1 / 13

CS160 Discussion Section Fitts Law and KLM

CS160 Discussion Section Fitts Law and KLM. David Sun Sept 26 th 2007. Fitts Law (recap). A model for computing the time it takes to move the hand/pointer to a target. Formula: a + b log (D/S +1) D: distance to the target S: width of the target measured along the dimension of motion.

tyler-hill
Download Presentation

CS160 Discussion Section Fitts Law and KLM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. CS160 Discussion SectionFitts Law and KLM David Sun Sept 26th 2007

  2. Fitts Law (recap) • A model for computing the time it takes to move the hand/pointer to a target. • Formula: a + b log (D/S +1) • D: distance to the target • S: width of the target measured along the dimension of motion. • a, b: constants determined through regression.

  3. Example:

  4. More on Fitts Law • A number of formulations exist. We use the Shannon Formulation (by Mackenzie). • Slightly better fit to data • Keeps ID positive • Fits with underlying information theory • Index of difficulty (ID): log (D/S +1), measured in bits. • Index of performance (IP) : 1/b, measured in seconds/bit. • IP is independent of the particular target.

  5. More on Fitts Law • Limitations: • One dimensional model of human movement • But often applied to two dimensional target acquisition tasks. D D start start S S

  6. More on Fitts Law • The amplitude of the move and the width of the terminating region are measure along the same axis. start D D D start start S D start S

  7. Examples: D S start S D D start S start

  8. Fixes • Smaller-of: smaller of the two dimensions (since small side is more indicative of the task complexity). Computes a upper-bound. • Requires both dimensions and D • Simple but less accurate. • Calculate W’ through trig functions. • Requires everything Smaller-of needs + angle of motion.

  9. Keystroke Level Model • Describe the task using the following Operators • K: pressing a key or a pressing (or releasing) of a button • T(K) = 0.08~1.2 seconds (~0.2 avg) • P: pointing • T(P) = 1.1 seconds (without button presses) • H: homing (switching device • T(H) = 0.4 sec • D(n,L): drawing segmented lines • T(D) = 0.9n + 0.16L • M: mentally prepare • T(M) = 1.35s • R(t) : system repsonse time • T(R) = t

  10. KLM Heuristic Rules (Raskin) 0: Insert M • in front of all K • in front of all P’s selecting a command (not in front of P’s ending a command) 1: Remove M between fully anticipated operators • MPK  PK 2: if a string of MKs belong to a cognitive unit, delete all M’s except the first • 4564.23: MKMKMKMKMKMKMK  MKKKKKKK 3: if K is a redundant terminator, then delete M in front of it • [enter] [enter]: MKMK  MKK 4a: if K terminates a constant string (command name) delete the M in front of it • cd [enter]: MKKMK  MKKK 4b: if K terminates a variable string (parameter) keep the M in front of it • cd class [enter]: MKKKMKKKKMK  MKKKMKKKKKMK

  11. More on KLM • Basic underlying cognitive assumption: • Serial stage model of human information processing: one activity is done at a time until a task is complement. • No parallel activities, no interruptions and interleaving goals. • Others models: NGOMSL (overlapping human activities), CPM-GOMS (more rigor)

  12. Example: K: pressing a key or pressing and releasing a button = 0.2s P: pointing = 1.1s (without button press) H: Homing (switching device) = 0.4s M: Mentally prepare = 1.35s

  13. Example

More Related