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Effects of Target Signal Shape and System Dynamics on Feedforward in Manual Control
Abstract
The human controller (HC) in manual control of a dynamical system often follows a visible and predictable reference path (target). The HC can adopt a control strategy combining closed-loop feedback and an open-loop feedforward response. The effects of the target signal waveform shape and the system dynamics on the human feedforward dynamics are still largely unknown, even for common, stable, vehicle-like dynamics. This paper studies the feedforward dynamics through computer model simulations and compares these to system identification results from human-in-the-loop experimental data. Two target waveform shapes are considered, constant velocity ramp segments and constant acceleration parabola segments. Furthermore, three representative vehicle-like system dynamics are considered: 1) a single integrator (SI); 2) a second-order system; and 3) a double integrator. The analyses show that the HC utilizes a combined feedforward/feedback control strategy for all dynamics with the parabola target, and for the SI and second-order system with the ramp target. The feedforward model parameters are, however, very different between the two target waveform shapes, illustrating the adaptability of the HC to task variables. Moreover, strong evidence of anticipatory control behavior in the HC is found for the parabola target signal. The HC anticipates the future course of the parabola target signal given extensive practice, reflected by negative feedforward time delay estimates.
... Studying feedforward thus requires novel black-box HC identification methods, e.g., based on LTI AutoRegressive with eXternal input (ARX) models [137]. Fig. 5 shows identification results obtained with the novel ARX-based method of [137] from a human-in-the-loop tracking experiment featuring target signals consisting of ramp segments [138]. Black-box identification results as shown in Fig. 5, provide a means to objectively detect the presence of feedforward HC control responses. ...
... For four participants, however, the phase response is mostly flat or even becomes positive, indicating a negative time delay and thus anticipation of the future course of the target. From observations it can be deduced that the feedforward path H pt of the HC model of Fig. 5 can be [138], for Hc(s) = 1/s. modeled with a gain, inverse system dynamics [101], a lowpass filter [123], and a time delay: ...
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.
... One of the main factors that was omitted from this task is humans' ability to form and make use of predictions in guiding their behaviours. There has been evidence showing that human tracking performance is much better for predictable signals than for unpredictable ones, even if the frequency and bandwidth of the signals are the same (Levison et al., 1969;Poulton, 1952;Pew et al., 1967;Poulton, 1957;Noble et al., 1966;Trumbo et al., 1965;Drop et al., 2016Drop et al., , 2013Laurense et al., 2014;Drop et al., 2018). We have also shown from our experiments that regardless of the speed of the signal, signal sample entropy, which is a measure of signal predictability, dictates tracking performance in terms of total information transferred (Lam and Zénon, 2021). ...
The sensation of effort, from an evolutionary point of view, could be understood as a mechanism for signalling the expenditure of scarce resources and which allows their efficient allocation. Understanding the decision making processes that are involved in effort allocation is crucial if one is to gain insight into human behaviour.One type of effort that is observed and reported in humans, and is the central subject of this thesis, is cognitive effort. Although there is still no general consensus over the true nature of the resources that cognitive effort was developed to safeguard, its aversiveness and involvement in decision-making are widely agreed upon. The principle of least action, entailing the minimisation of effort, provides a rational account for seemingly sub-optimal behaviours.Nevertheless, there are major obstacles to overcome in studying cognitive effort, many of which are associated with complications and biases associated with the measurement of subjective experiences. In response to these limitations, some recent work has focused instead on the influence that these subjective experiences have over observable, free choices of engagement. Notably, a neuroeconomic approach was employed to establish preference functions that express cognitive effort costs and task rewards in a common currency.Following this line of research, an information theoretic model of cognitive effort is proposed in this thesis work. The motivation for such a model is three-fold.Firstly, the mathematical framework of information theory provides a natural common currency, that is information, for quantifying task difficulty, engagement and performance. This could provide a more direct interpretation of the relationship between task demand, effort expenditure and associated gains.Secondly, information theoretic measures derived from first principles set bounds on the information rate associated with automatic and controlled behaviours.Lastly, information theory provides the common framework in which the interpretation of cognitive effort can be linked to well-established theories regarding computational efficiency in the brain such as efficient coding and/or predictive coding theorems.In this thesis work, a series of experiments were designed to validate the proposed model of cognitive effort. The main task used in these experiments is a continuous visual-motor tracking task with joystick control. In the first study, information theoretic measures representing information rate of the feed-back (controlled) and feed-forward (automatic) processing of the signal were derived from first principles and were validated through simulated tracking data from a linear quadratic regulator (LQR) model. These measures were subsequently applied to real tracking data to gain insight of their engagement in the task in terms of real-time information processing rate.The second study aims at investigating and comparing the effect that different task attributes, including signal speed, predictability and joystick delay have on feed-back and feed-forward information rate, as well as on performance.The third and fourth studies were dual-task experiments designed to investigate cross-task interactions in information rate and to infer global limits in the brain in terms of computational resources.Lastly, a model is built by modifying an intermittent controller to include an information bottleneck objective to provide a normative account of the cost/value trade-off in human tracking performance. This model is then applied to behavioral data to study the principles of allocation of information rate and the optimality of human motor control.
