Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry
summary
Claim: expert mobile typists intentionally slow movement to avoid time-consuming deletions. Empirical method uses a mini QWERTY pointing task with a simulated Delete penalty, 20 participants, ~1,200 training and 4,300 test trials each. Result: costly-error condition yields 2.4 WPM slower estimated typing on Android mini QWERTY; a risk-aware model tightens existing upper bounds by 3.29 WPM and shows risk aversion accounts for 65.98% explained of the gap between prior models and observed expert speeds.
AI-written summary · about
chapters (13)
- 0:00Introduction to expert text entry
- 1:14Costly typing errors impact
- 2:39Simulating text entry tasks
- 4:03Measured movement time changes
- 5:51Limitations of existing models
- 6:11New expert text entry model
- 8:25Estimating movement times
- 9:06Computing upper bounds
- 9:29Empirical validation study
- 11:57Risk aversion explains differences
- 12:38Implications for keyboard design
- 13:22Future research directions
- 18:08Q&A and discussion
related talks
23mIJQwerty: What Difference Does One Key Change Make? Gesture Typing Keyboard Optimization Bounded ...
15mA Cost–Benefit Study of Text Entry Suggestion Interaction
21mDualKey: Miniature Screen Text Entry via Finger Identification
20mTipText: Eyes-Free Text Entry on a Fingertip Keyboard
18mImproving Virtual Keyboards When All Finger Positions Are Known
18mQWERTY or DVORAK? Debunking the Keyboard Layout Myths
08mTypeBoard: Identifying Unintentional Touch on Pressure-Sensitive Touchscreen Keyboards
21mThe Impact of Word, Multiple Word, and Sentence Input on Virtual Keyboard Decoding Performance
