Signal Detection Theory Flashcards
what does signal detection theory allow us to predict
how the observer’s criterion, motivation, boredom, etc affect the detection of a stimulus
what is the assumption of the signal detection theory
that there is inherent noise in the visual system
-noise is the randomly fluctuating neuronal activity in the absence of a stimulus (background noise)
signal detection theory assumes that the stimulus a subject is trying to detect is….
always being detected in the presence of noise
-noise is not a problem most of the time but conditions near threshold (ex. weak stimulus) can severely affect the detectability of a stimulus
what is detectability (d’)
the difference btwn the mean values of noise and noise+signal divided by the common std deviation of the 2 distributions
-if d’ is large there is no difficulty detecting a stimulus
by setting a low criterion, your hit rate is ____, miss rate is ______, false alarm rate is _____, and correct rejections _______
hit rate high
miss rate low
false alarm high
correct rejection in btwn
setting a high criterion, your correct rejections is ____, miss rate is ______, false alarm rate ______, hit rate ______
correct rejection is high
high miss rate
low false alarm rate
hit rate in btwn
in the very lax, lax, strict, and very strict criteriorn: how are hit rate and false alarm rate related
very lax: hit rate is very high, false alarm rate veyr hihg
lax: hit rate is very hihg, fals alarm rate is high
strict: hit rate is high, false alarm rate is low
very strict: hit rate is very low, false alarm rate is very low
you can caluclate the hit and false alarm rate for different criterion levels and create a _____ curve
receiver operating characteristic curve (ROC)
the ROC curve is specific to what
a certain d’ value
ex. depends on the detectabilty btwn the noise and noise+signal distributions
what do we have if d’=0
how is the ROC curve
how are false alarm and hit rate related
very weak stimulus and N+S overlap completely
- gives a linear ROC curve
- false alarm rate and hit rate increase linearly, are equal for all criterion values
if the d’ is small but > 0
substatial overlap btwn distri but there is enough neural activity to separate them slightly, stimulus produces a signal just above noise level
- for strict crit: both the hit rate and false alarm rate are bow but hit rate is lsightly higher
- for lax: hit rate and false alarm rate high
if d’ is large what happens
no overlap btwn N and N+S
-hit rate increases substantially before the false alarm rate increases (low)
how do prey animals se ttheir criterion levels
very lax
-regonize a predator but have a very high false alarm rate
what is the likelihood ratio
beta=stimulus freq/1-stimulus freq x $CR-$FA/$H-$M
by showing the same payoff matrix to the subjects you know….
- you have the same bias throughout the trials
- all the subjects will try and set the same criterion levle and therefore you will have a consistence bias amont subjects