving study facilitating a Wizard-of-Oz approach, where 20 participants were interviewed after experiencing systems on two different LoAs under real driving conditions. The findings show that participants talked about the DAS by describing different relationships and dependencies between three different elements: the context (traffic conditions, road types), the vehicle (abilities, limitations, vehicle operations), and the driver (control, attentional demand, interaction with displays and controls, operation of vehicle), each with associated aspects that indicate what users identify as relevant when describing a vehicle with automated systems. Based on these findings, a conceptual model is proposed by which designers can differentiate LoAs from a human-centric perspective and that can aid in the development of design guidelines for driving automation.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2021-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/15553434211009024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46126842","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 : 2021-03-01DOI: 10.1177/1555343420986657
Floris van den Oever, J. Schraagen
Teams operating in time-pressured, dynamic environments frequently need to cope with critical situations varying in complexity and hazard. To cope with critical situations, teams may have to adapt their communication processes. Adaptation of team communication processes has been studied mostly at short time frames (minutes). Literature on adapting communication at longer time frames is limited (hours, relative to minutes). We used the relational event model to compare team communication in critical and noncritical situations of pediatric cardiac surgeries and Apollo 13 flight director’s voice loops. Teams showed some flattening of communication structures in critical situations. Both teams maintained institutional roles and displayed closed-loop and information-seeking communication. Communication patterns may change further with increasing criticality. The exact way teams adapt to critical situations may differ depending on team, team size and situation. Findings may inform team training procedures or team structure development.
{"title":"Team Communication Patterns in Critical Situations","authors":"Floris van den Oever, J. Schraagen","doi":"10.1177/1555343420986657","DOIUrl":"https://doi.org/10.1177/1555343420986657","url":null,"abstract":"Teams operating in time-pressured, dynamic environments frequently need to cope with critical situations varying in complexity and hazard. To cope with critical situations, teams may have to adapt their communication processes. Adaptation of team communication processes has been studied mostly at short time frames (minutes). Literature on adapting communication at longer time frames is limited (hours, relative to minutes). We used the relational event model to compare team communication in critical and noncritical situations of pediatric cardiac surgeries and Apollo 13 flight director’s voice loops. Teams showed some flattening of communication structures in critical situations. Both teams maintained institutional roles and displayed closed-loop and information-seeking communication. Communication patterns may change further with increasing criticality. The exact way teams adapt to critical situations may differ depending on team, team size and situation. Findings may inform team training procedures or team structure development.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420986657","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43805067","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 : 2021-03-01DOI: 10.1177/1555343420983126
Alexandra S. Mueller, I. Reagan, Jessica B. Cicchino
Level 2 driving automation has the potential to reduce crashes; however, there are known risks when using these systems, particularly as they relate to drivers becoming disengaged from driving. This paper provides data-driven recommendations for Level 2 driving automation design using the best currently available methods to encourage driver engagement and communicate where and how a system can safely be used. Our recommendations pertaining to driver engagement concern driver management systems that monitor the driver for signs of disengagement and return the driver to the loop using a multimodal escalation process with attention reminders, countermeasures for sustained noncompliance to the attention reminders, and proactive methods for keeping drivers engaged with respect to driver-system interactions and system functionality considerations. We also provide guidance on how the operational design domain (ODD), driver responsibilities, and system limitations should be communicated and how these systems must be self-limited within the ODD. In addition, we discuss the benefits and limitations of training to emphasize the importance of making these systems intuitive to all users, regardless of training, to ensure proper use. These recommendations should be applied as a whole, because selectively adhering to only some may inadvertently exacerbate the dangers of driver disengagement.
