1. Modeling in HCI Stuart Card Palo Alto Research Center (PARC) Stanford University, CS376 November 19, 2009 1s. card

2. Why Model? 2s. card

3. EXAMPLE: POINTING DEVICES Mouse. Engelbart and English 3 s. card

4. TRADITIONAL METHOD: EVALUATION Sun Labs 4 s. card

5. Engelbart 5 s. card

6. EXPERIMENT: MICE ARE FASTEST 6 s. card

7. WHY? (ENGINIEERING ANALYSIS) 1 2 3 3 2 1 0 456 Movement Time (sec) I D =log (Dist/Size +.5) 2 Mouse T = 1.03 +.096 log 2 (D/S +.5) sec Why these results? Time to position mouse proportional to Fitts’ Index of Difficulty I D. Proportionality constant = 10 bits/sec, same as hand. Therefore speed limit is in the eye-hand system, not the mouse. Therefore, mouse is a near optimal device. 7 s. card

8. ENGINEERING ANALYSIS (Modeling) Insightful Accumulate into a discipline Generative 8 s. card

9. CUMULATING INTO A DISCIPLINE Chapanis Report on HF (National Research Council) Experimental methods alone are inadequate. Of 40 non-experimental techniques in human factors, only 2 were validated and taught. 9 s. card

10. TO BE GENERATIVE Task analysis Approximation Calculation Zero-parameter predictions 10 s. card

11. EXAMPLE: ALTERNATIVE DEVICES Headmouse: No chance to win 11 s. card

12. ATTACHING POINTING DEVICE Use transducer on high bandwidth muscles 12 s. card

13. Mouse (Arm) 0 500 1000 1500 2000 Head- mouse (Head) Fingers Paragraph Word Char Period Hard Easy Hard Easy TIME (msec) EXAMPLE: STRUCTURING THE TASK SPACE BY PROJECTING THE MODEL 13 s. card

14. EXAMPLE: BEATING THE MOUSE Use transducer on high bandwidth muscles 14 s. card

15. DESIGNS FROM RESTRUCTURED TASK SPACE Work with Bill Moggridge, IDEO 15 s. card

16. EXAMPLE: DESIGN SPACE 16 s. card

17. MORPHOLOGICAL DESIGN: GENERATING ALL INPUT DEVICES 17 s. card

18. POINTING DEVICES human use and context computer 18 s. card

19. MODEL HUMAN PROCESSOR Processors and Memories applied to human Used for routine cognitive skill 19 s. card

20. 20 s. card

21. EXAMPLE: ZERO-PARAMETER CALC Problem: Inventor claims he invented 600 wpm typewriter. License and develop? Solution 1: Half stroke:  M = 70 ms/char Whole stroke:  M +  M = 140 ms/char but if between hands, overlap:  M = 70 ms = 131 words/min 21 s. card

22. EXAMPLE: ZERO-PARAMETER CALC Solution 2: (range calculation) Half stroke:  M =70 [30~100] ms/char = 131 [308~92] words/min Conclusion: Bogus claim. Throw him out! 22 s. card

23. TASK ANALYSIS: GOMS ( GOALS, OPERATORS, METHODS, SELECTION RULES) GOAL: EDIT-MANUSCRIPT repeat until done GOAL: EDIT-UNIT-TASK GOAL: ACQUIRE-UNIT-TASK if not remembered GET-NEXT-PAGE if at end of page GET-NEXT-TASK if an edit task found GOAL: EXECUTE-UNIT-TASK GOAL: LOCATE-LINE if task not on line [select : USE-QS-METHOD USE-LF-METHOD] GOAL: MODIFY-TEXT [select USE-S-COMMAND USE-M-COMMAND] task analysis 23 s. card

24. PREDICTS TIME WITHIN ABOUT 20% 24 s. card

25. SAE RECOMMENDED PRACTICE J2365 Predict task times for car navigation systems Check compliance with SAE J2364 (15-Second Rule) Note: To estimate times while driving, multiply by 1.3 to 1.5. Based on GOMS and work by Paul Green at Univ. of Michigan Transportation Research Institute. Dario Salvucci 25 s. card

