PZ
P.M.T. Zaal
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9 records found
1
Linear mixed-effects models provide several benefits over more traditional statistical inference tests and are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as between both experiments.
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Linear mixed-effects models provide several benefits over more traditional statistical inference tests and are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as between both experiments.
This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.
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This paper presents the results of a training and retention experiment conducted to objectively and quantitatively evaluate the acquisition, decay, and retention of skill-based manual control behavior in a compensatory dual-axis roll and pitch attitude tracking task. In this study, thirty-eight fully task-naive participants were trained in a fixed-base setting in the Human-Machine Interaction Laboratory at Delft University of Technology and subsequently divided into three matched groups based on their training performance and control behavior. Performance of the first group was re-evaluated after a period of non-practice of six months, whereas the second group was retested at both three and six months after training, and skill retention of the third group was measured after two, four, and six months. The goal of the experiment was to model the decay curve of skill-based manual control behavior and to determine the re-acquisition rate of lost skills compared to their initial acquisition rate. To explicitly quantify changes in manual control skills, learning curve models were fitted to metrics of task performance and control activity. The results suggest that control skills decay following a negatively accelerating decay curve and that lost skills are re-acquired at a higher rate than their initial development rate.
This paper describes an experiment investigating the effects of motion filter order on human manual control tracking behavior and performance. The experiment was performed on two simulators: the Vertical Motion Simulator at NASA Ames Research Center and the SIMONA Research Simulator at Delft University of Technology. Eighteen pilots in the Vertical Motion Simulator and twenty pilots in the SIMONA Research Simulator performed the experiment with a full factorial variation of three motion filter orders and two motion filter frequencies, in addition to a reference no-motion and full-motion condition. Motion shaping filters derived from Objective Motion Cueing Test measurements on the Vertical Motion Simulator were included in the SIMONA Research Simulator motion logic to match the motion cues between both simulators. Furthermore, the side sticks were set to matching characteristics and the visual cues were matched in terms of time delay, graphics size and screen characteristics. With increased motion filter order, pilots showed worse performance and a lowered contribution of motion feedback in their control strategy. Increasing the motion filter break frequency had similar effects, which were stronger than the effects of increasing the motion filter order, for the eight experimental conditions that were considered in this experiment. For the same motion condition the simulators showed offsets in the results. However, the trends between the motion conditions were similar, leading to the conclusion that for simulator comparisons relative trends are easier to replicate between simulators than absolute results within one condition.
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This paper describes an experiment investigating the effects of motion filter order on human manual control tracking behavior and performance. The experiment was performed on two simulators: the Vertical Motion Simulator at NASA Ames Research Center and the SIMONA Research Simulator at Delft University of Technology. Eighteen pilots in the Vertical Motion Simulator and twenty pilots in the SIMONA Research Simulator performed the experiment with a full factorial variation of three motion filter orders and two motion filter frequencies, in addition to a reference no-motion and full-motion condition. Motion shaping filters derived from Objective Motion Cueing Test measurements on the Vertical Motion Simulator were included in the SIMONA Research Simulator motion logic to match the motion cues between both simulators. Furthermore, the side sticks were set to matching characteristics and the visual cues were matched in terms of time delay, graphics size and screen characteristics. With increased motion filter order, pilots showed worse performance and a lowered contribution of motion feedback in their control strategy. Increasing the motion filter break frequency had similar effects, which were stronger than the effects of increasing the motion filter order, for the eight experimental conditions that were considered in this experiment. For the same motion condition the simulators showed offsets in the results. However, the trends between the motion conditions were similar, leading to the conclusion that for simulator comparisons relative trends are easier to replicate between simulators than absolute results within one condition.
Manual control cybernetics
State-of-the-art and current trends
Journal article
(2017)
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Max Mulder, Daan M. Pool, David A. Abbink, Erwin R. Boer, Peter M.T. Zaal, Frank M. Drop, Kasper van der El, Marinus M. van Paassen
Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics—human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling—that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semiautomatic control.
