CS 544 Human Abilities

1. CS 544Human Abilities Human Motor Capabilities Acknowledgement: Some of the material in these lectures is based on material prepared for similar courses by Saul Greenberg (University of Calgary), Ravin Balakrishnan (University of Toronto), James Landay (University of California at Berkeley), monica schraefel (University of Toronto), and Colin Ware (University of New Hampshire). Used with the permission of the respective original authors.

2. W D Example: Pointing Device Evaluation • Real task: interacting with GUI’s • pointing is fundamental • Experimental task: target acquisition • abstract, elementary, essential

3. Index of Difficulty (ID ) Index of Performance (IP ) = ID/MT (bits/s) W Bandwidth Throughput D Fitts’ Law (Paul Fitts, 1954) Task difficulty is analogous to information - execution interpreted as human rate of information processing

4. MT (secs) * * * * * * * * different way to calculate IP * * * * * * * * * * b = slope IP = 1/b * * * * * * * * * * * * a ID (bits) log2(D/W + 1)

5. 50 years of data Reference: MacKenzie, I. Fitts’ Law as a research and design tool in human computer interaction. Human Computer Interaction, 1992, Vol. 7, pp. 91-139

6. What does Fitts’ law really model? Target Width Target Width Velocity (c) (a) (b) Distance

7. Power law of Practice • task time on nth trial follows a power law • Tn = T1 n-a, where a = .4 (empirically determined) • i.e., you get faster the more times you do it! • applies to skilled behavior (sensory & motor) • does not apply to knowledge acquisition or quality

8. Hick’s law • Reaction time T = a + blog2(n+1) • Where n is the number of choices • a, b empirically determined constants • log2(n+1) represents amount of information processed by human operator (in Bits) • Example: a telephone switch panel consisting of 10 buttons, any one of which may light up, prompting the operator to press the lit button. • Unequal probabilities:

9. Pop-up Linear Menu Pop-up Pie Menu Today Sunday Monday Tuesday Wednesday Thursday Friday Saturday Using these law’s to predict performance Which will be faster on average? • pie menu (bigger targets & less distance)?

10. Beyond pointing: Trajectory based tasks

11. D W D/2 D/2 D/N D/N D/N D W From targets to tunnels… • 2 goals passing • 3 goals passing • N+1 goals passing •  goals passing

12. Fixed width tunnel Narrowing tunnel W1 W(x) W2 dx General Steering Law W(s) ds D c W Steering Law (Accot, 1997)“Beyond Fitts’ Law: Models for trajectory based HCI tasks.”Proceedings of ACM CHI 1997 Conference

13. Some results (from Accot, 1997)

14. Readings • MacKenzie, I. S. (1992). Movement time prediction in human-computer interfaces. (Reprinted in BGBG 483-493).