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Selection of Action

Selection of Action. chapter 9. Announcements. Test next week Paper topic due next week. What is your topic? How will you analyze it cognitively?. Response Times. Two classes of RTs: Simple RT (aka Reaction Times) Choose whether to respond or not “Go No-go” tasks Example: drag racing

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Selection of Action

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  1. Selection of Action chapter 9

  2. Announcements • Test next week • Paper topic due next week. • What is your topic? • How will you analyze it cognitively?

  3. Response Times • Two classes of RTs: • Simple RT (aka Reaction Times) • Choose whether to respond or not • “Go No-go” tasks • Example: drag racing • Choice RT (aka Response Times) • Must choose which response to make • Example: avoiding an accident • Steer left? • Slam on brakes?

  4. Variables that influence All RTs • Stimulus Modality • Auditory is quicker than visual • Stimulus Intensity • RTs decrease as intensity increases • Accumulation model • Assumes that a decision is made once enough evidence has been accumulated. • Increasing the intensity increases the rate that information is transmitted, decreasing the time it takes to accumulate a satisfactory amount of evidence.

  5. Simple Accumulation Model

  6. Simple Accumulation w/noise

  7. Temporal Uncertainty • Terminology • Warning Signal • Informs that the Imperative stimulus will appear shortly. “Ready…Set…” • Imperative Stimulus • The stimulus that is responded to “GO!” • Warning Interval • The interval between the Warning Signal and the Imperative Stimulus

  8. Temporal Uncertainty • Temporal Uncertainty • The predictability of the warning interval • The more predictable the warning interval, the easier it is to focus attention on a specific time window. • If the imperative stimulus (go-signal) occurs during the expected time window, it is responded too faster. • However, if the warning interval is highly variable, people won’t be ready for the imperative stimulus and responses will be slowed.

  9. Temporal Uncertainty Attention increases rate of accumulation. That is, attention increases our sensitivity to a stimulus.  The more attention you pay to an interval in time, the more quickly you will accumulate evidence about the imperative stimulus, and the quicker you can come to a decision and make a response.

  10. Warning Interval Length • People aren’t good at judging time, and the longer the time, the greater the variability in the judgment. • Example: an exactly 5 second interval will show less variability in judgments than a 30 second interval.  the greater the warning interval, the greater the temporal uncertainty, and therefore the greater the RT.

  11. Warning Interval Length • Exceptions • Too short to prepare • If the warning interval is too short, people might not have enough time to prepare.

  12. Temporal Uncertainty Applications • Warning intervals that are too short might not give enough time to get ready. • e.g. Yellow traffic lights that are too short in duration • Example: • Intalling a red light camera did not reduce the incidents of violations at US50 & Fair Ridge. • However, increasing the yellow light duration from 4 sec to 5.5 decreased red light violations by 96%. • http://www.insurancejournal.com/magazines/southcentral/2002/06/24/partingshots/21771.htm • Warning intervals that are too long can lead to complacency • e.g. 30 second draw-bridge warning.

  13. Expectancy • The variability of warning intervals, on average, slows responses. • Even if the warning interval is “random”, people can still pick up on the pattern of randomness and use it to their advantage. • That is, the pattern of variability also affects RTs.

  14. Expectancy • Example: • If you run track, and you know that the regulations say that the gun must go off between 5 and 10 seconds after “set…” then you have some idea about the when the gun will go off.

  15. Expectancy • Let’s say that the probability that the gun will go off at any given time is equally distributed:

  16. What happens if at given time, the gun hasn’t gone off yet? • The probability that the gun will go off at the next moment increase! * probability that “Go!” will occur at time t given that it already hasn’t gone off by time t -1.

  17. Expectancy • Therefore, as time goes on and the window for the “Go!” event narrows down (you become more certain), you response times will actually speed up! • This function of certainty over time for when an event will occur (given that it hasn’t already) is known as a Hazard function.

  18. Probability Density Function • Probability that an event will happen at time t. • For the starter’s gun, the probability was flat. • That is, each time had an equal opportunity to be randomly chosen..

  19. Cumulative Distribution Function • The cumulative probability that an event will have happened by time t. • For the starters the gun must go off after 10 seconds, so that P() = 1 at that point. • Calculated by integrating PDF

  20. Hazard Function • The instantaneous probability that an event will happen at time t given that it already hasn’t happened. • For the starters the gun must go off after 10 seconds, so that P() = 1 at that point.

  21. Hazard Function • Increasing Hazard function • With increasing Hazard functions, you should be pay more attention towards the end of the warning interval because that has the least uncertainty. • Keep in mind, that the end might not be the most likely time for the imperative stimulus to occur, but the end will have the least uncertainty. • Flat Hazard Functions • Uncertainty does not increase over time, therefore... • Attention should not increase over time.

  22. Famous Hazard Functions • Serial Self-Terminating Search • Each item is examined one at a time until the target is found. Items are never reinspected. • Sampling without replacement. • Flat PDF distribution • Identical to starter’s gun example. • After N-1 items, if you haven’t found the target yet (and you know it is present), you can be certain that the next item must be the target. • That is, P() of finding the target increases as more and more items are examined (if the target is there).

  23. Famous Hazard Functions • Memoryless search • Each item is examined one at a time until the target is found. Items are chosen at random, and there is no limitation in reinspecting items (i.e. no memory). • Sampling with replacement. • Exponential PDF distribution • Since you are sampling with replacement, the probability that you will randomly stumble across a target does not increase as time goes on.

