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1. œœ≥ • Just a quick note to let you know that PSYC 645 (methods in HFAC) has been scheduled for Tuesdays from 4:30-7:10 in Room 2073A David King (our conference room). All first-year students should be enrolled in this class for the spring. Let me know if you have any questions/concerns about the class.Debbie

2. Manual Control chapter 10

4. Intro to calculus • Derivatives: change (subtraction) • 0-order: position • 1st-order: speed (change in position) • 1st derivative of position • 2nd-order: acceleration (change in speed) • 1st derivative of speed • 2nd derivative of position

5. Derivatives • Derivative = change in something per unit time change in something unit of time

6. Position position = p(t) p(1) = 20 p(2)= 28 p(3)= 32, etc…

7. Speed speed = s(t)

8. Speed speed = s(t)

9. Acceleration acceleration = a(t)

10. Acceleration acceleration = a(t)

12. Integration • Integration = addition (summation) • So, speed at time 4 (constant acceleration example)

13. Manual Control • Previously, we looked at discrete responses: stimulus » perception » decision » selection » execution • open loop (environmental feedback) • discrete movements • focus on response selection rather than execution • Manual Control focuses on response execution • discrete open- and closed-loop • continuous closed-loop responses

14. Open- & Closed-loop systems • Closed Loop • Feedback based on directly measuring errors • That is, the action taken is dependent on the output of the system. • Output produces some error • Error is feedback to the system • next decision then takes that error into account

15. Open- & Closed-loop systems • Closed Loop • Heat-seeking missile • Missile constantly measures source of heat. • Source of heat relative to heading changes, producing error. • Missile uses error to correct it’s course

16. Open- & Closed-loop systems • Open Loop • Output of system does not affect subsequent actions • There is no feedback – ballistic in nature • Canon example: • Shell is fired, but it can not correct it’s course • Trajectory is determine by initial inputs only • elevation, amount of charge used, etc.

17. Discrete Movement Time • Fitt’s Law • Speed/accuracy trade-off for making ballistic (open-loop) movements • Trade-off is a function of • distance • target sizes • Mouse movement • target size = size of icon • distance = distance of mouse movement

18. Fitt’s Law MT = movement time a &b are irrelevant constants A = distance W= target width So, if target size is held constant, difficulty increases as distance increases. Likewise, if distance is held constant, difficulty decreases as width increases. Index of difficulty

19. Fitt’s Law Why are menu bars that are placed at the top of the screen accessed more quickly than menu bars at the tops of windows?

20. Fitt’s Law Because the menu bar is bounded at the top of the screen, it has infinite height. Users can toss the cursor to the top while being confident that the pointer will not overshoot the menu bar.

21. Fitt’s Law • Since it is a speed-accuracy trade-off, if speed and target width are held constant and distance increases, then accuracy must decrease. • Fitt’s Law only describes the latency to initiate a movement, rather than speed of the movement.

22. Models of Discrete Movement • Movements are rarely completely ballistic in nature • even saccades can be corrected in mid-flight • However, the corrections take some time, and are not continuous. • If the movement is long enough, several discrete corrections might occur. • If the movement is too fast, there might not be enough time to make a correction. • Makes movements appear open-loop

23. W A

24. Closed-loop corrections • How do we know if a change that occurs during flight is a closed-loop correction? • If the calculation for the ‘correction’ is made before the movement is initialized, the frame of reference for the movement will be off: E E E S S S Error Closed-Loop, in-flight correction open-Loop, pre-flight ‘correction’ Correct

25. Motor Programs & Schema • Feedback for well-learned skills • Feedback is not visual, but proprioceptive or vestibular (motor senses) • tying shoe laces • balancing while walking • The pattern of desired muscle movements is stored in LTM and occur as an open-loop motor program. • Motor programs are relatively automatic and therefore require little attention • Riding a bicycle

26. Motor Programs • Single Response Selection • Each program is considered a single reponse • i.e., although the program consists of many intricate muscle movements, only the program itself is considered a single action • Each decision is considered a single action • i.e. “Shift from 2nd to 3rd gear” consists of 3 movements: • forward > right > forward

27. Motor Programs • Single Response Selection • Because each motor program is considered a single response, the latency for launching the program is unaffected by program complexity. • However, the complexity of making the decision does affect latency.

28. Motor Schemas • Schemas • Motor program implies a very consistent series of muscle movements. • What appears to be consistent is the result of the movements, rather than the actual movements themselves. • Example: • signing your signature on a paper placed on a desk vs. a clipboard on a wall. • Both produce similar outcomes, but the patterns of muscle movements are quite different

29. Motor Schemas • Schemas • This suggests that what is being recalled from LTM is not an exact set of muscle commands, but a generic process in how to produce correct result. • i.e. what is stored in LTM is how the ballistic problem can be solved. • analogous to the equations for coming up with a firing solution. • “given these different variables, what adjustments do I need to make to come up with the correct result?”

