Assistant Professor Computer Science. Introduction to Human-Computer Interaction

Transcription

1 CMSC434 Introduction to Human-Computer Interaction Week 07 Lecture 19 Nov 4, 2014 Human Information Processing Human Computer Interaction Assistant Professor Computer Science

2 TODAY 1. Wrapping up Fitts Law 2. Improving Pointing 3. Human-Information Processing 4. GOMS Model 5. TA06 Check-In

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5 rapping up itts law

6 FITTS LAW MT A (amplitude) movement time W (width) There are different formulations in HCI

7 FITTS LAW IN PRACTICE Which will be faster on average? pie menu (bigger targets & less distance) [adapted from Hartmann, Landay]

8 PIE MENU VS. LINEAR MENU

9 USING A PIE MENU IN PRACTICE Source:

10 OTHER PIE MENU EXAMPLES Why aren t Pie Menus more widely adopted? Rainbow 6 Maya The Sims [adapted from Landay]

11 MARKING MENUS

12 Source:

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14 mproving ointing

15 TARGET ACQUISITION [adapted from Findlater]

16 SUB-MOVEMENT ANALYSIS [adapted from Findlater]

17 SUB-MOVEMENT ANALYSIS [adapted from Findlater]

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20 Bubble Cursor Grossman & Balakrishnan, CHI 05

21 enhanced area cursors reducing fine pointing demands for people with motor impairments leah findlater alex jansen kristen shinohara morgan dixon peter kamb joshua rakita jacob o. wobbrock 21

22 ENHANCED AREA CURSORS

23 [adapted from Findlater]

24 ENHANCED AREA CURSORS: FOUR TYPES [adapted from Findlater]

25 [adapted from Findlater]

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27 [adapted from Findlater]

28 [adapted from Findlater]

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30 [adapted from Findlater]

31 [adapted from Findlater]

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34 evaluation

35 do the new cursors lessen effects of small target size? reduce need for corrective-phase pointing? reduce need for accurate, steady clicking? [adapted from Findlater]

36 task 36

37 12 participants de quervain s stenosynovitis tetraplegia cerebral palsy parkinson s disease spinal cord injury friedreich s ataxia multiple sclerosis muscular dystrophy [adapted from Findlater]

38 12 participants 3 target sizes 4px 8 px 16 px [adapted from Findlater]

39 12 participants none 3 target sizes 3 target spacings half-target width full-target width [adapted from Findlater]

40 12 participants 3 target sizes 3 target spacings 2 levels of clutter [adapted from Findlater]

41 12 participants click-and-cross 3 target sizes 3 target spacings 2 levels of clutter 6 cursors cross-and-cross motor-magnifier visual-motormagnifier bubble point [adapted from Findlater]

42 do the new cursors lessen effects of small target size? [adapted from Findlater]

43 mean trial time (seconds) speed 8 4 pixels 8 pixels 16 pixels error bars: standard error point bubble motormagnifier visualmotormagnifier clickandcross crossandcross [adapted from Findlater]

44 mean trial time (seconds) speed pixels 8 pixels 16 pixels fastest for smaller sizes error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross [adapted from Findlater]

45 mean trial time (seconds) speed 8 4 pixels 8 pixels 16 pixels reduced effect of small target size error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross [adapted from Findlater]

46 mean trial time (seconds) speed 8 4 pixels 8 pixels 16 pixels error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross [adapted from Findlater]

47 mean error rate errors pixels 8 pixels 16 pixels error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross [adapted from Findlater]

48 mean error rate errors pixels 8 pixels 16 pixels reduced errors compared to point error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross e e e [adapted from Findlater]

49 do the new cursors lessen effects of small target size? reduce need for corrective-phase pointing? [adapted from Findlater]

50 mean number of submovements submovement analysis 50 4 pixels 8 pixels 16 pixels error bars: standard error point bubble motors magnifier visualmotormagnifier s clickandcross s crossandcross e e e [adapted from Findlater]

51 mean number of submovements submovement analysis pixels 8 pixels 16 pixels reduced submovements compared to point error bars: standard error point bubble motors m magnifier visualmotormagnifier s m clickandcross s crossandcross e e m e [adapted from Findlater]

52 mean number of submovements submovement analysis pixels 8 pixels 16 pixels extra movement for activation error bars: standard error point bubble motors m magnifier visualmotormagnifier s m clickandcross s crossandcross e e m e [adapted from Findlater]

53 mean number of submovements submovement analysis 50 4 pixels 8 pixels 16 pixels error bars: standard error point bubble motors m magnifier visualmotormagnifier s m clickandcross s crossandcross e e m e [adapted from Findlater]

54 do the new cursors lessen effects of small target size? reduce need for corrective-phase pointing? reduce need for accurate, steady clicking? [adapted from Findlater]