26. SAE J2365 OPERATOR TIMES Paul Green UMITRI 26s. card

27. LHX HELICOPTER SIMULATION (Corker, Davis, Papazian, & Pew, 1986) POP-UP-AND-SCAN POP-UP-FOR- SCAN [in parallel-do: LOOK-FOR POP-UP] STABILIZE-CRAFT HOVER-AND-SCAN [in-parallel-do: HOVER SCAN] GOMS used as task analysis to code doctrine 27 s. card

28. IMMEDIATE BEHAVIOR human use and context computer 28 s. card

29. HUMAN INFORMATION INTERACTION 29 s. card

30. GOMS Routine cognitive skill Well-known path 30 s. card

31. Information Search Problem solving Heuristic search Exponential if don’t know what to do 31 s. card

32. OPTIMALITY THEORY Information Energy Max Useful info Time Max Energy Time [][] Optimal Foraging TheoryInformation Foraging Theory 32 s. card

33. Information Foraging Theory: People are information rate maximizers of benefits/costs Information has a cost structure 33 s. card

34. INFORMATION PATCHES e.g. desk piles, Alta vista search list unlike animals foraging for food, humans can do patch construction 34 s. card

35. CHARNOV’S MARGINAL VALUE THEOREM: max gain when slope of within-path gain g = average gain R (tangent in diagram) Gain Within- patch time Between-patch time tBtB t* R* g(t W ) 35 s. card

36. BETWEEN-PATCH ENRICHMENT Gain Within- patch time Between-patch time t B1 t1*t1* g(t W ) Example: arrange physical office efficiently t B2 t2*t2* R1R1 R2R2 enrichment 36 s. card

37. WITHIN-PATCH ENRICHMENT Within- patch time Between-patch time g 1 (t W ) Example: Better filtering of search hits Gain tBtB t1*t1*t2*t2* R1R1 R2R2 g 2 (t W ) Behavior adapts to cost structure of environment. 37 s. card

38. WITHIN-PATCH ENRICHMENT: INFORMATION SCENT Tokyo San Francisco New York perception of value and cost of a path to a source based on proximal cues 38 s. card

39. RELEVANCE-ENHANCED THUMBNAILS Emphasize text that is relevant to query Text callouts Enlarge text that might be helpful in assessing page Enlarge headers Allison Woodruff 39 s. card

40. PHASE TRANSITION IN NAVIGATION COSTS AS FUNCTION OF INFORMATION SCENT Notes: Average branching factor = 10 Depth = 10 0246810 0 50 100 150 Depth Number of pages visited.100.125.150 0246810 0 50 100 150.100.150 Probability of choosing wrong link (f) 00.050.10.150.2 0 20 40 60 80 100 f Number of Pages Visited per Level Linear Exponential 40 s. card

41. IMPORTANCE FOR WEB DESIGN Jarad Spool, UIE 41 s. card

42. MACHINE MODELING OF INFORMATION SCENT cell patient dose beam new medical treatments procedures Information Goal Link Text 42 s. card

43. PREDICTION OF LINK CHOICE R 2 = 0.72 0 5 10 15 20 25 30 35 05101520253035 Observed frequency Predicted frequency R 2 = 0.90 0 10 20 30 40 50 01020304050 Observed frequency Predicted frequency (a)ParcWeb (b) Yahoo 43 s. card

44. USER FLOW MODEL Flow users through the network User need (vector of goal concepts) Determine relevance of documents.5.3.2 Calculate Pr(Link Choice) for each page Examine user patterns Start users at page 44 s. card

45. BLOODHOUND PROJECT Starting Point: www.xerox.com Task: look for “high end copiers” OUTPUT usability metrics INPUT Chi, et al 45 s. card

46. Information Cost Landscapes Exercise 46s. card

47. Moving to a Patch Gain Time gain(patch foraging time) Overall rate of gain (R) Optimal patch foraging timeTravel 47 s. card

48. How long to get to any one item in a patch? Gain Time  t2t2 ave. time for patches time to get one item n items accessible total items in patches t2t2 t1t1  48 s. card

49. Example: Rectangular patch of patches 10 items/patch A B1 B2B3 C6 D9 D23 C16 C2 C3C4 C5 D8 D24 C1 D3 D4D5 D6 D7 D1 D2 B8 B4 C7 D10 D22 C15 B7 B6B5 C8 D11 D21 C14 C12 C11C10 C9 D12 D20 C13 D17 D16D15 D14 D13 D19 D18 49 s. card

