1. chapter 5 Models and theories 1

2. Cognitive modeling If we can build a model of how a user works, then we can predict how s/he will interact with the interface.  Goals: 1. Predict performance of design alternatives 2. Evaluate suitability of designs to support and enhance human abilities and limitations. 3. Generate design guidelines that enhance performance and overcome human limitations. 2

3. Cognitive modeling  components:  Model some aspects of user’s understanding, knowledge, intentions understanding, knowledge, intentions and processing  Vary in representation levels: high level plans and problem-solving to low level motor actions such as keypresses. 3

4. Cognitive models 1. Model human processor 2. GOMS 3. Production systems 4. grammars 4

5. Cognitive models 1. Model human processor:  Consider humans as information processing systems - Predicting performance - Not deciding how one would act - A “procedural” model  From Card, Moran, and Newell (1980’s) 5

6. Cognitive models 1. Model human processor:  Consists of memories and processors. 6

7. Cognitive models 1. Model human processor:  Components:  Set of memories and processors together.  Set of “principles of operation”.  Discrete, sequential model.  Each stage has timing characteristics. 7

8. Cognitive models 1. Model human processor:  Perceptual processor: Consists of sensors and associated buffer memories – Most important memories being visual image store and audio image store – Hold output of sensory system while it is being symbolically coded 8

9. Cognitive models 1. Model human processor:  Cognitive processor: Receives symbolically coded information from sensory image stores in its working memory Uses that with previously information in long- term memory to make decisions on how to respond 9

10. Cognitive models 1. Model human processor:  Motor processor: Controls movements of body Applying the MHP to a user interface:  a flashing cursor will indicate where the user is to enter data  a beep may sound when a mistake is made  a logical order to the inputs will be employed 10

11. Cognitive models 2. GOMS: Goals, Operators, Methods, Selection Rules. (Developed by Card, Moran and Newell ’83) Probably the most widely known and used technique in this family. 11

12. Cognitive models 2. GOMS:  Goals: - End state trying to achieve. - Then decompose into subgoals. 12

13. Cognitive models 2. GOMS:  Operators: - Basic actions available for performing a task (lowest level actions) - Examples: move mouse pointer, drag, press key, read dialog box, …  Methods: - Sequence of operators (procedures) for accomplishing a goal - Example: Select sentence - – Move mouse pointer to first word - – Depress button - – Drag to last word - – Release 13

14. Cognitive models 2. GOMS:  Selection rules: - Invoked when there is a choice of a method - GOMS attempts to predict which methods will be used - Example: Could cut sentence either by pulldown or by ctrl-x Limitations GOMS is not for: – Tasks where steps are not well understood – Inexperienced users 14

15. Cognitive models 2. GOMS: GOMS variants: GOMS is often combined with a keystroke level analysis – KLM - Keystroke level model – Analyze only observable behaviors such as keypresses, mouse movements – Low-level GOMS where method is given – Assumes error -free performance 15

16. Cognitive models 2. GOMS: Keystroke Level Model: It allows predictions to be made about how long it takes an expert user to perform a task.  KLM characteristics: operators: – K: keystroke, mouse button push – P: point with pointing device – D: move mouse to draw line – H: move hands to keyboard or mouse – M: mental preparation for an operation – R: system response time 16

17. Cognitive models 2. GOMS: Other GOMS variants: * NGOMSL (Kieras): – Very similar to GOMS – Goals expressed as noun-action pair eg., delete word – Same predictions as other methods – More sophisticated, incorporates learning, consistency – Handles expert. 17

18. Cognitive models 3. Production systems :  IF-THEN decision trees (Kieras & Polson ’85) – Cognitive Complexity Theory – Uses goal decomposition from GOMS and provides more predictive power – Goal-like hierarchy expressed using production rules if condition, then action Makes a generalized transition network  Very long series of decisions – Note: In practice, very similar to NGOMSL – NGOMSL model easier to develop – Production systems easier to program 18

19. Cognitive models 3. Grammars : To describe the interaction, a formalized set of productions rules (a language) can be assembled. “Grammar” defines what is a valid or correct sequence in the language. Reisner Reisner (1981) “Cognitive grammar” describes human- computer interaction in Backus-Naur Form (BNF) like linguistics. Used to determine the consistency of a system design. Task action grammar (TAG) 19

20. ( ٍ Summary)Cognitive models 20 1.Model Human Processor (MHP) 2.GOMS Basic model Keystroke-level models (KLM) NGOMSL 3.Production systems Cognitive Complexity Theory 4.Grammars Backus-Naur Form (BNF) Task action grammar (TAG)

21. lower levels models (Fitt’s law) 21 More physically-based – Human as biomechanical machine Basic idea: Movement time for a well-rehearsed selection task – Increases as the distance to the target increases – Decreases as the size of the target increases

22. lower levels models (Fitt’s law) 22 Time (in msec) = a + b log 2 (D/S+1) where a, b = constants (empirically derived) D = distance S = size ID is Index of Difficulty = log 2 (D/S+1) d s

23. lower levels models (Fitt’s law) 23 Same ID → Same Difficulty Target 1 Target 2 Time = a + b log 2 (D/S+1)

24. lower levels models (Fitt’s law) 24 Smaller ID → Easier Target 2 Target 1 Time = a + b log 2 (D/S+1)

25. lower levels models (Fitt’s law) 25 Larger ID → Harder Target 2 Target 1 Time = a + b log 2 (D/S+1)

26. lower levels models (Fitt’s law) 26 Microsoft Toolbars allow you to either keep or remove the labels under Toolbar buttons According to Fitts’ Law, which is more efficient?

27. lower levels models (Fitt’s law) 27 1. The label becomes part of the target. The target is therefore bigger. Bigger targets, all else being equal, can always be accessed faster, by Fitt's Law. 2. When labels are not used, the tool icons crowd together.

28. lower levels models (Fitt’s law) 28 1. The label becomes part of the target. The target is therefore bigger. Bigger targets, all else being equal, can always be accessed faster, by Fitt's Law. 2. When labels are not used, the tool icons crowd together.