30. The Tracking Loop • Command input i(t) – task demands, curve of road, item that needs to be tracked • Display e(t) – display of information, perception • Human Operator f(t) – decision maker, controller • Control u(t) – joystick, steering wheel • Systrem o(t) – output from machine, steering rack movements, elevators & flaps. i(t) o(t) e(t) f(t) u(t) Display Operator Control System

31. Transfer Functions • Transfer Functions • Represent the mathematical relationship between input and output of the system. • In the case of humans, we care about u(t), or the position of the control. • That is, the result of the muscle output (signature), rather than the muscle outputs themselves. • Control Order • The number of integrations performed by the transfer function.

32. Two Parameters • Gain • degree of amplification (multiplication) • Ratio of input to output • Pure Gain • High-gain: sports car steering • Low-gain: moving van

33. Two Parameters • Lag • Pure Time Delay • Output is delayed, but otherwise unaltered • Example - simulators and video games have an inherent time delay(computations, refresh rate)

34. Two Parameters • Lag • Exponential Lag • Lag is not pure delay • Result appears to “home in” on final result • Early power-steering systems

35. ‘Order’ and controls • Zero Order (Position controller) • Example: computer mouse or graphics tablet • Types: • Pure gain - (mouse speed in control panel) • Pure time delay - (refresh rate and processing time) • Exponential lag • Good For • Systems designed to control position precisely • Controllers w/o a natural zero-point

36. ‘Order’ and controls • 1st Order (Velocity controller) • Single integration over time • Example: car steering wheel • Angle of wheel affects the speed at which the heading changes. • Angle of wheel determines the change in heading, not the heading itself. • If the wheel is cocked 5 degrees (assume wheels are cocked 5 degrees) • Then the heading will change 5 degrees with each passing unit of time • So, heading constantly changing, even though control remains in a constant position.

37. ‘Order’ and controls • Good For • Systems designed to control velocity (angular, in the case of steering a car) • Controllers w/ a natural zero-point • joysticks, steering wheels, etc.

38. ‘Order’ and controls • 2nd Order (Acceleration controller) • Example: car brakes or gas pedal • Input displacement is equal to acceleration • A brake pedal press 25% will slow the car down less than when it is pressed 75% of it’s travel. • Because of inherent inertia in the system, tracking tends to be unstable, often producing oscillations. • Not a big deal in the case of braking, because the braking point is constant and not a moving target.

39. Human limits in tracking • Processing time • When performing tracking, there is always some time delay between when an error occurs and a response is made. • Lags are shortest for 0-th and 1st-order systems (150-300 ms), and longer for 2nd-order (400-500). • These delays reflect the complexity of the error-correction decision. • Delays can lead to problems of instability, especially oscillatory behavior.

40. Lag and Oscillations • How overcorrecting oversteer leads to fishtailing 1 2 3 4 5 • Car begins to oversteer • Driver countersteers to the left • Car straightens, but driver still counter steering • Car fishtails in opposite direction • Driver corrects oversteer by countersteering to the right. • rinse, repeat

41. Human limits in tracking • Bandwidth • For unpredictable signals, the upper limit for human tracking is 1-2 Hz (up to 2 corrective decisions per second). • If signals are predictable, number of corrections increases to 2-3 Hz • Upper-limit of Hz limited by the speed at which responses can be generated. • e.g. simple RTs (go no-go) less than 100 msec are considered an error • Choice RTs take longer than simple RTs

42. Human limits in tracking • Prediction and anticipation • When there is a lag in system output, people must anticipate the resulting error. • Example: steering a ship (or a really overloaded shopping cart at Home Depot). • Inertia means that there is a lag before the inputs lead to a course correction.

43. Human limits in tracking • Prediction and anticipation • Inertia means that there is a lag before the inputs lead to a course correction. • So, the operator must make inputs now based on future errors. • Since predictability goes down as lag increases, errors increase. • In addition, humans are poor at perceiving changes in velocity (acceleration) and acceleration (degree of acceleration).

44. Effects of Tracking Displays • Preview • Goal is to eliminate effects of lags or higher-order systems • Preview displays help to predict input • Example: on a clear day, the driver can see the road winding ahead and has several seconds of preview • On a foggy or rainy day, that preview is reduced • The greater the preview (of future demands) the more accurate the tracking.

45. Effects of Tracking Displays • Output Prediction and Quickening • Predictive displays: • Computer estimates future position and adds a symbology to the display representing this future position (prediction span) • Quickened displays • Same as predictive, but w/o current position

46. Future flight path based on current inputs and system status