Cyberneticists develop mathematical human control models which are used to tune manual control systems and understand human performance limits. Neuroscientists explore the physiology and circuitry of the central nervous system to understand how the brain works. Both research human visuomotor control tasks, such as the pursuit tracking task. In this paper we discuss some commonalities and differences in both approaches to better understand the adapting human controller. Special attention is given to Adaptive Model Theory, which studied adaptive human control using several linear and nonlinear control engineering techniques. The insights gained yield schemes and concepts which pave the way for key future work on this topic.
In human-in-the-loop control systems, operators can learn to manually control dynamic machines with either hand using a combination of reactive (feedback) and predictive (feedforward) control. This article studies the effect of handedness on learned controllers and performance during a trajectory-tracking task. In an experiment with 18 participants, subjects perform an assay of unimanual trajectory-tracking and disturbance-rejection tasks through second-order machine dynamics, first with one hand then the other. To assess how hand preference (or dominance) affects learned controllers, we extend, validate, and apply a nonparametric modeling method to estimate the concurrent feedback and feedforward controllers. We find that performance improves because feedback adapts, regardless of the hand used. We do not detect statistically significant differences in performance or learned controllers between hands. Adaptation to reject disturbances arising exogenously (i.e., applied by the experimenter) and endogenously (i.e., generated by sensorimotor noise) explains observed performance improvements.
We present results from an experiment in which 55 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into five groups. For each interaction, a subject performs a command-following task, where the reference command is the same for all subjects and all trials; however, each group interacts with a different linear time-invariant dynamic system. We use a subsystem identification algorithm to estimate the control strategy that each subject uses on each trial. The experimental and identification results are used to examine the impact of the system characteristics (e.g., poles, zeros, relative degree, system order, phase lag) on the subjects’ command-following performance and the control strategies that the subjects learn. Results demonstrate that phase lag (which arises from higher relative degree and nonminimum-phase zeros) tends to make dynamic systems more difficult for humans to control, whereas higher system order does not necessarily make a system more difficult to control. The identification results demonstrate that improvement in performance is attributed to: 1) using a comparatively accurate approximation of the inverse dynamics in feedforward; and 2) using a feedback controller with comparatively high gain. Results also demonstrate that system phase lag is an important impediment to a subject’s ability to approximate the inverse dynamics in feedforward, and that a key aspect of approximating the inverse dynamics in feedforward is learning to use the correct amount of phase lead in feedforward.
This article presents results from an experiment in which 44 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into four groups. All groups interact with the same dynamic system, but each group performs a different sequence of command-following tasks. All reference commands have frequency content between 0 and 0.5 Hz. We use a subsystem identification algorithm to estimate the control strategy (feedback and feedforward) that each subject uses on each trial. The experimental and identification results are used to examine the impact of the command-following tasks on the subjects' performance and the control strategies that the subjects learn. Results demonstrate that certain reference commands (e.g., a sum of sinusoids) are more difficult for subjects to learn to follow than others (e.g., a chirp), and the difference in difficulty is related to the subjects' ability to match the phase of the reference command. In addition, the identification results show that differences in command-following performance for different tasks can be attributed to three aspects of the subjects' identified controllers: 1) compensating for time delay in feedforward; 2) using a comparatively accurate approximation of the inverse dynamics in feedforward; and 3) using a feedback controller with comparatively high gain. Results also demonstrate that subjects generalize their control strategy when the command changes. Specifically, when the command changes, subjects maintain relatively high gain in feedback and retain their feedforward internal model of the inverse dynamics. Finally, we provide evidence that subjects use prediction of the command (if possible) to improve performance but that subjects can learn to improve performance without prediction. Specifically, subjects learn to use feedback controllers with comparatively high gain to improve performance even though the command is unpredictable.
