On a unifying noise-velocity relationship and information transmission in human-machine systems

Chan, Robin; Childress, Dudley S.

doi:10.1109/21.59975

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…Chan and Childress (1990) have proposed a relationship that relates the variance of the human-machine noise to the mean square velocity of the human-machine output. Furthermore, they showed that this noise-velocity relationship is verified for some human-machine models (McReur's [1980] crossover model, Elkind's human-machine model [Elkind & Forgie 1959], and Fitts' law), and that it can be considered as a more fundamental human-machine behavior property.…”

Section: The Unifying Noise/velocity Relationship Of Chanmentioning

confidence: 99%

Distance versus position information in the control of aiming movements

Wieringen¹,

Beek²

1997

Behav Brain Sci

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Abstract:This target article presents a critical survey of the scientific literature dealing with the speed/accuracy trade-offs in rapid-aimed movements. It highlights the numerous mathematical and theoretical interpretations that have been proposed in recent decades. Although the variety of points of view reflects the richness of the field and the high degree of interest that such basic phenomena attract in the understanding of human movements, it calls into question the ability of 'many models to explain the basic observations consistently reported in the field. This target article summarizes the kinematic theory of rapid human movements, proposed recently by R. Plamondon (1993b; 1993c;, and analyzes its predictions in the context of speed/accuracy trade-offs. Data from human movement literature are reanalyzed and reinterpreted in the context of the new theory. It is shown that the various aspects of speed/accuracy tradeoffs can be taken into account by considering the asymptotic behavior of a large number of coupled linear systems, from which a deltalognormal law can be derived to describe the velocity profile of an end-effector driven by a neuromuscular synergy. This law not only describes velocity profiles almost perfectly, it also predicts the kinematic properties of simple rapid movements and provides a consistent framework for the analysis of different types of speed/accuracy trade-offs using a quadratic (or power) law that emerges from the model.

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Section: The Unifying Noise/velocity Relationship Of Chanmentioning

confidence: 99%

Distance versus position information in the control of aiming movements

Wieringen¹,

Beek²

1997

Behav Brain Sci

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“…Strong fit of model has been empirically supported by many experiments (Douglas et al, 1999;Soukoreff and Mackenzie, 2004). The model was investigated from information theoretic view (Fitts, 1954;Mackenzie, 1989;Chan and Childress, 1990), from kinesthetic view and control theory (Connelly, 1984). Also, several studies have elaborated to come up with better index of difficulty (Fitts, 1954;Crossman, 1956;Welford et al, 1969).…”

Section: Introductionmentioning

confidence: 97%

Adjusting Fitts’ Paradigm for Small Touch-Sensitive Input Device with Large Group of Users

Kim

2012

Proceedings of the Human Factors and Ergonomics Society Annual

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Fitts’ paradigm is a classic theory to describe the relationship between movement time, distance and accuracy for rapid aimed movement. Generally speaking, it describes the Speed-Accuracy Tradeoff (SAT) along task condition as well as along users. A controlled factorial experiment is conducted for small touch-sensitive input device to observe SAT relationship within task condition and large number of users. Fitts’ paradigm did not successfully model the SAT relationship, due to ambiguous definition of accuracy index and lack of variables to reflect subject bias. Fitts’ paradigm was adjusted by revising accuracy index appropriate for direct finger input, and by introducing a trajectory variable to keep track of the subject bias. It raised the model fit (R2) from 0.523 to 0.833 for a model of task condition.

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“…Tracking studies have demonstrated that for both deterministic and stochastic inputs (e.g. colored gaussian noise), bandwidths of between 0.5 and 1 Hz result in a peak in the information transmission rate, and that at higher frequencies the input power is in a range that is too high for the operator to follow (Chan and Childress 1990;Elkind and Sprague 1961;McRuer 1980;Mesplay and Childress 1988). Although there are limits to the frequency response of the human neuromuscular system (Kearney and Hunter 1990), it is generally considered that the bandwidth of the human operator system reflects cognitive (computational) rather than input (sensing) or output (actuating) limitations on information transmission, because the latter are quite broad when compared with the bandwidth of the cognitive processing stage (Beatty 1975).…”

mentioning

confidence: 97%

“…At signal bandwidths of less than 1 Hz this does not result in any significant tracking error, but at higher bandwidths the delay can induce greater error and interact with system lags to produce instability (Wickens 1986). Second, a human operator is unable to respond when the rate of information transmission exceeds 4-8 bits/s (Chan and Childress 1990). Third, higher-order system dynamics impose reduced gains and increased phase lags that make a system harder to track (Lee and Han 1980).…”

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confidence: 99%

Influence of the mechanical properties of a manipulandum on human operator dynamics. II. Viscosity

Jones

Hunter

1993

Biol. Cybern.

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The influence of the viscosity of a manipulandum used by a human operator in a position-control pursuit-tracking task was examined. An active servo-system was used to set the viscosity of a manipulandum (motor) connected to the forearm to one of seven levels ranging in a geometric series from 12 to 800 N.s/m. During each condition the viscosity of the motor was held constant by a computer while subjects tracked, by moving their forearm in the sagittal plane, a visually presented target whose position changed randomly every 1.5 s for 255 s. Nonparametric and parametric impulse response functions were calculated between the input (target) and output (position) in each tracking condition. Nonparametric analyses revealed that subjects became sluggish at higher viscosities (above 200 N.s/m) and took longer to reach the target. A second-order low-pass transfer function was found to provide a very good description of tracking performance at each viscous level. The gain and damping parameter of this transfer function were not affected by the manipulandum's viscosity, whereas both the pure delay and natural frequency of the human operator system decreased systematically with increasing manipulandum viscosity. These findings suggest that over the range of viscosities studied, there is no speed-accuracy trade-off in terms of determining an optimal level of manipulandum viscosity for a human operator, and that a less viscous interface will result in faster performance.

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On a unifying noise-velocity relationship and information transmission in human-machine systems

Cited by 16 publications

References 22 publications

Distance versus position information in the control of aiming movements

Distance versus position information in the control of aiming movements

Adjusting Fitts’ Paradigm for Small Touch-Sensitive Input Device with Large Group of Users

Influence of the mechanical properties of a manipulandum on human operator dynamics. II. Viscosity

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