Cognitive Engineering PSYC 530 Cognitive Engineering and Allocation of Function in Systems

1. CognitiveEngineeringPSYC 530Cognitive Engineering and Allocation of Function in Systems Raja Parasuraman

2. Overview • Cognitive Engineering Methods • Allocation of Function • The Information Processing Approach

3. Cognitive Engineering Methods • Field studies • Accident reports • Surveys • Usability analyses • Task simulations • Laboratory experiments • Literature (research and handbooks) • Models (human simulations)

4. Field Studies • Pros: • Any findings can be immediately applied • Natural behavior (“Cognition in the Wild”) • But does observation change the observed behavior? • Cons: • Uncontrolled setting • Can general principles be obtained? • Observer theoretical bias (cf. Anthropology)

5. Accident Reports Lt. Selfridge, 1908 (Orville Wright, Pilot) • Pros: • Potential for identifying specific factors—human and machine—contributing to a real event • Cons: • Hindsight bias • Limited generality Three Mile Island, 1979 Chernobyl, 1986 PanAm/KLM, Tenerife, 1979 SUV Rollover

6. Surveys • Pros: • Relatively accurate assessment of user preferences in a specified population • Useful starting point for controlled lab studies or field work • Cons: • Reasons or underlying mechanisms for preferences not well understood • Preference may not translate into performance (e.g., use of color displays in satellite photo interpretation by intelligence analysts)

7. Usability Analyses • Pros: • Relatively accurate assessment of user preferences in a specified population • Useful in assessing prototypes • Cons: • Methods vary from very simple (“do you like it?”) through “think-aloud” to extensive (eye movements, performance testing); lack of standardization Useful new tool: CogTool (Carnegie Mellon University) http://www.cs.cmu.edu/~bej/cogtool/

8. Task Simulations Driving • Pros: • Captures some of the complexity of real systems in a controlled experimental setting • Can safely study operator response to emergency or catastrophic events (e.g., crash scenarios in driving simulators; engine failure in flight simulators) • Can be linked to cognitive theory derived from basic laboratory studies • Cons: • Generalizability to real world may be limited • Can be expensive if domain experts are used as subjects (e.g., air traffic controllers, pilots) Aviation Medical

9. Laboratory Experiments • Pros: • Control over confounding variables • Can potentially lead to theory that can be applied to a range of system design issues • Cons: • Highly controlled setting unrepresentative of real world behavior • Generality of findings limited due to number of uncontrolled factors in real world

10. Laboratory Experiments: Some Cons • Potential for endless testing and re-testing of • competing hypotheses and micro-issues, with • little relevance outside the laboratory Smith Smith: “For my PhD thesis I will show that the theory of Jones is wrong because it does not take into account the factor X that Jones has shown to be important and the factor Y that I have identified.” Brown: “Smith and Jones were partially correct but neither of them considered that factor Z moderates the effects of X and Y.” Jones Brown Smith, Jones, and Brown are great scientists. But are X, Y, and Z relevant in the real world?

11. Literature and Handbooks • Pros: • Handbooks: Ready source of information for application (e.g., Boff & Lincoln, Engineering Data Compendium, 1988). • Guidelines for design of interfaces (e.g, minimum font size, use of color, anthropometry, etc,) • Cons: • Assumptions behind the data may be violated in specific application (e.g., font size for static vs. moving stimuli) • Certain design issues may not be addressed at all (e.g., what kind of feedback should an automated system provide to the user about system state?)

12. Modeling • Pros: • Can be used to examine a wide range of situations that cannot be economically studies with simulations or experiments • Can be used to predict impact of proposed designs on human operator performance (e.g., IMPRINT model of mental workload) • Cons: • Assumptions of model may be violated in real world settings • Difficult to distinguish between competing models (e.g., AMBR project pitting ACT-R vs SOAR vs others)

13. Cognitive Engineering Methods Hard High Field studies Accident reports Surveys Task simulations Lab experiments Literature and handbooks Models Ease of System Design Change Design Relevance of Conclusions Easy Ease of Control and Inference Low

14. This is not a design course. But if it were………..

15. Boeing 777 Flight Deck Designing a safe and efficient automated cockpit Successful design requires a systems approach that follows from a functional analysis of top-level goals Designing a user friendly interface for a personal computer

16. Human-Machine System Environment Human Machine Sensory Cognitive Motor Display Processor Control Interface

17. Matching Humans and Machines • I. “Fitting the Machine to the Human”: Display control, and interface design • II. “Fitting the Human to the Machine”: Selection and training Human Machine Sensory Cognitive Motor Display Processor Control Interface

18. Systems Design • The human-machine system is designed to achieve certain goals • Elements of the human-machine system include humans, machines, interfaces, and the environment, and sub-systems of these • Elements interact and exchange energy, matter, and information • Each element has a function • Systems design issue: which elements carry out a particular function?

