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Background. Mnemonic procedures are currently being taught to airline pilots to manage startle and surprise. We previously tested the effectiveness of a four-item mnemonic. Pilots generally rated it as useful but some remarked that it induced too much additional workload. Therefore, we tested whether a simpler mnemonic, Aviate-Breathe-Check, would be more useful. Method. The experiment took place in a hexapod simulator with a Piper Seneca aerodynamic model and a generic cockpit. Airline pilots (n = 25) were divided into an experimental (“ABC”) and control group. All received ground training on startle and surprise, which included instructions on the ABC mnemonic for the ABC group. The mnemonic aims to support prioritization of flight-path management (Aviate), followed by physiological and mental stress management (Breathe), followed by troubleshooting (Check). All pilots performed four familiarization scenarios, during which the ABC group practiced the ABC mnemonic. Two test scenarios were then performed to evaluate performance, mental effort, stress, and pilot evaluations of the ABC mnemonic. Results. The pilots’ evaluations of the ABC mnemonic were significantly higher than those were for the previously-tested mnemonic in the same scenarios. There were no significant differences between the ABC and control group in mental effort and stress, whereas there were trends towards higher mental effort and stress with the previous mnemonic. No significant effects on performance were found. Conclusions. The results suggest that the ABC mnemonic was more useful and easier to apply than a previously tested mnemonic. This is promising for the development of effective pilot training interventions for startle and surprise.
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Background. Mnemonic procedures are currently being taught to airline pilots to manage startle and surprise. We previously tested the effectiveness of a four-item mnemonic. Pilots generally rated it as useful but some remarked that it induced too much additional workload. Therefore, we tested whether a simpler mnemonic, Aviate-Breathe-Check, would be more useful. Method. The experiment took place in a hexapod simulator with a Piper Seneca aerodynamic model and a generic cockpit. Airline pilots (n = 25) were divided into an experimental (“ABC”) and control group. All received ground training on startle and surprise, which included instructions on the ABC mnemonic for the ABC group. The mnemonic aims to support prioritization of flight-path management (Aviate), followed by physiological and mental stress management (Breathe), followed by troubleshooting (Check). All pilots performed four familiarization scenarios, during which the ABC group practiced the ABC mnemonic. Two test scenarios were then performed to evaluate performance, mental effort, stress, and pilot evaluations of the ABC mnemonic. Results. The pilots’ evaluations of the ABC mnemonic were significantly higher than those were for the previously-tested mnemonic in the same scenarios. There were no significant differences between the ABC and control group in mental effort and stress, whereas there were trends towards higher mental effort and stress with the previous mnemonic. No significant effects on performance were found. Conclusions. The results suggest that the ABC mnemonic was more useful and easier to apply than a previously tested mnemonic. This is promising for the development of effective pilot training interventions for startle and surprise.
Many research works have been oriented to the formulation of different algorithms for estimating the paths in indoor environments from three-dimensional representations of space. The architectural configuration, the actions that take place within it, and the location of some objects in the space influence the paths along which is it possible to move, as they may cause visibility problems. To overcome the visibility issue, different methods have been proposed which allow to identify the visible areas and from a certain point of view, but often they do not take into account the user's visual perception of the environment and not allow estimating how much may be complicated to follow a certain path. In the field of space syntax and cognitive science, it has been attempted to describe the characteristics of a building or an urban environment by the isovists and visibility graphs methods; some numerical properties of these representations allow to describe the space as for how it is perceived by a user. However, most of these studies are directed to analyze the environment in a two-dimensional space. In this paper we propose a method to evaluate in a quantitative way the complexity of a certain path within an environment represented by a three-dimensional point cloud, by the combination of some of the previously mentioned techniques, considering the space visible from a certain point of view, depending on the moving agent (pedestrian, people in wheelchairs, UAV, UGV, robot).
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Many research works have been oriented to the formulation of different algorithms for estimating the paths in indoor environments from three-dimensional representations of space. The architectural configuration, the actions that take place within it, and the location of some objects in the space influence the paths along which is it possible to move, as they may cause visibility problems. To overcome the visibility issue, different methods have been proposed which allow to identify the visible areas and from a certain point of view, but often they do not take into account the user's visual perception of the environment and not allow estimating how much may be complicated to follow a certain path. In the field of space syntax and cognitive science, it has been attempted to describe the characteristics of a building or an urban environment by the isovists and visibility graphs methods; some numerical properties of these representations allow to describe the space as for how it is perceived by a user. However, most of these studies are directed to analyze the environment in a two-dimensional space. In this paper we propose a method to evaluate in a quantitative way the complexity of a certain path within an environment represented by a three-dimensional point cloud, by the combination of some of the previously mentioned techniques, considering the space visible from a certain point of view, depending on the moving agent (pedestrian, people in wheelchairs, UAV, UGV, robot).