50. Task Names (Patch Names) D1D2D3D4D5D6D7 D24C1C2C3C4C5D8 D23C16B1B2B3C6D9 D22C15B8AB4C7D10 D21C14B7B6B5C8D11 D20C13C12C11C10C9D12 D19D18D17D16D15D14D13 50 s. card

51. Distances From Patch A              51 s. card

52. Patch Distances        52 s. card

53. COKCF Calculation Table TASKCOST (s)N Items A0.0010 B11.4110 B21.0010 B31.4110 B41.0010 B51.4110 B61.0010 B71.4110 B81.0010 C12.8310... 53 s. card

54. COKCF Calculation Table Sorted by Cost TASKCOST (s)N Items A0.0010 B21.0010 B41.0010 B61.0010 B81.0010 B11.4110 B31.4110 B51.4110 B71.4110 C22.2410 ……… 54 s. card

55. Cost-of-Knowledge Characteristic Function (COKCF) 55 s. card

56. COCKF Calculation Table Sorted by Cost TASKCOST (s)N Items A0.0010 B21.0010 B41.0010 B61.0010 B81.0010 B11.4110 B31.4110 B51.4110 B71.4110 C22.2410... 0  0.5 (10 items) 1  0.5 (80 items) 2  0.5 56 s. card

57. COCKF Calculation Table Grouped by Class Interval TASKCOST (s)N Items 0  0.5 0.00 10 1  0.5 1.00 80 2  0.5 2.00120 3  0.5 3.00160 4  0.5 4.00120 57 s. card

58. Smoothed COKCF 58 s. card

59. Cost of Knowledge Characteristic Function Gain in Knowledge Cost [Time] 59 s. card

60. Spiral Calendar 60 s. card

61. COCKF Calculation Table TASK Days Before 7 Sep 1993 COST (s)N Items 1) 1 (6 Sep 1993) 2) 3 (4 Sep 1993) 3) 10 (28 Aug 1993) 4) 30 (8 Aug 1993) 5) 100 (30 May 1993) 6) 300 (11 Nov 1992) 7) 1,000 (12 Dec 1990) 8) 3,000 (25 Sep 1982) 9) 10,000 (22 Apr 1966) 10) 30,000 (10 Jul 1911) 11) 100,000 (23 Nov 1719) 61 s. card

62. Design Warm-up (1 Block x 11 Trials) Test (5 Blocks x 11 Trials) [Order counterbalanced] 62 s. card

63. COCKF Calculation Table TASK Days Before 7 Sep 1993 COST (s)N Items 1) 1 (6 Sep 1993) 5.6 2) 3 (4 Sep 1993)11.1 3) 10 (28 Aug 1993)14.3 4) 30 (8 Aug 1993)14.6 5) 100 (30 May 1993)14.4 6) 300 (11 Nov 1992)16.6 7) 1,000 (12 Dec 1990)17.8 8) 3,000 (25 Sep 1982)21.1 9) 10,000 (22 Apr 1966)21.2 10) 30,000 (10 Jul 1911)20.7 11) 100,000 (23 Nov 1719)24.3 63 s. card

64. Raw Results 10 0 10 1 10 2 10 3 10 4 10 5 10 6 DAYS BACK 0 5 10 15 20 25 30 ACCESS TIME (s) 64 s. card

65. Many Interfaces for Foraging are Direct Walk Display 1 Display 2 Display 3 Click, Gesture, Etc Click, Gesture, Etc Click, Gesture, Etc Examples: WWW, Mac Finder, HyperCard 65 s. card

66. GOMS ANALYSIS Century-Method ::= GOAL: DO-TASK GOAL: ACCESS-DAY-CALENDAR GET-YEAR... if necessary GOAL: SELECT-CENTURY (1700’s) POINT-TO (Century=1700-1790s)==> GET-YEAR... if necessary GOAL: SELECT-DECADE (1710’s) POINT-TO (Decade=1710-1719)==> GET-YEAR... if necessary GOAL: SELECT-YEAR (1719) POINT-TO (Year=1700-1790s)==> GET-MONTH... if necessary GOAL: SELECT-MONTH (November) POINT-TO (Month=1700-1790s)==> GET-DAY... if necessary GOAL: SELECT-WEEK () POINT-TO (Week=contains 23)==> GET-DAY... if necessary GOAL: SELECT-DAY (23) POINT-TO (Day=23)==> } Cycle 1 } Cycle 2 } Cycle 3 } Cycle 4 } Cycle 6 } Cycle 5 66 s. card

