Descriptive Cognitive Models

1. Descriptive Cognitive Models Rodin once said that carving a sculpture was easy, all you had to do was take a block of stone and remove everything that wasn´t the statue. MV4002 Training in Virtual Environments

2. Overview • Situated cognition • Naturalistic decision making (NDM) • Recognition-primed decision (RPD) model • GOMS (Goals, Operators, Methods and Selection rules) MV4002 Training in Virtual Environments

3. Situated Cognition • Explain limitation of current instruction • Ignore situated nature of knowledge • Speculate on why knowledge is situated • Compare learning activities • Propose improved methods • “Cognitive apprenticeship” MV4002 Training in Virtual Environments

4. Situated! • Example – vocabulary • Classroom: 100-200 words/year • Life: 5,000 words/year • Dictionary and examples • Reference vice teaching tool • Context-dependent aspect • Indexical words • All knowledge is context dependent MV4002 Training in Virtual Environments

5. Situated? • Example of language • Primarily a social task • Biology of language acquisition • OK, situated • Application • Abstraction is a necessary evil • Knowledge acquisition or maintenance MV4002 Training in Virtual Environments

6. Knowledge and Learning • Acquisition versus use • Analogous to tools • Ability to manipulate algorithms • JPFs and practitioners strategies • Problem world based • Effective • Cultural dependencies • Current culture is school MV4002 Training in Virtual Environments

7. Enculturation • Byproducts of culture of classroom • Position of problem in book • Key words as cues (‘left’ ~ subtraction) • What about your training system? • USMC H-53 MBT flight dynamics • Cueing for and compression of EPs MV4002 Training in Virtual Environments

8. Apprenticeship Activity based Honors nature of learning Context-dependent Situated Enculturation Cognitive Beyond physical skill Cognitive Apprenticeship MV4002 Training in Virtual Environments

9. Models of Decision Making MV4002 Training in Virtual Environments

10. Overview • Classical decision making • Naturalistic decision making • Recognition-primed decision model • GOMS (Goals, Operators, Methods, Selection) • Model • Case study MV4002 Training in Virtual Environments

11. Classical Decision Making • Prescriptive • Superimpose formal structure for decision making • Assumes model can be learned and followed • Little room for variation • Poor transfer to real world • Situated cognition MV4002 Training in Virtual Environments

12. Naturalistic Decision Making • Focus on how people actually make decisions • Emphasizes complex, real-world decision making • Ill-structured problems • Uncertain dynamic environments • Shifting, ill-defined, competing goals • Action/feedback loops • Time stress • High stakes • Multiple players • Organizational norms and goals MV4002 Training in Virtual Environments

13. Naturalistic Decision Making • Initial course of action is often good • Time pressure doesn’t degrade expert performance • Training systematic scanning degraded performance • Relies on expertise MV4002 Training in Virtual Environments

14. NDM • Decision making is not a task, but a means to an end • Requires flexibility because of the changing situation or knowledge • Multiple goals competing for attention • Time pressure and stress • Execution may involve multiple people MV4002 Training in Virtual Environments

15. NDM • Value of simulation • Decision making cannot be taught in isolation • Team decision making should be incorporated MV4002 Training in Virtual Environments

16. Prescriptive Model • Military decisions • Often made under the conditions in which NDM occurs • Become increasingly complex as information and components increase • Military planning model • Check list, coordinating vehicle, and aid to better decision making MV4002 Training in Virtual Environments

17. Prescriptive Model • Criteria for decision quality of models • Time needed • Clarity in formulation of vision • Perception of credibility, execution and necessity • Degree of originality • Degree of systems coordination • Degree of leveraging prior experience MV4002 Training in Virtual Environments

18. Current Planning Model • Two parts: planning and execution • Planning is a highly linear step-by-step model • Time-consuming because emphasis is on developing 3 own-force COAs and 2 enemy COAs • Little emphasis on analysis • Different from expert decision makers MV4002 Training in Virtual Environments

19. New Planning Model • Need for a model that describes how commander actually make decisions • Account for individual differences • Account for problem complexity • Evolving problem • Stressors: due to time pressure, lack of sleep, personal threat… • No objectively right solution MV4002 Training in Virtual Environments

20. New Planning Model • Better to come to a quick acceptable solution than to take more time • Solution that takes more time can still fail if course of events changes • Only use events that are crucial to situation • Planners should also participate in execution MV4002 Training in Virtual Environments

21. New Planning Model • Deal with uncertainty, but don’t try to quantify it • Don’t place too high a demand on cognitive processes • Intuition and unconscious understanding of the situation play a big part MV4002 Training in Virtual Environments

22. Planning Under Time Pressure Model • Understand the mission • Assess the situation • Define criteria of success • Find concept of COAs • Develop COA • Simulate • Decide • Develop mission orders • Develop opportunities for proactive decision making MV4002 Training in Virtual Environments

23. PUT Advantages • Less time-consuming • More reflective of how decisions actually occur • Expert takes a more active part • Leverages off prior experience MV4002 Training in Virtual Environments

24. RPD Fundamentals • Naturalistic decision making model • Time pressure • Ambiguous information • Ill-defined goals • Changing conditions • Experience to assess a situation • Train people to make better decisions • Train people to design equipment that will support decision-making MV4002 Training in Virtual Environments

