Models of Human Performance

1. Models of Human Performance Prof. Chris Baber

2. Objectives • Introduce theory-based models for predicting human performance • Introduce competence-based models for assessing cognitive activity • Relate modelling to interactive systems design and evaluation

3. Some Background Reading Dix, A et al., 1998, Human-Computer Interaction (chapters 6 and 7) London: Prentice Hall Anderson, J.R., 1983, The Architecture of Cognition, Harvard, MA: Harvard University Press Card, S.K. et al., 1983, The Psychology of Human-Computer Interaction, Hillsdale, NJ: LEA Carroll, J., 2003, HCI Models, Theories and Frameworks: towards a multidisciplinary science, (chapters 1, 3, 4, 5) San Francisco, CA: Morgan Kaufman

4. Reading for the Assignment Salvucci, D.D., 2009, Rapid prototyping and evaluation of in-vehicle interfaces, ACM Transactions of Computer-Human Interaction, 16 (2), http://www.cs.drexel.edu/~salvucci/publications/Salvucci-ToCHI09.pdf

5. Models of Human Performance in Systems Design

6. Why Model Performance? • Human-centred systems design as Applied Science • Theory from cognitive sciences used as basis for design • General principles of perceptual, motor and cognitive activity • Development and testing of theory through models • Human-centred systems design as Engineering • Abstract aspects of performance to build models • Use models to predict performance • Use predictions to evaluate design concepts

7. Pros and Cons of Modelling • PROS • Consistent description through (semi) formal representations • Set of ‘typical’ examples • Allows prediction / description of performance • CONS • Selective (some things don’t fit into models) • Assumption of invariability • Misses creative, flexible, non-standard activity

8. Types of Model in HCI Whitefield, 1987

9. User Models in Design • Benchmarking • Human Virtual Machines • Evaluation of concepts • Comparison of concepts • Analytical prototyping

10. Elementary Cognitive Psychology

11. Seven Stage Action Model[Norman, 1990] GOAL OF PERSON

12. Seven Stage Action Model[Norman, 1990] GOAL OF PERSON Action Processes Cognitive Processes

13. Describing Human Action • Input • Information from world • Processor • Matching input to output • Output • response

14. Typing 14 • Eye-hand span related to expertise • Expert = 9, novice = 1 • Inter-key interval • Expert = 100ms • Strategy • Hunt & Peck vs. Touch typing • Keystroke • Novice = highly variable keystroke time • Novice = very slow on ‘unusual’ letters, e.g., X or Z

15. Salthouse (1986) 15 • Input • Text converted to chunks • Parsing • Chunks decomposed to strings • Translation • Strings into characters and linked to movements • Execution • Key pressed

16. Rumelhart & Norman (1982) 16 • Perceptual processes • Perceive text, generate word schema • Parsing • Compute codes for each letter • Keypress schemata • Activate schema for letter-keypress • Response activation • Press defined key through activation of appropriate hand / finger

17. Schematic of Rumelhart and Norman’s connectionist model of typing middle index ring thumb little Right hand middle ring index little thumb Left hand Response system Keypress node, breaking Word into typed letters; Excites and inhibits nodes z z j a activation Word node, activated from Visual or auditory stimulus jazz 17

18. Performance vs. Competence • Performance Models • Make statements and predictions about the time, effort or likelihood of error when performing specific tasks; • Competence Models • Make statements about what a given user knows and how this knowledge might be organised.

19. Performance Models

20. Fitts’ Law • Paul Fitts 1954 • Information-theoretic account of simple movements • Define the number of ‘bits’ processed in performing a given task

21. Fitts’ Tapping Task W D

22. Distance (mm) W D MT Time (ms) Graphs of Distance and Velocity Velocity Time Preparation Ballistic Homing Rest

23. Assumption • Feedback processing • Approach target and sample remaining distance • Proportional correction to movement to reduce error between current position and target

24. Derivation • Assume movement from Start to Target is a series of submovements • Assume each submovement requires a constant time (t) • Assume each submovement moves a constant fraction of the remaining distance to the target (1-r) • Assume that distance to target reduces exponentially ovcr time • Assume that a number, N, submovment needs to fall inside target • At t = 0, distance to target = D • At t = 1, distance to target is rD • At t = n, remaining distance is rnD