  24. “Visual search has memory”, Peterson et al., Psychological Science, 2001in reply to Horowitz and Wolfe’s “Visual search has no memory”, Nature, 1998

  25. Applications of Hazard Functions • Drag Racing • Use an exponentially distributed Warning Interval to prevent jump starts. • Engineering Reality: there is a real probability that the warning interval could last forever (or at least a really long time!) Engines could overheat, fans could get restless… • Solution: Use catchtrials in which the light does not turn green.

  26. Applications of Hazard Functions • Stop Light • Goal is to prevent people from running red lights. • Make Warning Interval (how long the yellow light stays on) a flat and narrow distribution. • Maximizes certainty about when the light will turn from yellow to red. • Unexpected Events • Truly rare events have high uncertainty, and therefore are responded to more slowly. • Example: I had a squirrel fall out of a tree once and into the path of my car.

  27. Things that influence Choice-RT only • Hick-Hyman Law • RTs are a function of the amount of stimulus information needed to make a decision. • As the number of alternative choices increases, so does the amount of information, and therefore RTs . • As number of alternative , RTs increase at a negatively accelerating rate.

  28. Things that influence Choice-RT only • Hick-Hyman Law • As number of alternative , RTs  at a negatively accelerating rate.

  29. Things that influence Choice-RT only • But, as amount of information increases linearly, RTs also increase linearly. • Information = log2(alternatives)

  30. Things that influence Choice-RT only • Things that influence the amount of information also influence RTs • Probability • Low(rare): lots of information, slow responses • High(common): less information, faster responses

  31. Things that influence Choice-RT only • Speed-Accuracy Trade-off • People can adjust their criterion for how much evidence they need before responding. • If they set their criterion too liberal, they will need less information and respond more quickly, but many of those responses will be errors. • That is, by adjusting their criterion, people can trade-off accuracy for speed.

  32. Random Walk • Evidence is accumulated over time, and a decision is made when when enough information is accumulated.

  33. Random Walk • Lowering your criterion will lead to faster responses, but increases likelihood of errors. Lower decision criterion (barrier) leads to faster responses.

  34. Random Walk • Lowering your criterion will lead to faster responses, but increases likelihood of errors. The lower the decision criterion (barrier), the more likely an error will occur.

  35. 100% Accuracy Chance Response Time Speed-Accuracy Operating Characteristic Fast & Accurate Sloppy & Slow

  36. Speed-Accuracy Operating Characteristic • Trade-off • Speed and accuracy can very along a single curve. • The person can choose to be fast and sloppy or slow and accurate (or somewhere in between) • Between curves • A person might be better at one task than another. • That is, a person might be good (fast and accurate) in one task and do more poorly in another task (slow and sloppy).

  37. Micro trade-offs • Fast Guess • When the stimuli are highly salient • Responding before an adequate amount of information has been accumulated. • Random-walk example / Speed-Accuracy Trade-off. • Errors faster than correct responses • Slow Guess • When saliency is low or stims are difficult to process. • If the correct answer isn’t readily apparent, people give up and guess. • Errors are slower than correct responses.

  38. Conclusions • RTs affected by: • modality • rate of information accrual • decision bias • expectancy (attention) • Expectancy affected by • warning interval length • experience with the variability in the warning interval • rarity of the event

  39. Departures from Information Theory • Stimulus Discriminability • The more similar stimuli are to each other, the longer the RT. • Example: • vs: o  X  L L T L T

  40. Um…how much gas do I have left?

  41. Departures from Information Theory • Repetition Effect • When the same answer occurs twice in a row, the 2nd response is faster. • Why? Recently priming of pathways used for response. • Exceptions: • Long Delays: the same response may be slower – Gambler’s fallacy: it’s unlikely for too many identical events to occur in a row. • Rapid response with same finger – there is a refractory period. If the delay is too short, the finger might not have recovered in time.

  42. Departures from Information Theory • Response Factors • Effects of confusability • using different fingers on the same hand is slower than using fingers on different hands. • Controls with different shapes and feel are less likely to be confused:

  43. How do you change the channel?

  44. Nuclear powerplant controls

  45. Departures from Information Theory • Response Factors • Effects of Complexity • The more complex the response, the slower the response. • Latency to initiate typing a word is slower than latency for a single button press.

  46. Departures from Information Theory • Practice • The more highly practiced, the quicker the response • automaticity takes over • the more difficult the task, the more it benefits from practice: • Example: steering (small benefit from practice) manual shifting (large benefit f/ practice)

  47. Departures from Information Theory • Task Switching (Executive Control) • When switching from one task to another, it takes some time to become ready for the new task. • That is, before a new task can be worked on, the rules for the task must be “loaded”

  48. Task Switching How many numerals? Which numeral? 1113333333 1333

  49. Stages in RT • Subtraction Method (Donders) • Each time you add another cognitive function to a task, RTs should increase by the amount of time it takes to perform that function. • Example: • Try to find a white car in a parking lot full of white cars. As the number of distractors (cars) increases, so does search time… • Search time is the aggregate of all of the individual examinations.

  50. Stages in RT • Now try to find a red car in a parking lot full of white cars. Task is much easier. Donder’s assumption does not hold…

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