25. RPD Assertions • People can use experience to generate a plausible option as the first one they consider • Time pressure need not cripple the performance of decision makers with considerable experience because • Rely on pattern matching • Experts don’t compare and contrast options before deciding MV4002 Training in Virtual Environments

26. RPD Diagnosis • Descriptive account of the way experienced people make decisions • Link observed to causal factors • Explanation of events • Evaluate different courses of action MV4002 Training in Virtual Environments

27. Recognition Primed Decisions • Simple Match • Carry out a course of action without having to generate analysis of options for purposes of comparison • Use experience to identify a workable course of action as the first one considered MV4002 Training in Virtual Environments

28. Simple Match Decision maker identifies a situation and reacts accordingly Goals are obvious Critical cues are addressed Future expectations formed Course of action recognized MV4002 Training in Virtual Environments

29. Recognition Expectancies Cues Goals Action Simple Match Typical? Implement COA MV4002 Training in Virtual Environments

30. Diagnose the Situation • Use feature matching and story building • Relate to known/solved problems • Revisit situation to validate assumptions MV4002 Training in Virtual Environments

31. Recognition Expectancies Cues Goals Action Diagnose the Situation Experience the Situation Typical? Diagnose Clarify Anomaly Implement COA MV4002 Training in Virtual Environments

32. Mental Simulation • Evaluate a Course of Action • Deliberately assessed by conducting a mental simulation • Possible difficulties • Possible remedies and outcomes • New courses of action MV4002 Training in Virtual Environments

33. Evaluate Course of Action Typical? Recognition Expectancies Cues Goals Actions (1..n) Evaluate Modify Good? Yes, but… Hell no Implement COA MV4002 Training in Virtual Environments

34. RPD Conclusion • Simple match • Experience-based • Situation diagnosis • Pre-assessment • Mental simulation • Trial and evaluation MV4002 Training in Virtual Environments

35. GOMS Model Cognitive information-processing activities • Goals • Operators • Methods • Selection Rules MV4002 Training in Virtual Environments

36. Goals Symbolic structure that defines a state of affairs to be achieved and determines a set of possible methods by which it may be accomplished • Task segmentation • Small discrete units • Example: • Goal: Edit-Manuscript • Goal: • Edit-Unit-Task • Edit-Unit-Task • Edit-Unit-Task MV4002 Training in Virtual Environments

37. Goal: Edit-Manuscript • Goal: Edit-Unit-Task • Goal: Acquire-Unit-Task • Get-Next-Page • Get-Next-Task • Goal: Execute-Unit-Task • Goal: Locate-Line • [select: subtask] • [select: subtask] • Goal: Modify-Text • [select: subtask] • [select: subtask] MV4002 Training in Virtual Environments

38. Operators Perceptual, motor, or cognitive acts whose execution is necessary to change any aspect of the user’s mental state or to affect the task environment • Sequence of operators in user’s behavior: • Get-Next-Task • Use-Subtask-Method • Use-Subtask-Command • Verify-Edit MV4002 Training in Virtual Environments

39. Methods A method describes a procedure for accomplishing a goal and specifies the ways in which a user stores his knowledge of a task. • Goal: Acquire-Unit-Task • Get-Next-Page • Get-Next-Task MV4002 Training in Virtual Environments

40. Control Structure When a goal is attempted, there may be more than one method available to the user to accomplish the goal. • Selection of which method to be used • Extended decision process • Environment dictates only one option • Genuine decision may be required • Selection rules take hold MV4002 Training in Virtual Environments

41. GOMS Expert Analysis • Predictions of expert performance • Sequential analysis • Information is perceived right before it is needed for the next task • The user gets information that defines the next subtask and accomplishes that subtask, then gets the information for the next subtask, and so on • Looking ahead for information • User overtly records information for future use MV4002 Training in Virtual Environments

42. GOMS Modeling Heuristics • Expertise assumption • Anticipate likely situations from preliminary information • System response time • Time it takes for the system to provide information • Auditory, visual, physical parameters • Time it takes for the system to provide information MV4002 Training in Virtual Environments

43. GOMS Application • Map current process • Flowchart information or system process • Proposed specification • Model ideal or possible processes • Critical path • Determine best flow/path through possible systems • Implement model and evaluate MV4002 Training in Virtual Environments

44. GOMS Key Points • System evaluation in the real world • Training prototype design, development, implementation, evaluation • Structured methodology • To evaluate alternatives • To present options • To test scenarios • To evaluate outcomes MV4002 Training in Virtual Environments

45. Extension of GOMS • Typical GOMS • Static, visual input • User-paced • Constant data • Data on demand • TAO • Fleeting visual and auditory input • Speech input and output • Tasks in parallel MV4002 Training in Virtual Environments

46. TAO Model • Detailed model of TAO • Microseconds = megabucks • Critical path method • Arrange CPM in rows • Box - action; Line - data dependency • Bold line indicates critical path MV4002 Training in Virtual Environments

47. Analysis of TAO Workstations • Old system • 300 baud • Functional grouping of keyboard • Character-oriented display • Proposed system • 1200 baud • Ergonomic keyboard (spacing) • High resolution display • GUI based, HCI sensitive interface MV4002 Training in Virtual Environments

48. Results….. • Field test • Proposed workstation was 4% slower • GOMS prediction • Proposed workstation would be 3% slower MV4002 Training in Virtual Environments

49. Why? • Added steps to critical path • (Eliminated steps were from slack time) • Implication • Value of modeling • Value of usability study MV4002 Training in Virtual Environments