We present results from an experiment in which 33 human subjects interact with a dynamic system 40 times over a one-week period. The subjects are divided into three groups. For each interaction, a subject performs a command-following task, where the reference command is the same for all trials and all subjects. However, each group interacts with a different dynamic system, which is represented by a transfer function. The transfer functions have the same poles but different zeros. One has a minimum-phase zero zₘ < 0, another has a nonminimum-phase zero zₙ = - zₘ > 0, and the last has a slower (i.e., closer to the imaginary axis) nonminimum-phase zero zsn ∈ (0,zₙ). The experimental results show that nonminimum-phase zeros tend to make dynamic systems more difficult for humans to learn to control. We use a subsystem identification algorithm to identify the control strategy that each subject uses on each trial. The identification results show that the identified feedforward controllers approximate the inverse dynamics of the system with which the subjects interact better on the last trial than on the first trial. However, the subjects interacting with the minimum-phase system are able to approximate the inverse dynamics in feedforward more accurately than the subjects interacting with the nonminimum-phase system. This observation suggests that nonminimum-phase zeros are an impediment to approximating inverse dynamics in feedforward. Finally, we provide evidence that humans rely on feedforward-step-like-control strategies with systems (e.g., nonminimum-phase systems) for which it is difficult to approximate the inverse dynamics in feedforward.
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.
Real-life tracking tasks often show preview information to the human controller about the future track to follow. The effect of preview on manual control behavior is still relatively unknown. This paper proposes a generic operator model for preview tracking, empirically derived from experimental measurements. Conditions included pursuit tracking, i.e., without preview information, and tracking with 1 s of preview. Controlled element dynamics varied between gain, single integrator, and double integrator. The model is derived in the frequency domain, after application of a black-box system identification method based on Fourier coefficients. Parameter estimates are obtained to assess the validity of the model in both the time domain and frequency domain. Measured behavior in all evaluated conditions can be captured with the commonly used quasi-linear operator model for compensatory tracking, extended with two viewpoints of the previewed target. The derived model provides new insights into how human operators use preview information in tracking tasks.
In real-life manual control tasks, human controllers are often required to follow a visible and predictable reference signal, enabling them to use feedforward control actions in conjunction with feedback actions that compensate for errors. Little is known about human control behavior in these situations. This paper investigates how humans adapt their feedforward control dynamics to the controlled element dynamics in a combined ramp-tracking and disturbance-rejection task. A human-in-the-loop experiment is performed with a pursuit display and vehicle-like controlled elements, ranging from a single integrator through second-order systems with a break frequency at either 3, 2, or 1 rad/s, to a double integrator. Because the potential benefits of feedforward control increase with steeper ramp segments in the target signal, three steepness levels are tested to investigate their possible effect on feedforward control with the various controlled elements. Analyses with four novel models of the operator, fitted to time-domain data, reveal feedforward control for all tested controlled elements and both (nonzero) tested levels of ramp steepness. For the range of controlled element dynamics investigated, it is found that humans adapt to these dynamics in their feedforward response, with a close to perfect inversion of the controlled element dynamics. No significant effects of ramp steepness on the feedforward model parameters are found.
