Human Perception

1. Human Perception Christine Robson September 20, 2007

2. First Computer “bug”

3. Self Checkoutlove it or hate it?

4. too much of a good thing?

5. Another word about grading • We are not grading according to strict percentages • This class is qualitative not quantitative • Assignments are less structured then most CS classes • Most of the grade is on the final project • Overall, pleased with effort • Giving feedback on areas to improve

6. Today • Human Information Processing • Perception • Motor Skills • Memory • Decision Making • Attention • Vision • Modeling Human Actions • Fitts’s Law • GOMS • KLM

7. Stage Theory of Human Perception & Memory Sensory Image Store WorkingMemory Long Term Memory

8. Short-Term Sensory Store • Visual information store • Encoded as a physical image • Size approx 7-17 letters • Decay ~200ms (70-1000ms) • Auditory information store • Encoded as a physical sound • Size 4.4-6.2 letters • Decay ~1500ms (900-3500ms)

9. Preattentive Processing http://www.csc.ncsu.edu/faculty/healey/PP/index.html

10. Preattentive Processing http://www.csc.ncsu.edu/faculty/healey/PP/index.html

11. Preattentive Processing http://www.csc.ncsu.edu/faculty/healey/PP/index.html

12. Say the colors of these words aloud Cat Jacket Train Lunch Knife Road

13. Do it again… Orange Purple White Red Yellow Green

14. Read them in order… White Green Orange Yellow Purple Red

15. Perceptual Fusion • Two stimuli within the same PP cycle (perceptual processor cycle, approx 100ms) appear fused • Consequences: • 10 frames/second seems to be moving (20fps looks smooth) • Computer responses in less then 100ms appear instantaneous • i.e. That’s how this projector works

16. Stage Theory of Human Perception & Memory maintenance rehearsal Sensory Image Store WorkingMemory Long Term Memory elaboration decay decay,displacement decay? interference?

17. Working Memory • Small capacity: 7 +/- 2 chunks • A memory chunk is a small grouping of data eg 800 411 4664 is three chunks • Fast decay rate (~7 [5-226] sec) • Maintenance Rehearsal fends off delay • Interference causes faster delay

18. Long-term Memory (LTM) • Huge capacity • Little decay • Elaborative rehearsal moves chunks from working memory into LTM by making connections with other chunks

19. Recall • Who were the 7 dwarves in Snow White?

20. Recognition • Does that help? Grouchy Sneezy Smiley Sleepy Pop Grumpy Cheerful Dopey Bashful Wheezy Doc Lazy Happy Nifty Sleepy

21. Power Law of Practice • Task time on the nth trial: Tn = T1 n-a + cwhere a = 0.4 ; c is a limiting constant • You get faster the more times you do it! • Applies to skilled behavior • eg. Sensory & Motor • Not to knowledge acquisition or improving quality

22. Human Actions

23. Divided Attention • Multitasking • Attention is a resource that can be shared among different tasks simultaneously • Depends on • The structure of the tasks (similar tasks interfere, different tasks are easy to share) • modality, encoding, and components • Difficulty of the task

24. Choice Reaction Time • Reaction time is proportional to information content of stimulus • If the user has to make a choice, it takes much longer to respond • Double your number of stimuli, double your reaction time

25. Hick’s Law • Time it takes for a user to make a decision. • Given n equally probable choices, the average reaction time T required to choose among them: T = b log2(n + 1)

26. Information Clutter • We don’t even need Hick’s Law to see this is a bad idea…

27. Motor Processing • Pianist: up to 16 finger movements per second • You might faster then you speak • You certainly type faster then you click • Point of no return for muscle action

28. Fitts’s Law • Time T to move your hand to a target of size S at distance D away is T = RT + MT = a + b log (D/S +1) • Depends only on index of difficulty log (D/S +1) • Hand movement based on a series of micro-corrections D start S

29. Implications of Fitts’s Law • Which targets are easier to hit? Why? A B start start D C start start

30. Visualization of Fitts’s Law • Time to move for distances (1 to 10) and a widths (0.1 to 1.0): www.mindhacks.com/blog/moving/index.html

31. Toolbar Example • How can you make a simple change to improve this tool bar • Apply Fitts’s Law! • Targets at screen edge are easy to hit

32. GOMS • Describe the user behavior in terms of • Goals • i.e. edit manuscript, locate line • Operators • Elementary perceptual, motor, or cognative acts • Methods • Procedure for using operators to accomplish goals • Selection rules • Used if several methods are available for a given goal • Family of methods • KLM, CMN-GOMS, NGOMSL, CPM-GOMS

33. GOMS Example • Goal (the big picture) • Go from home to the airport • Methods (or subgoals?) • Take BART, taxi, airport shuttle • Operators • Go to BART station, wait for BART… • Selection rules • BART is cheaper, but I’m running late…

34. GOMS How-To: • Generate task description • Pick high-level user Goal • Write Methods for reaching Goal (may invoke sub-goals) • Write Methods for sub-goals • Iterate recursively until Operators are reached • Evaluate description of task • Apply results to UI • Iterate

35. GOMS Calculations • Execution time • Add up times from operators • Assume experts (have mastered tasks) • Assume error-free behavior • Very good rank ordering • Absolute accuracy (~10%-20%)

36. Using GOMS Analysis • Check that frequent goals can be achieved quickly • Making operator hierarchy is often the value • Functional coverage & consistency • Does UI contain needed functions? • Are similar tasks preformed similarly? • Operator Sequence • In what order are individual operations done?

37. 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

38. 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

39. Using KLM • Encode using all physical operators • K, M, P, H, D(n,l), R(t) • Apply Raskin’s KLM rules • Transform R followed by an M • If t ≤ T(M) : R(t)  R(0) • If T(M) < t : R(t)  R(t – T(M) ) • Compute the total time by adding each time cost

40. Applications of GOMS • Compare different UI designs • Profiling (time) • Building a help system? Why? • Modeling makes user tasks & goals explicit • Can suggest questions users will ask & the answers

41. What GOMS Can Model • Task must be goal-directed • Some activities are more goal-directed then others • Creative activities may not be as goal-directed • Task must be a routine cognitive skill • As opposed to problem solving • Good for machine operators • Serial and parallel tasks (CMP-GOMS)

42. Advantages of GOMS • Gives qualitative and quantitative measures • Model explains the results • Less work then a user study- no users! • Easy to modify when UI is revised

43. Disadvantages of GOMS • Not as easy as other evaluation methods • Heuristic evaluation, guidelines, etc. • Takes lots of time, skill & effort • Only works for goal-directed tasks • Assumes expert performance • Does not address several UI issues • Readability, memorizability of icons, etc

44. In conclusion • Know your users capabilities and limits • Models such as Fitts’s and GOMS can help you test your UI without real users • But there’s still no substitute for user studies

45. Nuts & Bolts

46. Assignments Upcoming: • Contextual inquiry (Due Sept. 27) • Pick appropriate method • Group analysis • Report

47. Next time Design Process: Implement Low Fidelity Prototyping • Readings • The Case Against User Interface Consistency • Norman's The Design of Everyday Things, Chapter 6 • Steve Krug "Don't make me think" (handout)

48. Don’t Forget to pickup: “Don’t Make Me Think!” handout A gift for your test subject