{"title":"Addressing Driver Disengagement and Proper System Use: Human Factors Recommendations for Level 2 Driving Automation Design","authors":"Alexandra S. Mueller, I. Reagan, Jessica B. Cicchino","doi":"10.1177/1555343420983126","DOIUrl":"https://doi.org/10.1177/1555343420983126","url":null,"abstract":"Level 2 driving automation has the potential to reduce crashes; however, there are known risks when using these systems, particularly as they relate to drivers becoming disengaged from driving. This paper provides data-driven recommendations for Level 2 driving automation design using the best currently available methods to encourage driver engagement and communicate where and how a system can safely be used. Our recommendations pertaining to driver engagement concern driver management systems that monitor the driver for signs of disengagement and return the driver to the loop using a multimodal escalation process with attention reminders, countermeasures for sustained noncompliance to the attention reminders, and proactive methods for keeping drivers engaged with respect to driver-system interactions and system functionality considerations. We also provide guidance on how the operational design domain (ODD), driver responsibilities, and system limitations should be communicated and how these systems must be self-limited within the ODD. In addition, we discuss the benefits and limitations of training to emphasize the importance of making these systems intuitive to all users, regardless of training, to ensure proper use. These recommendations should be applied as a whole, because selectively adhering to only some may inadvertently exacerbate the dangers of driver disengagement.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420983126","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44151241","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 : 2020-12-01DOI: 10.1177/1555343420976014
E. Papautsky, Robert V Strouse, Cindy Dominguez
Representations of step-by-step procedures, such as task flows, are developed and used to support technology design and evaluation as well as for training purposes in complex work domains. However, task flows may not represent how people carry out work under uncertainty, time pressure, or high-risk conditions. We combined methods of cognitive task analysis and participatory design, resulting in a new approach for developing task flows. This approach accounts for both cognitive and behavioral work and explicitly represents its dynamic nature. Additional advantages of this approach include flexibility and adaptiveness to help overcome challenges of conducting research in real-world domains, including time constraints and access to subject matter experts. We demonstrate this approach in the context of developing a task flow for a submarine watch team’s use of an updated imaging system to maintain the ship’s safety by forming and maintaining a picture of the external environment. We provide a detailed description of each phase as well as a domain-neutral ready-to-use job aid.
{"title":"Combining Cognitive Task Analysis and Participatory Design Methods to Elicit and Represent Task Flows","authors":"E. Papautsky, Robert V Strouse, Cindy Dominguez","doi":"10.1177/1555343420976014","DOIUrl":"https://doi.org/10.1177/1555343420976014","url":null,"abstract":"Representations of step-by-step procedures, such as task flows, are developed and used to support technology design and evaluation as well as for training purposes in complex work domains. However, task flows may not represent how people carry out work under uncertainty, time pressure, or high-risk conditions. We combined methods of cognitive task analysis and participatory design, resulting in a new approach for developing task flows. This approach accounts for both cognitive and behavioral work and explicitly represents its dynamic nature. Additional advantages of this approach include flexibility and adaptiveness to help overcome challenges of conducting research in real-world domains, including time constraints and access to subject matter experts. We demonstrate this approach in the context of developing a task flow for a submarine watch team’s use of an updated imaging system to maintain the ship’s safety by forming and maintaining a picture of the external environment. We provide a detailed description of each phase as well as a domain-neutral ready-to-use job aid.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420976014","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45533247","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 : 2020-11-09DOI: 10.1177/1555343420962897
K. Volz, M. Dorneich
This work aimed to identify cognitive skills associated with flight planning, suggest which skills might be susceptible to skill degradation, and investigate the effects of cognitive skill degradation over time. Information automation systems offload cognitive tasks to reduce workload and error. However, the same phenomena seen with physical skill degradation in highly automated aircrafts may also occur when automating cognitive tasks. Two studies were conducted. An applied cognitive task analysis identified cognitive skills in flight planning. An empirical evaluation examined whether some of those skills were susceptible to cognitive skill degradation over time when using automation. Participants were placed into three groups. After conducting a flight planning task manually, groups differed in the next three practice trials: manual, alternating between manual and automation, or only with automation. Finally, all groups conducted the task manually again. Trials were separated by 2 weeks. The automation group showed the most performance degradation and highest workload, while the manual group showed the least performance degradation and least workload. Automation use did not provide the practice needed to mitigate cognitive skill degradation. Analysis of the impacts of information automation on cognitive performance is a first step in understanding the root causes of errors and developing mitigations.