19. Functions and Means • Goal Functions  Means • (Rasmussen, 1986; Information Processing and Human-Machine Interaction) • A function is a representation of a transaction that must take place to achieve the goal • The function can be identified without specifying the means by which the function is carried out

20. Examples • Function: Application of power • Gasoline engine and generator • Batteries • Solar power • Function: Information storage • Computer disk • Magnetic tape • Human long term memory • Function: Diagnostic decision making • Human expert • Computer expert system

21. Systems Design • Identify and define functions • Allocate functions to system elements--humans, machines, environment • Test resulting system • Display, control and interface design • Selection and training of personnel • Final system design • Field test

22. Allocation of Function • The Fitt’s List (Fitts, 1951) • Also known as the MABA-MABA* List: • Critiques by Jordan (1963) and others • Goals-means-ends analysis (Rasmussen, 1986) • Concept of adaptive function allocation (Hancock, Rouse, etc) * “Machines are better at — Men are better at”

23. The Fitts’ List (1951) • Machines are better at the following functions: • Speed (Human time delay: 0.5 - 1 s) • Power (Human maximum output: 0.2 hp/day) • Consistency of application (Machine: constant; Human: variable, susceptible to fatigue) • Humans are better at the following functions: • Reasoning (Human: Inductive and deductive; Machine: deductive only) • Intelligence (Human: Adaptive, flexibl;: Machine: limited) • Fine motor skills (Human: Versatile, flexible; Machine: limited) • Mixed • Computation (Human: slow, error prone but good at error correction; Machine: Fast, accurate, poor at error correction) • Memory (Human: Large store, random access; Machine: Very large, poor access)

24. The usefulness for systems design of the Fitts’ List or related approaches to function allocation has been questioned However, the approach may still have merit if function allocation is considered to be dynamic rather than static Machine Functions System Design System Operations Static Fixed Division Of Labor Machine Functions Human Functions System Operations System Design Variable Division Of Labor Dynamic Human Functions

25. Does the Fitt’s List still apply? • Increasing machine intelligence • Moore’s Law • Kurzweil’s predictions • Human-machine symbiosis (“Joint cognitive systems” • Augmented human cognition

26. Moore’s Law

27. Kurzweil’s Predictions http://www.kurzweilAI.net/ Technological change is exponential, contrary to the common-sense "intuitive linear" view. So we won't experience 100 years of progress in the 21st century -- it will be approximately 20,000 years of progress (at today's rate). The "returns," such as chip speed and cost-effectiveness, also increase exponentially. There's even exponential growth in the rate of exponential growth. This exponential growth is not restricted to hardware, but with accelerating gains in brain reverse engineering, also applies to software. Within a few decades, machine intelligence will surpass human intelligence, allowing nonbiological intelligence to combine the subtleties of human intelligence with the speed and knowledge sharing ability of machines. The results will include the merger of biological and nonbiological intelligence, downloading the brain and immortal software-based humans -- the next step in evolution.

28. Human-Machine System Environment Human Machine Sensory Display Processor Control Cognitive Motor Interface

29. Cognition and Behavior Stimulus Response Inferred Information Processing Components “The Machinery of the Mind”

30. History of Cognitivism • Wundt (1860)—Psychophysics • Titchener (1880)—Structuralism • James (1890)—Functionalism • Freud (1900)—The unconscious • Watson (1920) to Skinner (1950): Behaviorism • Renewal of Cognitive Psychology (1950-present) • Broadbent (1958) • Miller (1957) • Chomsky (1957)

31. Metaphors for Cognitivism • 1950-60s: Telephone and radio communications (Information theory) • 1970-present: General-purpose, serial digital computer • Register (Short-term store) • Memory (Long-term store) • Central Processing Unit — CPU (Working Memory) • Time-shared CPU (Attentional Resources) • 1980s-present: Parallel computers • Connectionism • Learning • 1990s-present: Biological systems • Cognitive neuroscience • Genetics • Evolutionary psychology

32. From Memory 0 “Top-Down” “Bottom-Up” Processing 1 0 0 8-Bit Shift Register 1 0 1 1 0 1 From Input (e.g, keyboard) Machine Metaphors and Cognitive Theory: An Example

33. The Information-Processing View • Behavior is a function of the execution of different stages of information processing • Sensory memory (iconic/echoic memory) • Short-term memory • Perceptual processing • Symbolic or semantic processing • Decision making • Response selection • Response execution • Each stage passes on its product to the next stage • Stages make differential demands on a central processing store (attentional resources; working memory)

34. Caveats Serial vs parallel vs cascaded processing S R Serial S R Parallel S Cascaded R

35. T E C T “Percepts without concepts are blind; concepts without percepts are empty.” Immanuel Kant, Critique of Pure Reason (1781) Caveats (contd.) Top-down vs. Bottom-up

36. Caveats (contd.) Linear vs. interactive (cyclic) information processing Information processing approach S R Perception Ecological Psychology approach Action

37. Domains of Information Processing • Signal detection • Vigilance • Perception • Selective attention • Memory • Decision making • Attention and mental workload