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Manual control cybernetics aims to understand and describe how humans control vehicles and devices using mathematical models of human control dynamics. This “cybernetic approach” enables objective and quantitative comparisons of human behavior, and allows a systematic optimization of human control interfaces and training associated with manual control. Current cybernetics theory is primarily based on technology and analysis methods formalized in the 1960s and has shown to be limited in its capability to capture the full breadth of human cognition and control. This paper reviews the current state-of-the-art in our knowledge of human manual control, points out the main fundamental limitations in cybernetics, and proposes a possible roadmap to advance the theory and its applications. Central in this roadmap will be a shift from the current linear time-invariant modeling approach that is only truly valid for human behavior under tightly controlled and stationary conditions, to methods that facilitate the analysis of adaptive, and possibly time-varying, human behavior in realistic control tasks. Examples of key current developments in the field of cybernetics—human use of preview, predictable discrete maneuvering, skill acquisition and training, time-varying human modeling, and neuromuscular system modeling—that contribute to this shift are presented in this paper. The new foundations for cybernetics that will emerge from these efforts will impact all domains that involve humans in manual and semiautomatic control.
This paper presents a cybernetic approach to assess the training of manual control skills in simulators. The approach uses multi-channel pilot models that separate pilots’ responses to visual and motion stimuli. This allows for a quantitative analysis of pilots’ use of visual and motion cues for manual aircraft control, as well as the evolution of these control skills during training and after transfer. The cybernetic approach was applied to data from a quasi-transfer-of-training experiment performed in the SIMONA Research Simulator at Delft University of Technology. In this experiment, fully task-naive participants were trained to perform an aircraft pitch attitude tracking task in a fixed-base simulator environment. After training, participants were transferred to a motion-base simulator environment. Results indicate that the cybernetic approach is successful in revealing progressive changes in participants’ utilization of visual and motion cues – i.e., their equalization dynamics – during training and after transfer. Furthermore, the results show that convergence to a final skill-based manual control strategy requires significant training.
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This paper presents a cybernetic approach to assess the training of manual control skills in simulators. The approach uses multi-channel pilot models that separate pilots’ responses to visual and motion stimuli. This allows for a quantitative analysis of pilots’ use of visual and motion cues for manual aircraft control, as well as the evolution of these control skills during training and after transfer. The cybernetic approach was applied to data from a quasi-transfer-of-training experiment performed in the SIMONA Research Simulator at Delft University of Technology. In this experiment, fully task-naive participants were trained to perform an aircraft pitch attitude tracking task in a fixed-base simulator environment. After training, participants were transferred to a motion-base simulator environment. Results indicate that the cybernetic approach is successful in revealing progressive changes in participants’ utilization of visual and motion cues – i.e., their equalization dynamics – during training and after transfer. Furthermore, the results show that convergence to a final skill-based manual control strategy requires significant training.
This study attempts to partially explain the characteristics of the human perceptual remnant, following Levison’s representation of the remnant as an equivalent observation noise. Eye activity parameters are recorded using an eye tracker in two compensatory tracking tasks in which the visual information is presented using either a first or second-order visual stimulus. Differences in the two conditions between remnant characteristics, eye activity measures and human operator model parameters are analyzed, using preliminary data from three subjects. Preliminary results show that the second-order visual stimulus introduces changes in both eye activity and remnant model parameters. Although high correlations are observed between remnant gain and blink frequency, between remnant break frequency and eye opening amplitude, and between remnant power and pupil diameter, a definitive conclusion about the perceptual remnant - eye activity characteristics relation cannot be drawn due to the small sample size of the obtained data. This preliminary study is a first step in identifying possible physiological parameters that affect the perceptual human remnant.
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This study attempts to partially explain the characteristics of the human perceptual remnant, following Levison’s representation of the remnant as an equivalent observation noise. Eye activity parameters are recorded using an eye tracker in two compensatory tracking tasks in which the visual information is presented using either a first or second-order visual stimulus. Differences in the two conditions between remnant characteristics, eye activity measures and human operator model parameters are analyzed, using preliminary data from three subjects. Preliminary results show that the second-order visual stimulus introduces changes in both eye activity and remnant model parameters. Although high correlations are observed between remnant gain and blink frequency, between remnant break frequency and eye opening amplitude, and between remnant power and pupil diameter, a definitive conclusion about the perceptual remnant - eye activity characteristics relation cannot be drawn due to the small sample size of the obtained data. This preliminary study is a first step in identifying possible physiological parameters that affect the perceptual human remnant.