The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Extending Fitts' law to two-dimensional tasks
Open accessAbstract
Fitts' law, a one-dimensional model of human movement, is commonly applied to two-dimensional target acquisition tasks on interactive computing systems. For rectangular targets, such as words, it is demonstrated that the model can break down and yield unrealistically low (even negative!) ratings for a task's index of difficulty (ID). The Shannon formulation is shown to partially correct this problem, since ID is always ≥ 0 bits. As well, two alternative interpretations “target width” are introduced that accommodate the two-dimensional nature of tasks. Results of an experiment are presented that show a significant improvement in the model's performance using the suggested changes.
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Citations
Cites
The information capacity of the human motor system in controlling the amplitude of movement.
Fitts, P. M. (1954) · Journal of Experimental Psychology · doi:10.1037/h0055392 · Cited by 6,466
Information capacity of discrete motor responses.
Fitts, P. M., & Peterson, J. R. (1964) · Journal of Experimental Psychology · doi:10.1037/h0045689 · Cited by 1,534
Comparison of Six Cursor Control Devices Based on Fitts' Law Models
Epps, B. W. (1986) · Proceedings of the Human Factors Society Annual Meeting · doi:10.1177/154193128603000403 · Cited by 147 · cited 2×
“Objects of interest are often “iconic” — represented on a two-dimensional CRT display and selected by a mouse, joystick, or trackball [e.g., 2, 3, 12].”
Background
Cited by
Fitts' Law as a Research and Design Tool in Human-Computer Interaction
MacKenzie, I. S. (1992) · Human–Computer Interaction · doi:10.1207/s15327051hci0701_3 · Cited by 1,637
Self-citationA high-performance neural prosthesis enabled by control algorithm design
Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., et al. (2012) · Nature Neuroscience · doi:10.1038/nn.3265 · Cited by 564
Automatically generating personalized user interfaces with Supple
Gajos, K. Z., Weld, D. S., & Wobbrock, J. O. (2010) · Artificial Intelligence · doi:10.1016/j.artint.2010.05.005 · Cited by 317
Evaluating Large Language Models in Generating Synthetic HCI Research Data: a Case Study
Hämäläinen, P., Tavast, M., & Kunnari, A. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580688 · Cited by 248
Gestural Control of Sound Synthesis
Wanderley, M. M., & Depalle, P. (2004) · Proceedings of the IEEE · doi:10.1109/jproc.2004.825882 · Cited by 196
A human motor behavior model for distal pointing tasks
Kopper, R., Bowman, D. A., Silva, M. G., & McMahan, R. P. (2010) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2010.05.001 · Cited by 178
Investigating selection and reading performance on a mobile phone while walking
Schildbach, B., & Rukzio, E. (2010) · Proceedings of the 12th international conference on Human computer interaction with mobile devices and services · doi:10.1145/1851600.1851619 · Cited by 154
Touch a Screen or Turn a Knob: Choosing the Best Device for the Job
Rogers, W. A., Fisk, A. D., McLaughlin, A. C., & Pak, R. (2005) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1518/0018720054679452 · Cited by 141
Predictive interaction using the delphian desktop
Asano, T., Sharlin, E., Kitamura, Y., Takashima, K., & Kishino, F. (2005) · Proceedings of the 18th annual ACM symposium on User interface software and technology · doi:10.1145/1095034.1095058 · Cited by 116
(2018) · Springer Series on Touch and Haptic Systems · doi:10.1007/978-3-319-58316-7 · Cited by 60
“Examples include models that capture the touch response of the piano action [9, 10] and feel of the drum head [11].”
BackgroundInfluentialAction selection in complex routinized sequential behaviors.
Ruh, N., Cooper, R. P., & Mareschal, D. (2010) · Journal of Experimental Psychology: Human Perception and Performance · doi:10.1037/a0017608 · Cited by 31
Design pattern TRABING: touchscreen-based input technique for people affected by intention tremor
Mertens, A., Jochems, N., Schlick, C. M., Dünnebacke, D., & Dornberg, J. H. (2010) · Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems · doi:10.1145/1822018.1822060 · Cited by 22
Huang, Y.-C., & Wu, F.-G. (2015) · International Journal of Industrial Ergonomics · doi:10.1016/j.ergon.2015.08.002 · Cited by 12
Touchscreen-based input technique for people with intention tremor
Mertens, A., Koch-Korfges, D., Jochems, N., & Schlick, C. M. (2010) · 3rd International Conference on Human System Interaction · doi:10.1109/hsi.2010.5514563 · Cited by 5
Joh, H., Lee, Y. J., & Oh, U. (2022) · Proceedings of the 19th International Web for All Conference · doi:10.1145/3493612.3520454 · Cited by 1
Metadata
- Authors
- MacKenzie, I. S., & Buxton, W.
- Venue
- Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '92
- Publisher
- ACM Press
- Published
- 1992
- Pages
- 219-226
- OpenAlex
- W2122297397
- Cited by
- 617
- References
- 3
- FWCI
- 53.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