In continuous manual control tasks, human controllers adapt their control strategy to the dynamics of the controlled element. This compensation for the controlled-element dynamics is performed around the pilot-vehicle system crossover frequency, in order to obtain satisfactory performance of the combined pilot-vehicle system, but is also seen to extend to frequencies well above crossover. For a controlled element representing the linearized pitch dynamics of a small conventional jet aircraft, an extension to the models for pilot equalization described in the literature was found to be needed for the modeling of the adopted pilot equalization dynamics over a wide frequency range. Measured pilot describing functions revealed that pilots use a combination of low-frequency lag and high-frequency lead equalization to compensate for the characteristics of these typical aircraft pitch dynamics around the short-period mode. An additional high-frequency lead term in the pilot equalization transfer function was found to allow for the modeling of these adopted equalization dynamics over a wide frequency range, thereby also yielding a significant increase in the percentage of measured control inputs that is explained by the pilot model. Furthermore, for this controlled element the extended model for the equalization dynamics was found to be important for the interpretation of the changes in pilot control behavior that occur due to the presence of physical motion feedback.
In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.
This paper investigates the crossover-regression phenomenon in compensatory manual-control tasks. The adjustment, between-subject variation, and accuracy of linear human-operator models are analyzed in detail. A theoretical investigation into closed-loop error minimization will be presented. Our main hypothesis was that crossover regression is caused by an operator's inability to sufficiently decrease the time delays required to limit forcing-function resonance. To test the hypothesis and explore the use of linear-operator models in regressed conditions, an experiment very similar to McRuer's landmark 1965 experiment was conducted. A comparison between regressive and nonregressive conditions revealed that crossover regression is indeed a strategy to reduce forcing-function resonance. The bandwidth of the forcing-function signal at which participants regressed their crossover frequency was found to vary considerably between participants. In regressed conditions, the between-subject variability in frequency-domain performance increased. Additionally, the operator control behavior became increasingly nonlinear, resulting in larger uncertainties and a higher between-subject variability in the linear-model parameter estimates.
This paper presents a new method for estimating the parameters of multichannel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-in-the-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics are obtained. Multiple simulations with increasing levels of pilot remnant are performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that the bias in the parameter estimates is only substantial for very high levels of pilot remnant. Some adjustments to the maximum likelihood method are proposed to reduce this bias.
The research presented in this paper focuses on the design of a driver support system for the manual longitudinal control of a car during car-following. The aim of the design was to develop a system that would cooperate with the driver in comfortably maintaining (safe) separation with a lead vehicle. Three important design issues for a haptic gas pedal feedback system can be distinguished: 1) quantification of intervehicle separation parameters; 2) the type of haptic feedback; and 3) the relation between haptic feedback and intervehicle separation. Because of the inverse relationship between time-to-contact (TTC) and time-headway (THW)-the smaller the THW, the more important the avoidance of high TTC-THW should act as an amplifier for the haptic gas pedal feedback based on TTC. Using gas pedal stiffness feedback is expected to better facilitate the manual control of intervehicle separation changes, quantified by THW and TTC, because stiffness feedback allows perception of force and force-slope changes. The force changes inform drivers of instantaneous changes in the environment. Force-slope changes prevent drivers from input to the car that would continue to reduce the following gap in situations where this would be undesirable. A review of fixed-base simulator and field tests confirms that haptic gas pedal feedback improves driver vigilance during car-following without increasing the workload.
The experiments showed that the possible adaptation range in human impedance control is extremely large when controlling a steering wheel: at low frequencies a factor of a 1000 could be distinguished between giving way to forces and resisting them. This adaptability confirms the underlying hypothesis of this chapter that it is difficult to design shared control forces if the neuromuscular impedance is not known. Even if the system's goals and controller completely match the human goals and control strategy, when trial-and-error tuning has tuned the forces to be optimal for a slightly `resist forces' task (somewhere in between relaxed state and maximally resisting state) the feedback forces will be too large, and the human will have to resist them sometimes, or be satisfied with a different trajectory. Moreover, the experimental results strengthen the evidence found in other studies (Abbink, 2006; Abbink, 2007), which suggest that minimizing force errors (i.e., giving way to external forces) is not only possible, but also useful when interacting with guidance forces from shared
In the manual control of a dynamic system, the human controller (HC) is often
required to follow a visible and predictable reference path. Using the predictable aspect of
a reference signal, through applying feedforward control, the HC can significantly improve
performance as compared to a purely feedback control strategy. A proper definition of a signal’s
predictability, however, is never given in literature. This paper investigates the predictability of
a sum-of-sinusoids target signal, as a function of the number of sinusoid components and the
fact whether the sinusoid frequencies are harmonic, or not. A human-in-the-loop experiment
was done, with target signals varying for these two signal characteristics. A combined feedback-
feedforward HC model was identified and parameters were estimated. It was found that for
all experimental conditions, subjects used a feedforward strategy. Results further showed that
subjects were able to perform better for harmonic signals as compared to non-harmonic signals,
for signals with roughly the same frequency content.