{"title":"Evaluation of Cognitive Skill Degradation in Flight Planning","authors":"K. Volz, M. Dorneich","doi":"10.1177/1555343420962897","DOIUrl":"https://doi.org/10.1177/1555343420962897","url":null,"abstract":"This work aimed to identify cognitive skills associated with flight planning, suggest which skills might be susceptible to skill degradation, and investigate the effects of cognitive skill degradation over time. Information automation systems offload cognitive tasks to reduce workload and error. However, the same phenomena seen with physical skill degradation in highly automated aircrafts may also occur when automating cognitive tasks. Two studies were conducted. An applied cognitive task analysis identified cognitive skills in flight planning. An empirical evaluation examined whether some of those skills were susceptible to cognitive skill degradation over time when using automation. Participants were placed into three groups. After conducting a flight planning task manually, groups differed in the next three practice trials: manual, alternating between manual and automation, or only with automation. Finally, all groups conducted the task manually again. Trials were separated by 2 weeks. The automation group showed the most performance degradation and highest workload, while the manual group showed the least performance degradation and least workload. Automation use did not provide the practice needed to mitigate cognitive skill degradation. Analysis of the impacts of information automation on cognitive performance is a first step in understanding the root causes of errors and developing mitigations.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2020-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420962897","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47754958","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 : 2020-09-01DOI: 10.1177/1555343420943460
August A. Capiola, H. Baxter, M. Pfahler, Christopher S. Calhoun, P. Bobko
Trust is important for establishing successful relationships and performance outcomes. In some contexts, however, rich information such as knowledge of and experience with a teammate is not available to inform one’s trust. Yet, parties in these contexts are expected to work together toward common goals for a relatively brief and finite period of time. This research investigated the antecedents to quickly-formed trust (often referred to as swift trust) in fast-paced, time-constrained contexts. We conducted a cognitive task analysis (CTA) based on 11 structured interviews of subject-matter experts (SMEs) in Intelligence (Intel)—a heterogeneous job category comprising distributed and co-located personnel within multi-domain command and control (MDC2) environments. Eight antecedents to swift trust emerged from these interviews (i.e., ability, integrity, benevolence, communication, mission-focus, self-awareness, shared perspectives/experiences, and calm), with further analysis implying that swift trust is a relevant and emergent state in MDC2 that facilitates reliance. These findings offer implications for teams operating in high-risk distributed contexts and should be expanded through basic experimental investigations as well as applied initiatives.
{"title":"Swift Trust in Ad Hoc Teams: A Cognitive Task Analysis of Intelligence Operators in Multi-Domain Command and Control Contexts","authors":"August A. Capiola, H. Baxter, M. Pfahler, Christopher S. Calhoun, P. Bobko","doi":"10.1177/1555343420943460","DOIUrl":"https://doi.org/10.1177/1555343420943460","url":null,"abstract":"Trust is important for establishing successful relationships and performance outcomes. In some contexts, however, rich information such as knowledge of and experience with a teammate is not available to inform one’s trust. Yet, parties in these contexts are expected to work together toward common goals for a relatively brief and finite period of time. This research investigated the antecedents to quickly-formed trust (often referred to as swift trust) in fast-paced, time-constrained contexts. We conducted a cognitive task analysis (CTA) based on 11 structured interviews of subject-matter experts (SMEs) in Intelligence (Intel)—a heterogeneous job category comprising distributed and co-located personnel within multi-domain command and control (MDC2) environments. Eight antecedents to swift trust emerged from these interviews (i.e., ability, integrity, benevolence, communication, mission-focus, self-awareness, shared perspectives/experiences, and calm), with further analysis implying that swift trust is a relevant and emergent state in MDC2 that facilitates reliance. These findings offer implications for teams operating in high-risk distributed contexts and should be expanded through basic experimental investigations as well as applied initiatives.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420943460","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45273401","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 : 2020-08-26DOI: 10.1177/1555343420948720
M. Tušl, G. Rainieri, F. Fraboni, Marco de Angelis, M. Depolo, L. Pietrantoni, Andrea Pingitore
Helicopter shipboard landing is a cognitively complex task that is challenging both for pilots and their crew. Effective communication, accurate reading of the flight instruments, as well as monitoring of the external environment are crucial for a successful landing. In particular, the final phases of landing are critical as they imply high workload situations in an unstable environment with restricted space. In the present qualitative study, we interviewed ten helicopter pilots from the Italian Navy using an applied cognitive task analysis approach. We aimed to obtain a detailed description of the landing procedure, and to identify relevant factors that affect pilots’ workload, performance, and safety. Based on the content analysis of the interviews, we have identified six distinct phases of approaching and landing on a ship deck and four categories of factors that may significantly affect pilots’ performance and safety of the landing procedure. Consistent with previous studies, our findings suggest that external visual cueing is vital for a successful landing, in particular during the last phases of landing. Therefore, based on the pilots’ statements, we provide suggestions for possible improvements of external visual cues that have the potential to reduce pilots’ workload and improve the overall safety of landing operations.