55 most preferred number of participants visual-motor-magnifier slowest, but still preferred cross-and-cross click-and-cross bubble

56 uman-information rocessing

57 Cognitive psychology is the study of higher mental processes such as attention, language use, memory, perception, problem solving, and thinking. American Psychological Association

58 Stuart K. Card Thomas P. Moran Allen Newell

59 Distinguished Engineer at IBM PhD in psychology from CMU Early HCI Pioneer at PARC PhD in from CMU w/herb Simon Early HCI Pioneer at RAND/CMU Stuart K. Card Thomas P. Moran Allen Newell

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61 The domain of concern to us, and the subject of this book, is how humans interact with computers. A scientific psychology should help us in arranging this interface so it is easy, efficient, error-free even enjoyable. Card, Moran, and Newell Early pioneers of the field of HCI Quote from: The Psychology of Human-Computer Interaction, 1983, p. vii

62 Model Human Processor The Model Human Processor offers a simplified view of the human processing involved in interacting with computing systems. Comprises three subsystems: 1. Perceptual system 2. Motor system 3. Cognitive systems Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

63 1. The perceptual system handles sensory stimuli from the outside world 2. The cognitive system provides the processing needed to connect the two 3. The motor system controls physical actions

64 Each subsystem has its own processor and memory 1. The perceptual system handles sensory stimuli from the outside world 2. The cognitive system provides the processing needed to connect the two 3. The motor system controls physical actions

65 The Model Human Processor The Principles of Operation P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

66 The Model Human Processor The Principles of Operation The time T n to perform a task on the n th trial follows a power law: T n = T 1 n -α P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

67 The Model Human Processor The Principles of Operation The time T n to perform a task on the n th trial follows a power law: T n = T 1 n α P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle where α =.4 [ ] Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

68 POWER-LAW OF PRACTICE The power law of practice states that the logarithm of the completion time for a particular task decreases linearly with the logarithm of the number of practice trials taken

69 Source: Newell & Rosenbloom, Mechanisms of skills acquisition and the law of practice, 1980

70 POWER-LAW OF PRACTICE: EXAMPLE TASKS Trail Making Test Match-to-Sample Task

71 The Model Human Processor The Principles of Operation The time T pos to move the hand to a target of size S which lies a distance D away: P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle T pos = I M log 2 (D/S + 0.5) Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

72 The Model Human Processor The Principles of Operation A person acts so as to attain his goals through rational action, given the structure of the task and his inputs of information and bounded limitations on his knowledge and processing ability: P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle Goals + Task + Operators + Inputs + Knowledge + Process-limits -> Behavior Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26

73 GOMS Model A GOMS model, as proposed by Card, Moran, and Newell (1983), is a description of the knowledge that a user must have in order to carry out tasks on a device or system; it is a representation of the "how to do it" knowledge that is required by a system in order to get the intended tasks accomplished. [Kieras, A Guide to GOMS Analysis, 1994; Card et al., The Psychology of Human-Computer Interaction, 1983]

74 GOMS Model An attempt to model the knowledge and cognitive processes involved when a user interacts with a system Goals refers to a particular state the user wants to achieve Operators refers to the cognitive processes and physical actions that need to be performed to achieve those goals Methods are learned procedures for accomplishing the goals Selection rules are used to determine which method to select when there is more than one available. [Rogers et al., Interaction Design, Chapter 15, 2011; Card et al., The Psychology of HCI, 1986]

75 GOMS Model Example 1 2 Goal: find a website about GOMS Operators: Decide to use search engine, decide which search engine to use,

76 GOMS Model Example Goal: find a website about GOMS Operators: Decide to use search engine, decide which search engine to use, think up and enter keywords. Methods: I know I have to type in search terms and then press the search button. Selection: Do I use the mouse button or hit the enter key?

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79 GOMS Model The goal of this work [GOMS modeling] is to radically reduce the time and cost of designing usable systems through developing analytic engineering models for usability based on validated computational models of human cognition and performance. DavidKieras Professor in EECS and Psychology at the University of Michigan GOMS Advocate [Kieras, GOMS Models: An Approach to Rapid Usability Evaluation,

80 GOMS Model GOMS is such a formalized representation that it can be used to predict task performance well enough that a GOMS model can be used as a substitute for much (but not all) of the empirical user testing needed to arrive at a system design that is both functional and usable. DavidKieras Professor in EECS and Psychology at the University of Michigan GOMS Advocate [Kieras, GOMS Models: An Approach to Rapid Usability Evaluation,

81 TA06 Mid-Fi Prototypes Check-In Remember: In-Class Design Critiques This Thursday!

82 Dark Palette

83 Light Palette

84 Smartsheet Gantt Palette

85 Light Palette