Realistic manual control tasks typically involve predictable target signals and random disturbances. The human controller (HC) is hypothesized to use a feedforward control strategy for target-following, in addition to feedback control for disturbance-rejection. Little is known about human feedforward control, partly because common system identification methods have difficulty in identifying whether, and (if so) how, the HC applies a feedforward strategy. In this paper, an identification procedure is presented that aims at an objective model selection for identifying the human feedforward response, using linear time-invariant autoregressive with exogenous input models. A new model selection criterion is proposed to decide on the model order (number of parameters) and the presence of feedforward in addition to feedback. For a range of typical control tasks, it is shown by means of Monte Carlo computer simulations that the classical Bayesian information criterion (BIC) leads to selecting models that contain a feedforward path from data generated by a pure feedback model: "false-positive" feedforward detection. To eliminate these false-positives, the modified BIC includes an additional penalty on model complexity. The appropriate weighting is found through computer simulations with a hypothesized HC model prior to performing a tracking experiment. Experimental human-in-the-loop data will be considered in future work. With appropriate weighting, the method correctly identifies the HC dynamics in a wide range of control tasks, without false-positive results.
During the past several years, an analytical theory of manual control of vehicles has been in development and has emerged as a useful engineering tool for the explanation of past test results and prediction of new phenomena. An essential feature of this theory is the use of quasi-linear analytical models for the human pilot wherein the models' form and parameters are adapted to the task variables involved in the particular pilot-vehicle situation. This paper summarizes the current state of these models, and includes background on the nature of the models; experimental data and equations of describing function models for compensatory, pursuit, periodic, and multiloop control situations; the effects of task variables on some of the model parameters; some data on “remnant”; and the relationship of handling qualities ratings to the model parameters. Copyright © 1967 by The Institute of Electrical and Electronics Engineers, Inc.
This paper investigates the modeling of human manual control behavior for pursuit tracking tasks in which target forcing functions consisting of multiple ramp-like changes in target attitude are used. Due to the use of a pursuit display and the predictability of such forcing function signals, it can be anticipated that a pursuit or precognitive control strategy, consisting of open-loop feedforward control inputs in response to the predictable reference signal, is applied by the human operator. If combined with an additional disturbance on the controlled element, a control task results that is similar to performing a commanded turn entry/exit or altitude capture in turbulence. It is as of yet uncertain if such pursuit or precognitive control is indeed used during such a control task, and to what extent a quasi-random disturbance would suppress pursuit/precognitive control strategies. A human-in-the-loop evaluation of the combined ramp-following and disturbance-rejection task was performed to gather data for the modeling of human manual control behavior. It is found that despite the anticipated pursuit and precognitive control inputs, classical compensatory models of human manual control dynamics are highly capable of describing human dynamics for these specific control tasks. Measured control inputs, however, are found to correspond well with proposed models for open-loop feedforward operations as well, suggesting future evaluation of a model of human behavior that combines, or switches between, error-reducing compensatory and open-loop feedforward operations. © 2010 by Delft University of Technology. Published by the American Institute of Aeronautics and Astronautics, Inc.
The introduction of information technology based on digital computers for the design of man-machine interface systems has led to a requirement for consistent models of human performance in routine task environments and during unfamiliar task conditions. A discussion is presented of the requirement for different types of models for representing performance at the skill-, rule-, and knowledge-based levels, together with a review of the different levels in terms of signals, signs, and symbols. Particular attention is paid to the different possible ways of representing system properties which underlie knowledge-based performance and which can be characterised at several levels of abstraction-from the representation of physical form, through functional representation, to representation in terms of intention or purpose. Furthermore, the role of qualitative and quantitative models in the design and evaluation of interface systems is mentioned, and the need to consider such distinctions carefully is discussed.