{"title":"Helicopter Pilots’ Tasks, Subjective Workload, and the Role of External Visual Cues During Shipboard Landing","authors":"M. Tušl, G. Rainieri, F. Fraboni, Marco de Angelis, M. Depolo, L. Pietrantoni, Andrea Pingitore","doi":"10.1177/1555343420948720","DOIUrl":"https://doi.org/10.1177/1555343420948720","url":null,"abstract":"Helicopter shipboard landing is a cognitively complex task that is challenging both for pilots and their crew. Effective communication, accurate reading of the flight instruments, as well as monitoring of the external environment are crucial for a successful landing. In particular, the final phases of landing are critical as they imply high workload situations in an unstable environment with restricted space. In the present qualitative study, we interviewed ten helicopter pilots from the Italian Navy using an applied cognitive task analysis approach. We aimed to obtain a detailed description of the landing procedure, and to identify relevant factors that affect pilots’ workload, performance, and safety. Based on the content analysis of the interviews, we have identified six distinct phases of approaching and landing on a ship deck and four categories of factors that may significantly affect pilots’ performance and safety of the landing procedure. Consistent with previous studies, our findings suggest that external visual cueing is vital for a successful landing, in particular during the last phases of landing. Therefore, based on the pilots’ statements, we provide suggestions for possible improvements of external visual cues that have the potential to reduce pilots’ workload and improve the overall safety of landing operations.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2020-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420948720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48936489","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 : 2020-08-17DOI: 10.1177/1555343420926287
T. van Gelder, Ariel Kruger, Sujai Thomman, Richard de Rozario, Elizabeth Silver, Morgan Saletta, Ashley Barnett, R. Sinnott, G. Jayaputera, M. Burgman
How might analytic reasoning in intelligence reports be substantially improved? One conjecture is that this can be achieved through a combination of crowdsourcing and structured analytic techniques (SATs). To explore this conjecture, we developed a new crowdsourcing platform supporting groups in collaborative reasoning and intelligence report drafting using a novel SAT we call “Contending Analyses.” In this paper we present findings from a large study designed to assess whether groups of professional analysts working on the platform produce better-reasoned reports than those analysts produce when using methods and tools normally used in their organizations. Secondary questions were whether professional analysts working on the platform produce better reasoning than the general public working on the platform; and how usable the platform is. Our main finding is a large effect size (Cohen’s d = 1.37) in favor of working on platform. This provides early support for the general conjecture. We discuss limitations of our study, implications for intelligence organizations, and future directions for the work as a whole.
{"title":"Improving Analytic Reasoning via Crowdsourcing and Structured Analytic Techniques","authors":"T. van Gelder, Ariel Kruger, Sujai Thomman, Richard de Rozario, Elizabeth Silver, Morgan Saletta, Ashley Barnett, R. Sinnott, G. Jayaputera, M. Burgman","doi":"10.1177/1555343420926287","DOIUrl":"https://doi.org/10.1177/1555343420926287","url":null,"abstract":"How might analytic reasoning in intelligence reports be substantially improved? One conjecture is that this can be achieved through a combination of crowdsourcing and structured analytic techniques (SATs). To explore this conjecture, we developed a new crowdsourcing platform supporting groups in collaborative reasoning and intelligence report drafting using a novel SAT we call “Contending Analyses.” In this paper we present findings from a large study designed to assess whether groups of professional analysts working on the platform produce better-reasoned reports than those analysts produce when using methods and tools normally used in their organizations. Secondary questions were whether professional analysts working on the platform produce better reasoning than the general public working on the platform; and how usable the platform is. Our main finding is a large effect size (Cohen’s d = 1.37) in favor of working on platform. This provides early support for the general conjecture. We discuss limitations of our study, implications for intelligence organizations, and future directions for the work as a whole.","PeriodicalId":46342,"journal":{"name":"Journal of Cognitive Engineering and Decision Making","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2020-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/1555343420926287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43903704","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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