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Signal detection theory (Theory
--> percentage of htis and false alarms…
Signal detection theory
Theory
--> percentage of htis and false alarms depend on criterion
--> persons sensitivity can be shown by roc curve
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Probability Distribution
--> depends on subjective perceived loudness of subjects
--> sound intensity is always the same, just subjective experience changes
Loudness
= subjective hearing of the actual sound
—> shifts with attention for example or changes in autidtory system, even though intensity = always same!! #
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Perceived loudness
--> left = only noise
--> middle where overlap = equal
---------> difficult zone
--> right = high probability of Signal (+noise)
Middle part is where the difficulty arises!
--> equal probability of being just noise or noise + signal because their perceived loudness is the same
--> what choice is made here depends on CRITERION # # #
Criterion (Xc)
--> if perceived effect (signal) > than criterion (to the right of it) = "Yes I perceived
--> if perceived effect (signal < than cirterion (to the left of it) = "No i did not perceive"
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Conservative
--> positioned far in S+N curve
--> small hit rate (cause only little of S+N curve on right of criterion)
--> almost no false alarms (as no N curve on right of criterion)
--> high correct rejections
Neutral
--> positioned exactly in middle of overlap of N vs S+N
--> Hit and false alarm = equal (but on low side)
Distance (d`) between a persons peaks (of N and S+N) indicate sensitivity of person to stimulus and is reflected on ROC curve !!
D' = Zscore of Probability of hit - Zscore of probability Fasle positive
- -----> d' > 0 <-- good sensitivity
- -----> d' = 0 <-- N and S+N courves cpmpletely overlap, ppl just guessing
- -----> d' < 0 <-- good sensitivity but got instructions the wrong way round lol[
Increased by:
- making signal stronger
- signal curve moves further to the right)
- person more sensitive to signal
- reducing variability in noise
- overlap reduces and both distributions become more peaked :3 !!) eg. see course manual
criticism:
--> the curves are never the same cause variation / spread si different
D' = seperation (actual d'calculation) /spread
receiver operator characteristic (ROC)
--> used to make sure the signal is perceived the same by everyone / everyone equally sensitive to signal
--> persons sensitivity to signal is shown by ROC curve shape
Created by adjusting participants criterion through payoffs/rewards!!
--> adjusted 3 ways
--> level of signal = kept same
--> if not adjusted, differences in test results migh tjsut be because different criterions or differences in focus of attention or ear adjustements! (see above under loudness)
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equal reward for all (hit, miss, false alarm, correct rejection)
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Sensitivity example
Low sensitivity
High sensitivity
BETA (wickens ereader)
--> Height / value of the probability distributions of the Noise and Noise + Signal distribution at the CRITERION :3 !!
--> information about strategy choosen
--> depends on Criterion #
Conservative = Bigger beta for (S+N)
--> smaller for Noise (cause criterion moved far into S+N curve , so N curve is probably just the small end of the curve of the N curve, so the height is low)
Liberal = small beta for (S+N)
--> bigger beta for Noise (cause criterion far to the left in noise curve, so the height of noise curve is high, while only the slope of (S+N) curve touches it, thus lower hight for it :3 !!
ratio of probabilities of noise and signal plus noise at the criterion ... but check again cause better in different words :3 !!
--> hight of criterion for S+N vs height of criterion for just N (noise)
Ratio between the heights = Beta
y(zscore of probability of hit) / y (zscore of probability of false alarm)
---> actually calculation ratio of hit/false alarm :D!! (thing statistics about ratios/odds :)!!
= same as
y(zscore of signal + noise) / y(zscore of probability noise)
Optimum BETA = (correct rejections + false alarms / hits + miss) * (1- probability of signal / probability of signal :3 !! )
SO can also be like this (correct rejections + false alarms / hits + miss) * (1- probability of hit / probability of hit :3 !! )
or like this: (correct rejections + false alarms / hits + miss) * (Noise / probability of signal :3 !!)
--> 1-Signal ; is same as 1- probability of hit ; is same as probability of false alarms ; is same as N; (N = Noise )
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Terms
Responders
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liberal
--> says detection even if unsure
--> lots of hits (cause low threshold for saying yes)
--> lots false positives / alarms
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Outcomes
Hit
--> saying yes if stimulus present
--> probability = Hit / total signals presented
#
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false alarm
--> saying yes when no stimulus present
--> caused by noise #
--> probability = false alarms / number of no signal trials #
correct rejection
--> correctly saying no, when no stimulus there
Evidence (wickens ereader)
--> whatever the person perceives, perceived value of the signal / stimulus / how strongy tehy perceive ti --> depends on many things like attention, changes in auditory system #-
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