IOE 333 - signal detection
Card Set Information
IOE 333 - signal detection
Ergonomics Final Exam Studyguide
When a target is located it becomes necessary to detect (confirm) whether the target is an item of interest (signal) or not (noise)
the relevant, important information
all stimuli, whether relevant or not
Detect signal from noise (using his/her visual, auditory or tactile skills)
Uncertainty in whether signal is present is cause by?
Noise in the world (bottom-up processing)
Prior knowledge, expectancies, biases (top-down processing)
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.
: searching a crack in airplane
Airport baggage check
Manufacturing products that meet company requirements
Photo processing (red eye)
: vibration of car wheel
Is something burning
Something is rotten
Assumptions in SDT
probability of observing signal or noise is normally distributed, random noise
signal increases the mean of the distribution
2 x 2 matrix, explain hit, false alarm, correct rejection, miss
: detect / is signal
: dont detect / is signal
: detect / not signal
: dont detect / not signal
: hit or false alarm
: correct rejection or miss
how good an operator is at separating signal from noise (bottom up)
d' = z(H) - z(FA)
What influences sensitivity (signal detection performance)
sound to noise ratio
redundancy of signal
operator alertness and motivation
cost of false alarms or misses
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)
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)))
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
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
B<1, more false alarms than misses.
Probability of correct rejection > probability of hits
B>1, more misses than false alarms
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
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
Sensitivity measure P(A) ~ d'
P(A) = P(hit) +[1-P(FA)] / 2
P(A) = P(hit) + P(CR) /2
increased discrimination (d') makes the curve more curved.
amplifying signal vs noise