Estimation of psychomotor delay from the Fitts’ law coefficients

Beamish, Dan; Bhatti, Shabana; Chubbs, C. Scott; MacKenzie, I. Scott; Wu, Jianhong; Jing, Zhujun

doi:10.1007/s00422-009-0336-3

Cited by 15 publications

(15 citation statements)

References 53 publications

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“…The speed-accuracy trade-off encapsulated in Fitts' law has been variously attributed to the need for visuomotor guidance for more accurate movement (Woodworth 1899); for one or more corrective submovements because of the inaccuracy of initial movements (Crossman andGoodeve 1963/1983;Keele 1968;Jagacinski et al 1980); to the amount of information that must be processed to achieve particular levels of accuracy (Fitts 1954;Fitts and Peterson 1964); to delays in visuomotor feedback (Beamish et al 2009); to minimization of endpoint positional variance (Harris and Wolpert 1998), and to submovement optimization (Meyer et al 1988). Fitts' law applies not only to linear hand or stylus movements (as in most of the studies above) but also to rotational movements (Jagacinski et al 1980;Meyer et al 1988) and to movements of a tool around obstacles (Jax et al 2007;Vaughan et al 2010).…”

Section: Introductionmentioning

confidence: 99%

The cost of moving with the left hand

2012

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Precise left-hand movements take longer than right-hand movements (for right-handers). To quantify how left-hand movements are affected by task difficulty and phase of movement control, we manipulated the difficulty of repetitive speeded aiming movements while participants used the left or right hand. We observed left-hand costs in both initial impulse and current control phases of movement. While left-hand cost during the initial impulse phase was small, left-hand cost during the current control phase varied from 10 to 60 ms, in direct proportion to the movement's difficulty as quantified by Fitts' law (0.77 < R² < 0.99, across three experiments). In particular, in comparison with a difficult task for the right hand (Fitts' ID(R) = 6.6), the left hand's task would have to be made easier by 0.5 bits (ID(L) = 6.1) to be performed as quickly. The left-hand cost may reflect the time required for callosal transfer of information between the left and right hemispheres during the current control phase of precision left-hand movements or reflect movement control differences in the current control phase of movement that are inherent to the hemispheres. Overall, the present results support multiphase models of movement generation, in which separate specialized processes contribute to the launching and completion of precision hand movements.

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Section: Introductionmentioning

confidence: 99%

The cost of moving with the left hand

2012

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“…Using the measurements available from over 25 previous Fitts's law style studies, they found feedback delays between 0-112 ms, generally below 60 ms. They also found that the nature of the VITE circuit imposes a limit on the performance of unidirectional movement [14]. When this limit is expressed as a Fitts's law Index of Difficulty (ID), it happens to be the typical range employed by previous experimenters.…”

Section: Models Of the Motor Systemmentioning

confidence: 99%

The Effects of Low Latency on Pointing and Steering Tasks

Friston

Karlstrm

Steed

2016

IEEE Trans. Visual. Comput. Graphics

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Abstract-Latency is detrimental to interactive systems, especially pseudo-physical systems that emulate real-world behaviour. It prevents users from making quick corrections to their movement, and causes their experience to deviate from their expectations. Latency is a result of the processing and transport delays inherent in current computer systems. As such, while a number of studies have hypothesized that any latency will have a degrading effect, few have been able to test this for latencies less than ∼50 ms. In this study we investigate the effects of latency on pointing and steering tasks. We design an apparatus with a latency lower than typical interactive systems, using it to perform interaction tasks based on Fitts's law and the Steering law. We find evidence that latency begins to affect performance at ∼16 ms, and that the effect is non-linear. Further, we find latency does not affect the various components of an aiming motion equally. We propose a three stage characterisation of pointing movements with each stage affected independently by latency. We suggest that understanding how users execute movement is essential for studying latency at low levels, as high level metrics such as total movement time may be misleading.

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“…To prove the decay condition (8), notice that along all trajectories of (4), we can use the triangle inequality to get…”

Section: Proofmentioning

confidence: 99%

“…Two key results are the Fitts Law, which was was first presented in [6] and explains an invariant in pointing performance for many human pointing interfaces and tasks, and the vector integration to endpoint (or VITE) model, which was first presented in [7] and reproduces the Fitts Law by describing pointing motions. Other papers have analyzed the dynamics of human pointing as described by the VITE model; see [8][9][10][11][12], with some having feedback delays and some not having delays. Our work [13] used dissipativity to design and study pointing systems, and it gave a preliminary analysis of the VITE model in the dissipativity framework using a switching controller.…”

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confidence: 99%

Stability and robustness analysis for human pointing motions with acceleration under feedback delays

Varnell

Malisoff

Zhang

2016

Int. J. Robust. Nonlinear Control

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Pointer acceleration is often used in computer mice and other interfaces to increase the range and speed of pointing motions without sacrificing precision during slow movements. However, the effects of pointer acceleration are not yet well understood. We use a system perspective and feedback control to analyze the effects of pointer acceleration. We use a new pointer acceleration model connected in feedback with the vector integration to endpoint model for pointing motions. When there are no feedback delays, we prove global asymptotic stability of the closed loop system for a general class of acceleration profiles. We also prove robustness under delays and perturbations by building Lyapunov-Krasovskii functionals for delay systems, and we find state performance bounds using robust forward invariance with maximal perturbation sets. The results are relevant to designing pointing interfaces, and our simulations illustrate the good performance of our control under realistic operating conditions.In this work, we use a systems perspective to approach the problem of analyzing the effects of pointer acceleration. We build a dynamic model of human pointing motion with pointing acceleration, and we analyze its performance and stability properties. Our results provide insights into the impact of pointer acceleration in pointer interfaces, and have the potential to benefit future designs of such interfaces.Human pointing interfaces have been studied extensively in human-computer interaction, neuroscience, physiology, and psychology; see [5]. Two key results are the Fitts Law, which was was first presented in [6] and explains an invariant in pointing performance for many human pointing interfaces and tasks, and the vector integration to endpoint (or VITE) model, which was first presented in [7] and reproduces the Fitts Law by describing pointing motions. Other papers have analyzed the dynamics of human pointing as described by the VITE model; see [8][9][10][11][12], with some having feedback delays and some not having delays. Our work [13] used dissipativity to design and study pointing systems, and it gave a preliminary analysis of the VITE model in the dissipativity framework using a switching controller. Our conference article [14] presented the new model of pointer acceleration that is used in this paper and analyzed its stability and performance properties when connected in feedback to the VITE model without any feedback delays or perturbations or robust forward invariance.Our approach is novel because, to the best of our knowledge, we are the first to use a systems perspective to study pointer acceleration. We use a new model that captures most existing implementations of pointer acceleration, and we show that when the VITE dynamics are connected in feedback to the pointer acceleration model, the interconnection is globally asymptotically stable, including in cases with feedback delays. We include a robustness analysis under perturbations, which can arise from discretization errors and inaccuracies in human perception a...

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Estimation of psychomotor delay from the Fitts’ law coefficients

Cited by 15 publications

References 53 publications

The cost of moving with the left hand

The cost of moving with the left hand

The Effects of Low Latency on Pointing and Steering Tasks

Stability and robustness analysis for human pointing motions with acceleration under feedback delays

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