How should fitts' law be applied to human-computer interaction?

Gillan, Douglas J.; Holden, Kritina; Adam, Susan C.; Rudisill, Marianne; Magee, Laura

doi:10.1016/0953-5438(92)90019-c

Cited by 24 publications

(13 citation statements)

References 13 publications

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“…However, RT and MT were affected by variables not ad- dressed by Fitts' Law, such as the angle between the target and start button, whether the target was on the right (ipsilateral) or left (contralateral), and the fidelity of the virtual hand. Some people have tried to generalize Fitts' Law (which was developed for 1D movement) to tasks involving two and even three dimensions (e.g., MacKenzie & Buxton, 1992;Sallnäs & Zhai, 2003); however, others have argued that additional characteristics of the task besides Fitts' original variables of target size and distance must be considered (Gillan, Holden, & Adam, 1992;Whisenand & Emurian, 1996).…”

Section: Discussionmentioning

confidence: 99%

Effect of Variations in Sensory Feedback on Performance in a Virtual Reaching Task

Durlach

Fowlkes

Metevier

2005

Presence: Teleoperators & Virtual Environments

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An experiment was conducted to investigate whether manipulation of the sensory cues provided in a virtual-reality context would affect performance of a reaching task and its associated phenomenology. Performance was measured by speed (the time taken to reach out and touch a virtually presented visual target) and accuracy (the distance of the fingertip from the center of the virtual target). Phenomenological experience was measured via questionnaires. The cues manipulated were the visual realism of the virtual hand (fidelity), whether the virtual fingertip was seen to penetrate the virtual target or not (constraint), and whether the feedback given on contact with the virtual target was tactile or auditory (feedback). We found that better hand fidelity speeded movement, increased presence, and reduced disorientation. In contrast, the constraint manipulation affected touch accuracy and disorientation. Tactile feedback enhanced the sense of presence and reduced disorientation, compared with auditory feedback.

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Section: Discussionmentioning

confidence: 99%

Effect of Variations in Sensory Feedback on Performance in a Virtual Reaching Task

Durlach

Fowlkes

Metevier

2005

Presence: Teleoperators & Virtual Environments

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“…There are conditions for which additional factors have to be accounted for, including eye movements (Chi and Lin, 1997) and ballistic arm movements (Hoffman, 1991). For the past 30 years, issues of computer interface design have driven much interest in Fitts’ law, helping to improve pointing time through improved screen layout and menu design (Gillan et al, 1992). More recently, it has been shown that Fitts’ law accurately describes the MTs of a one dimensional cursor in an electroencephalographically (EEG) driven brain–machine interface (BMI), both in normal subjects and in patients with amyotrophic lateral sclerosis and spinal muscular atrophy (Felton et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Cortical Correlates of Fitts’ Law

Ifft¹,

Lebedev²,

Nicolelis³

2011

Front. Integr. Neurosci.

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Fitts’ law describes the fundamental trade-off between movement accuracy and speed: it states that the duration of reaching movements is a function of target size (TS) and distance. While Fitts’ law has been extensively studied in ergonomics and has guided the design of human–computer interfaces, there have been few studies on its neuronal correlates. To elucidate sensorimotor cortical activity underlying Fitts’ law, we implanted two monkeys with multielectrode arrays in the primary motor (M1) and primary somatosensory (S1) cortices. The monkeys performed reaches with a joystick-controlled cursor toward targets of different size. The reaction time (RT), movement time, and movement velocity changed with TS, and M1 and S1 activity reflected these changes. Moreover, modifications of cortical activity could not be explained by changes of movement parameters alone, but required TS as an additional parameter. Neuronal representation of TS was especially prominent during the early RT period where it influenced the slope of the firing rate rise preceding movement initiation. During the movement period, cortical activity was correlated with movement velocity. Neural decoders were applied to simultaneously decode TS and motor parameters from cortical modulations. We suggest that sensorimotor cortex activity reflects the characteristics of both the movement and the target. Classifiers that extract these parameters from cortical ensembles could improve neuroprosthetic control.

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“…Recent studies have found that dragging is slower and more error prone than pointing for the tasks of text selection [1], simple target acquisition [2] and hierarchical menu selection [3]. The input devices studied include the mouse [1,2,3], trackball and stylus [2].…”

Section: Introductionmentioning

confidence: 99%

Studying the movement of high-tech Rodentia

Cohen

Meyer

Nilsen

1993

INTERACT '93 and CHI '93 Conference Companion on Human Factors in Computing Systems - CHI '93

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This study compares seven input devices (mouse, touchsereen, two trackba.lls, mousepen, touchp@ and joystick) performing a star tracing task. Along with the device comparisons, the diffemtce between moving with the selector button pressed (dragging) or with the button released (pointing) is examined. Recent work has found that dragging is slower and more error prone than pointing when using a mouse, stylus or trackball [1~,3]. In the present study, 28 subjects used all seven input deviees for boti dragging and pointing tasks. Highly significant device differences were found for both speed and accuracy (p's <.001). The touchscreen and mouse WE the~t devius and the joystick and touchpad were the worst. The fastest devices also produced the fewest errors. l%e main effect for the button position was also significant @'s c.005) with dragging being slower and more error-prone than pointing. However, there was a significant interaction between input device and button position. For one of the devices, the mousepen, dragging was actually faster and less error prone than pointing. What is different about the mousepen? Some possibilities are considered along with how these results can be applied to the design of input devices and interaction techniques.

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How should fitts' law be applied to human-computer interaction?

Cited by 24 publications

References 13 publications

Effect of Variations in Sensory Feedback on Performance in a Virtual Reaching Task

Effect of Variations in Sensory Feedback on Performance in a Virtual Reaching Task

Cortical Correlates of Fitts’ Law

Studying the movement of high-tech Rodentia

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