The primary purpose of the experimental series reported here is to investigate, on a preliminary and exploratory basis, human operator performance differences between pursuit and compensatory displays. For each display type a wide range of forcing function bandwidths and controlled element dynamics was used. The effect of the additional information provided by separately displaying both forcing function and controlled element output (pursuit) rather than their difference (compensatory) was evaluated using the mean-squared error and a quantity called the 'effective open-loop describing function' (Y beta). As a prelude to the new data, past pursuit/compensatory tracking results are reviewed, and then a tie-in is made between these and the current series.
"The experimental literature on responses to acceleration of target motion was reviewed. One significant observation was that smoothly accelerated motion is generally responded to as if the velocity were constant. Suggestions were made of a basic approach toward obtaining thresholds of acceleration. Examples of studies on constant velocity motion were included in order to develop a systematic graphic method of describing experiments on motion. The phenomenon of velocity constancy of a single moving target was identified and generalized. 21 references. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Motor skills development is explained in terms of perceptual organization. As skills develop, Successive Organizations of Perception (SOP) enable the operator to take increasing advantage of the redundancy or predictability of his input signals. A servomechanism model is presented to describe how the predictability of input signals could be made more apparent to the human operator by the use of appropriate displays. The hypothesis is advanced that this external organization of input signals achieved by displays is a model for the effective SOP achieved as skill is acquired. Experimental evidence to support this SOP model is presented from the literature on manual tracking. Implications for future research are discussed.
The sections in this article are1The Problem2Background and Literature3Outline4Displaying the Basic Ideas: Arx Models and the Linear Least Squares Method5Model Structures I: Linear Models6Model Structures Ii: Nonlinear Black-Box Models7General Parameter Estimation Techniques8Special Estimation Techniques for Linear Black-Box Models9Data Quality10Model Validation and Model Selection11Back to Data: The Practical Side of Identification
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Human operator model based on successive organization of perception theory of skill and on optimal signal prediction
A pursuit tracking task was carried out to investigate the effects of combinations of sine waves on the development of precognitive mode, which is defined as open-loop mode with little feedback. Subjects were asked to track the targets, which contained two or three sine waves as components, and then to reproduce the target motion after the target had been removed. Frequency characteristics of tracking revealed the superiority of the faster component over the slower components in terms of both amplitude ratio and tracking lag. Subjects' reproduction after removing the target demonstrated that in general, subjects could learn and memorize also the slower component motion, which had yielded inferior performance during the tracking period. These results are discussed in terms of a model based on successive organization of perception.
Recently, both high quality physiological data and human-operator-describing function data of low variability and large dynamic range have become available. These data lead to control engineering descriptions for neuromuscular actuation systems that are compatible with the available data and that provide insight into the overall human control structure (e.g., the types of feedback systems used for various inputs). In this paper, some of these physiological and human-operator data are briefly reviewed, and a simple neuromuscular actuation system model is presented. The physiological data of interest include recent anatomical and physiological data for the muscle spindle and input-output studies of the muscle. These data indicate that simple linear models can describe the basic behavior of these two elements in tracking tasks. This paper contains two key developments: 1) the variation in system parameters as a function of average muscle tension or operating point; and 2) the role of the muscle spindle both as an equalization element and in its effects on muscle tone or average tension. The simplest neuromuscular model suggested by and compatible with the data is one in which muscle spindles provide four functions in one entity: 1) the feedback of limb position; 2) lead/lag series equalization; 3) the source of at least one command signal to the system; and 4) a signal for adjustment of the spindle gain, equalization, and steady-state spindle output which produces the average muscle tension.
A study of sine-wave tracking is reported which illustrates the extent to which the predictability of the input and of the control device dynamics can be utilized with extended practice. Analysis of the error power spectra establishes the presence of a stable source of noise power in the operator's output that has implications for deriving models of manual tracking performance.
Human pilot dynamics in compensatory systems: Theory, models, and experiments with controlled element and forcing function variations
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- E S Krendel
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