IOE 333 - signal detection

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  1. Signal Detection
    When a target is located it becomes necessary to detect (confirm) whether the target is an item of interest (signal) or not (noise)
  2. Signal
    the relevant, important information
  3. Noise
    all stimuli, whether relevant or not
  4. Operator's goal
    Detect signal from noise (using his/her visual, auditory or tactile skills)
  5. Uncertainty in whether signal is present is cause by?
    • Noise in the world (bottom-up processing)
    • Prior knowledge, expectancies, biases (top-down processing)
  6. Signal Detection Theory
    Model of how humans separate "noise" from "signal". Humans collect observation (using senses) then determine if there is enough evidence to constitute a signal.
  7. Visual Inspection
    • Safety Inspection: searching a crack in airplane
    • Medical diagnosis
    • Airport baggage check
    • Manufacturing products that meet company requirements
    • Photo processing (red eye)
  8. Haptic (touch)
    • Medical diagnosis: fever
    • Vibration sensors: vibration of car wheel
  9. Olfactory (smell)
    • Is something burning
    • Gas leak
    • Something is rotten
  10. Assumptions in SDT
    • probability of observing signal or noise is normally distributed, random noise
    • signal increases the mean of the distribution
  11. 2 x 2 matrix, explain hit, false alarm, correct rejection, miss
    • hit: detect / is signal
    • miss: dont detect / is signal
    • false alarm: detect / not signal
    • correct rejection: dont detect / not signal
  12. Decison Criterion
    • yes: hit or false alarm 
    • no: correct rejection or miss
  13. Sensitivity (d')
    • how good an operator is at separating signal from noise (bottom up)
    • d' = z(H) - z(FA)
  14. What influences sensitivity (signal detection performance)
    • sound to noise ratio
    • redundancy of signal
    • operator alertness and motivation
    • cost of false alarms or misses
  15. want to reduce overlap in the distribution of signal and noise?
    • increase intensity of signal (better and stronger evidence in a criminal case)
    • Improve ability of observer (training)
  16. Response Criteria, Bias (beta)
    Bias of the operator to respond "yes" or "no" to signal due to expectancy, payoffs, incentives, experiences (top-down)

    Beta = Ordinary (Z(P(hit)) / Ordinary (Z(P(FA)))
  17. Liberal vs Conservative
    • Liberal - prone to say signal (risky) tendency to left
    • Conservative - not prone to say signal (safe) tendency to the left

    This impact which type of error there will be either more FA or more misses
  18. Neutral?
    When beta is 1. There is an equal amount of misses and false alarms.

    Intersection of distributions of signals and noise is exactly the probability of correct rejection = prob of hits
  19. Risky
    • B<1, more false alarms than misses.
    • Probability of correct rejection > probability of hits
  20. Conservative
    • B>1, more misses than false alarms 
    • P(hits)>P(correct rejections
  21. Optimal Beta
    Possible to set the optimal level of the RC, given cost, benefits, and signal probabilities

    Beta (opt) = P(Noise)/ P(signal) * (V(CR) + C(FA))/(V(hit)+C(miss))

    Where beta should be based on the existing ratio of S-N
  22. Sluggish beta
    Slow in asjusting our beta. Beta used by humans is not adjusted to changes in optimal beta.

    • Beta (opt) high, human beta is less risky
    • Beta (opt) low, human beta less conservative
  23. Sensitivity measure P(A) ~ d'
    P(A) = P(hit) +[1-P(FA)] / 2


    P(A) = P(hit) + P(CR) /2
  24. ROC curves
    increased discrimination (d') makes the curve more curved.
  25. Increase sensitivity
    • better training 
    • amplifying signal vs noise
Card Set:
IOE 333 - signal detection
2014-04-15 22:09:00
Signal Detection
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