26. Hits 60 40 20 0 54 43 • A = 62, W = 15 • A = 112, W = 7 • A = 112, W = 21 a = 10 b = 27.5 21 b a Log2 (2A/W) 1 = 5.3 2 = 4.5 3 = 3.2 Alternative Versions MT = a + b log2 (2A/W) MT = b log2 (A/W + 0.5) MT = a + b log2 (A/W/+1) Fitts’ Law MT = a + b (log2 2A/W)

27. MT x W x A http://www.mindhacks.com/blog/2005/01/size_and_selection_t.html

28. What does this tell us? • Some forms of target-aiming movement can be defined, in simple terms, as sample and correct • Target-aiming (in Fitts Law tasks) involves visual guidance of movement to a known end-point • This form of movement can be defined as CLOSED LOOP

29. Tracking • Control movement of X to keep it on the path

30. Simple Tracking Activity • Compensatory tracking • Closed loop • Sample output and compare with input • Correct difference (error) • Pursuit tracking • Open loop • Focus on input • Assume dynamics known and anticipated

31. Closed Tracking Loop Source: Wickens, C.D., 1992, Engineering Psychology and Human Performance, New York, Harper Collins [2nd edition]

32. Pursuit vs.Compensatory Pursuit tracking – display target and person’s movement separately Compensatory – display difference between target and movement

33. Pursuit vs. Anticipatory • Preview • Lag • Internal model (prediction of error)

34. Human Limits in Tracking (1) • Processing time • Effective time delay (lag between perturbation and perception) • Zero and First Order control  150ms – 300ms • Second Order control  400ms – 500ms • Bandwidth • Limited by quality of display, e.g. 4 – 10 bits per second • Limited by frequency of action, e.g., 0.5 – 1 Hz (i.e., around 2 corrections per second)

35. Human Limits in Tracking (2) • Prediction & Anticipation • Usually responses are not required at high limit of action, so operators are able to anticipate demands • Processing Demands and Compatibility • Anticipation requires an internal model, but managing such a model can place demands on working memory • Matching the internal model with the external world can be made easier if there is a good match (compatibility) between them

36. Anticipation • Anticipation requires some ‘model’ of the system being controlled • Understanding system dynamics (knowledge) • Tuning of performance (practice) • Information from the world (sampling)

37. Automaticity 37 • Norman and Shallice (1980) • Fully automatic processing controlled by SCHEMATA • Partially automatic processing controlled by either Contention Scheduling • Supervisory Attentional System (SAS)

38. Supervisory Attentional System Model Supervisory Attentional System Control schema Trigger database Perceptual System Effector System Contention scheduling 38

39. Contention Scheduling 39 • Gear changing when driving involves many routine activities but is performed ‘automatically’ – without conscious awareness • When routines clash, relative importance is used to determine which to perform – Contention Scheduling • e.g., right foot on brake or clutch

40. SAS activation 40 • Driving on roundabouts in France • Inhibit ‘look right’; Activate ‘look left’ • SAS to over-ride habitual actions • SAS active when: • Danger, Choice of response, Novelty etc.

41. Attentional Slips and Lapses 41 • Habitual actions become automatic • SAS inhibits habit • Perserveration • When SAS does not inhibit and habit proceeds • Distraction • Irrelevant objects attract attention • Utilisation behaviour: patients with frontal lobe damage will reach for object close to hand even when told not to

42. Data-driven perception 42 Activation of neural structures of sensory system by pattern of stimulation from environment

43. Theory-driven perception 43 Perception driven by memories and expectations about incoming information.

44. KEYPOINT 44 PERCEPTION involves a set of active processes that impose: STRUCTURE, STABILITY, and MEANING on the world

45. State of the World signal noise False alarm Hit Yes Response No Correct rejection Miss Signal Detection Theory • Detecting signals against noise

46. Distribution of responses No Yes noise signal Correct rejection Hit False alarm Miss  Criterion beta

47. Some maths… Given a distribution of Signals to Noise, trade-off the Value (V) of Hits and Correct Rejections against the Cost (C) of Misses and False Alarms. Changing the value of  leads to more ‘risky’ or more ‘conservative’ response

48. Performance Operating Characteristics Resource-dependent trade-off between performance levels on two tasks Task A and Task B performed several times, with instructions to allocate more effort to one task or the other

49. Task Difficulty • Data limited processes • Performance related to quality of data and will not improve with more resource • Resource limited processes • Performance related to amount of resource invested in task and will improve with more resource

50. POC P P Cost M Cost Task A Task A M Task B Task B Data limited Resource limited