67. COCKF Calculation Table TASK Days Before 7 Sep 1993 COST (s)N Items 1) 1 (6 Sep 1993) 5.6 (1 cycle) 2) 2 (4 Sep 1993)11.1 (2 cycles) 3) 10 (28 Aug 1993)14.3 (3 cycles) 4) 30 (8 Aug 1993)14.6 (3 cycles) 5) 100 (30 May 1993)14.4 (3 cycles) 6) 300 (11 Nov 1992)16.6 (4 cycles) 7) 1,000 (12 Dec 1990)17.8 (4 cycles) 8) 3,000 (25 Sep 1982)21.1 (5 cycles) 9) 10,000 (22 Apr 1966)21.2 (5 cycles) 10) 30,000 (10 Jul 1911)20.7 (5 cycles) 11) 100,000 (23 Nov 1719)24.3 (6 cycles) 67 s. card

68. Arranged by Interaction Cycles Day Week Month Year Decade 0 5 10 15 20 25 30 Century 10 0 10 1 10 2 10 3 10 4 10 5 10 6 DAYS BACK ACESS TIME (s) 68 s. card

69. COCKF Calculation Table (Grouped) TASK Days Before 7 Sep 1993 COST (s)N Items 1) Day (1 cycle) 5.6 (1 cycle) 2) Week (2 cycles)11.1 (2 cycles) 3) Month (3 cycles)14.4 (3 cycles) 6) Year (4 cycles)17.2 (4 cycles) 8) Decade (5 cycles)21.0 (5 cycles) 11) Century (6 cycles)24.3 (6 cycles) 69 s. card

70. Access Time = 3.3 + 3.5 NCycles NUMBER OF SELECT-DISPLAY CYCLES 0 5 10 15 20 25 30 0246 ACESS TIME (s) 70 s. card

71. COCKF Calculation Table (Grouped) TASK Days Before 7 Sep 1993 COST (s)N Items 1) Day (1 cycle) 5.6 (1 cycle) 1 2) Week (2 cycles)11.1 (2 cycles) 7 3) Month (3 cycles)14.4 (3 cycles) 30 6) Year (4 cycles)17.2 (4 cycles) 365 8) Decade (5 cycles)21.0 (5 cycles) 3,562 11) Century (6 cycles)24.3 (6 cycles)36,525 71 s. card

72. Spiral Calendar 10 2 10 1 10 3 10 4 10 5 10 6 10 7 10 0 ACCESS TIME = COST (s) 020406080100120 INFORMATION ACCESSIBLE (Days) 72 s. card

73. Sun CM Same task Same Procedure Restricted to Direct Walk methods Results of GOMS analysis: Access Time = 1.3 + 3.9m + 1.4 P + 0.36 B sec where m = number of point, menu pull-down, select P = number of point + select B = number of button presses 73 s. card

74. CM Spiral Calendar 10 2 10 1 10 3 10 4 10 5 10 6 10 7 10 0 COST (s) 020406080100120 INFORMATION ACCESSIBLE (Days) 74 s. card

75. No Week Calendar (calculated) 10 2 10 1 10 3 10 4 10 5 10 6 10 7 10 0 COST (s) 020406080100120 Spiral Calendar CM INFORMATION ACCESSIBLE (Days) 75 s. card

76. 2 sec User-Action Cycle (calculated) 10 2 10 1 10 3 10 4 10 5 10 6 10 7 10 0 COST (s) 020406080100120 Spiral Calendar CM INFORMATION ACCESSIBLE (Days) 76 s. card

77. 2 sec User-Action Cycle + No Week (calculated) 10 2 10 1 10 3 10 4 10 5 10 6 10 7 10 0 COST (s) 020406080100120 INFORMATION ACCESSIBLE (Days) 77 s. card

78. Summary Cost of Knowledge Characteristic Function (COKCF) is metric for cost landscape Can use to measure Can use for task analysis 78 s. card

79. Another COKCF calculation 79 s. card

80. SUMMARY: ENGINEERING ANALYSIS / MODLING 80 s. card