Touchscreen Typing As Optimal Supervisory Control

In Proceedings of the ACM Conference on Human Factors in Computing Systems, CHI 2021.
A computational model simulates human like typing
  • Typing on touchscreen is hard, because both finger movements and proofreading of possible errors need vision. Yet, humans are able to divide attention efficiently.
  • Our computational model discovers ways to efficiently move eyes and fingers while typing, and as a result is able to simulate human-like typing.
  • Humans are fast at learning new finger and eye movement strategies as conditions of typing change (e.g., when using intelligent error correction). Our model simulates similar ability to adapt to changing conditions.
  • The model can be used to improve touchscreen keyboard usability and design intelligent text entry solutions.

Traditionally, touchscreen typing has been studied in terms of motor performance. However, recent research has exposed a decisive role of visual attention being shared between the keyboard and the text area. Strategies for this are known to adapt to the task, design, and user. In this paper, we propose a unifying account of touchscreen typing, regarding it as optimal supervisory control. Under this theory, rules for controlling visuo-motor resources are learned via exploration in pursuit of maximal typing performance. The paper outlines the control problem and explains how visual and motor limitations affect it. We then present a model, implemented via reinforcement learning, that simulates co-ordination of eye and finger movements. Comparison with human data affirms that the model creates realistic finger- and eye-movement patterns and shows human-like adaptation. We demonstrate the model's utility for interface development in evaluating touchscreen keyboard designs.


Visualisation of model behaviour typing a sentence under three conditions:

  1. Model types the sentence without errors.
  2. Model types the same sentence but with some errors made, detected and corrected.
  3. The keyboard has semi-reliable error correction, and the model has learned to use it efficiently.

(a) No errors made

(b) Errors made

(c) With auto-correction

Model Architecture & Performance

The model of touchscreen typing is formulated as an optimal supervisory control. It is composed of four distinct agents: supervisor, pointing, vision, and proofreading.


Selected typing-performance metrics: aggregate values for the human subjects and the simulations, the absolute difference, and difference in the SD of the variable in the human data is shown below. We consider a prediction good (green shading) if it falls within the range of the human data and is within one SD of the human mean and we deem the values still acceptable (realistic but outliers relative to the humans, in red) if within three SDs from the human mean. Huamn data can be found here

Metric Human
human value
human value
Mean diff. Diff. in SD
IKI (ms) 380.94 311.35 514.25 50.95 398.85 17.92 0.35
WPM 27.19 19.12 33.30 3.61 25.22 1.97 0.55
Chunk length 3.98 3.44 5.13 0.41 3.90 0.08 0.20
Backspaces 2.61 0.35 8.80 1.81 1.49 1.13 0.62
Immediate backspacing 0.40 0.00 1.05 0.26 0.31 0.09 0.35
Delayed backspacing 0.63 0.10 2.15 0.47 0.47 0.17 0.35
Fixation count 24.04 17.75 36.38 4.56 23.21 0.83 0.18
Gaze shifts 3.91 1.19 8.69 1.50 4.16 0.25 0.17
Gaze keyboard time ratio 0.70 0.36 0.87 0.14 0.87 0.18 1.31
Finger travel distance (cm) 25.29 20.81 27.64 1.33 22.09 3.20 2.41

All model code and data are open for anyone to use.

Please contact Jussi P.P. Jokinen for further information on data or the model code.

Press Releases

PDF, 1,66 MB
Jokinen, J. P. P., Acharya, A., Uzair, M., Jiang, X., & Oulasvirta, A., 2021. Touchscreen Typing As Optimal Supervisory Control. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI ’21).

title={Touchscreen Typing as Optimal Supervisory Control},
author={Jokinen, Jussi P P and Acharya, Aditya and Uzair, Mohammad and Jiang, Xinhui and Oulasvirta, Antti},
booktitle={Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21)},
publisher = {ACM},
doi = {},
keywords = {touchscreen typing, computational modelling, rational adaptation}
Related Publications
  • Jiang, X., Li, Y., Jokinen, J. P. P., Hirvola, V., Oulasvirta, A., & Ren, X. 2020.
    How We Type: Eye and Finger Movement Strategies in Mobile Typing.
    In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI ’20).
    Project Page

  • Jokinen, J. P., Wang, Z., Sarcar, S., Oulasvirta, A., & Ren, X. 2020.
    Adaptive feature guidance: Modelling visual search with graphical layouts.
    International Journal of Human-Computer Studies (IJHCS ’20).

  • Gebhardt, C., Oulasvirta, A., & Hilliges, O. 2020.
    Hierarchical Reinforcement Learning Explains Task Interleaving Behavior.
    Computational Brain & Behavior (CB&B '20) .

  • Jokinen, J. P., Kujala, T., & Oulasvirta, A. 2020.
    Multitasking in driving as optimal adaptation under uncertainty.
    Human Factors: The Journal of the Human Factors and Ergonomics Society (HFES '20).

  • Palin, K., Feit, A., Kim, S., Kristensson, P., O., & Oulasvirta, A. 2019
    How do People Type on Mobile Devices? Observations from a Study with 37,000 Volunteers.
    In Proceedings of 21st International Conference on Human-Computer Interaction with Mobile Devices and Services, MobileHCI 2019.
    Project Page

  • Dhakal, V., Feit, A., Kristensson, P., O., & Oulasvirta, A. 2018.
    Observations on Typing from 136 Million Keystrokes.
    In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18).
    Project Page

  • Jokinen, J. P., Sarcar, S., Oulasvirta, A., Silpasuwanchai, C., Wang, Z., & Ren, X. 2017.
    Modelling learning of new keyboard layouts.
    In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI ’17).
    Project Page

  • Feit, A., Weir, D., Oulasvirta, A. 2016.
    How We Type: Movement Strategies and Performance in Everyday Typing.
    In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16).
    Project Page

Frequently Asked Questions

Q: Can the model simulate any keyboard layout, or just Qwerty?
A: As long as the keyboard contains all necessary keys for typing the sentences in the input corpus, the model learns to type with any layout. So, it can be used to predict differences between typing on a Qwerty and on a Dvorak, for instance.

Q: People type with different speed. Can the model account for individual differences?
A: The model's specification includes parameters that can be adjusted to simulate individual differences. In our evaluation, the model's parameters were based on average values from literature, so it typed like an average typist. But, adjusting the parameter for finger's accuracy, for instance, can be used to simulate how a person with essential tremor would type. One of the main benefits of the model is that it automatically adjusts its behaviour as a consequence to changes in such circumnstances.

Q: Can the model account for learning of new layouts and more generally for the user's knowledge of the current layout?
A: Presently, the model assumes that the simulated user is very familiar with the layout, meaning that the locations of the keys are known. In the future, the model can be extended to account for learning of keyboard layouts. This would allow us to simulate, for instance, how changing a layout from Qwerty to Dvorak would impact a user who is familiar with the former but not the latter.

Q: Can the model type in any language?
A: Currently the model is trained using the finnish keyboard layout and Finnish language corpus. However, this is not a restriction for the model, which can be trained with any languege corpus (and optionally the associated language-specific keyboard).


For questions and further information, please contact:

Jussi P.P. Jokinen

jussi.jokinen (at)

+358 45 196 1429

This research has been supported by the Academy of Finland projects BAD and Human Automata, and Finnish Center for AI (FCAI).