Hello 
everyone welcome to the lecture series on
basic cognitive processes I am Dr. R karma
from IIT Kanpur.
Today we are going to talk about signal detection
theory as you know in the last lectures we
have been talking about sensation and perception
we have been talking about, how to measure
sensation in one of the earlier lectures I
have told you about classical psycho physics
and how it has been used to measure elements
of sensation we have talked about quite a
few methods most of which come under classical
psychophysical theory in order to determine
concepts like absolute threshold different
threshold etc.
We have seen that these processes basically
help us identify when a person is feeling
a particular sensation and how that sensation
can be quantified using some of the methods
like the method of adjustment or the method
of constant stimuli etc.
Today I will be talking to you about a particular
theory which kind of diverges its approach
towards measuring sensation this particular
theory is called signal detection theory.
We will see some of the merits and maybe you
know how this theory is slightly different
from other class classical psychophysical
methods, now we have been talking about the
importance of thresholds is not it how did
you know how would it be if say for example
we do not need to determine threshold in the
first place can we do with something else
can we do with the you know related concept
which does not really merely you know depend
on determining at what point somebody perceives
a particular you know physical stimulus according
to the theory of signal detection our perception
in general Is controlled.
By evidence and decision processes so any
air you know stimulus in the environment can
be treated off as an evidence if for example
a particular array of light falling on the
retina is evidence of light you know that
is therein the external environment and you
have to take a decision whether the light
is let us say bright either for not or whether
the light whether there is any light at all
in the first place, so signal detection here
you basically you know assumes these processes
as a sum of both evidence which is you know
the property of the stimulus and decision
processes that are the property of the perceiver
a signal or a stimulus creates.
(Refer Slide Time: 02:33)
Evidence that depends on the intensity of
the signal and also the equity of the observer
say for example I asked you to distinguish
between two shapes partly this depends upon,
how different the two shapes are from each
other if it is you know a small line and some
very large lines next to it also it depends
on your ability to see the difference between
the two stimuli, so that is equity of you
equity of the perceiver or the observer both
of these factors interact to determine whether
you will give a yes response to the question
that things are different or whether you have
detected something.
They could also be other factors which determine
how you know you are going to respond to that
question say for example the willingness of
an observer to say yes maybe if you are not
very sure may be if the decision is slightly
you know valuable to make maybe you not say
yes, okay maybe you will wait for completely
more you know completely a convincing case
and more evidence to say yes these kind of
influences which determine whether you want
to say yes or not or your willingness to say
yes or not are called response biases these
response bias influences.
You know also include the payoff for being
accurate or the frequency of the signal and
so many other factors we talked about them
as we move ahead.
Look at this figure here you will see this
is basically how you know a theoretical representation
of what signal detection really means, you
can see that there are sensory systems here
there is a particular sensory module in the
brain and essentially module in the brain
kind of you know evaluates the evidence values
which is basically the intense visual stimulus
and those kind of things then you will see
that these evidence values feed on to a particular
box which is the decision model you know the
brain has to decide.
Whether to say different or not different
or whether to say detected or not detected
and in there you see a modulating factor is
the payoffs or the motivation or vigilance,
how alert you were if you were you know is
that when that signal was presented what is
the frequency of that signal does it happen
once in hundred times or does it happen let
us say 60 times 100 times okay this decision-making
module of the brain, actually you know leaves
on to the kind of responses that you end up
giving.
So this in nutshell is how signal detection
theory is really a you know approaches this
whole aspect of people detecting particular
person particular sensations, let us take
an example of the fact you know of the willingness
thing so for example imagine if your friend
has setup a blind date for you okay.
Now you have to really tell him by within
one hour or so whether you are interested
in going on that date or not, now you are
thinking now what could be the cause of such
a decision you know the maximum cost for you
know saying no to a blind date or saying,
yes to a blind date in that sense could be
let us say you waste an evening you know you
do not like the person you were set up with
and you did not like their behavior and stuff,
so maximum what will happen you based in evening
maybe you will say for example.
Not even like the food these course a slightly
lesser is not it and you know then the possible
benefits could be much higher say for example
if you like the person if the you know if
the two of you strike a chord and you know
an exciting evening happens, and many more
happen in the future so this kind of is cost
versus benefit analysis of this decision okay,
in such scenarios when the costs are slightly
lesser as compared to possible benefits people
basically you know highly favored are yes
strategy.
So what they will do is that they will kind
of you know evaluate what are the cost what
are the benefits if they find that the benefits
are slightly more than the course they will
actually go on with the yes response more
often than not this decision basically, if
you see now is based on the analysis of costs
and benefits because you do not really have
any information about the stimulus you do
not know about the girl it is a blind date
you do not know what else to take into account
that is one of doing it.
However if you have a high-cost decision to
make say for example if you have to say yes
or no to a marriage proposal that maybe one
of your parents have brought to you, now you
say if you do not know you know any information
about the girl you do not know you know what
is what does she look like what is the reduction
other things that you are going to consider
how do you do it then people have been found
to be very careful and very conservative in
scenarios where the possible benefits or you
know where the in third costs are much more
than the possible benefits interms of decision.
Theory most of us there in these kind of scenarios
a very conservative decision makers when costs
are higher relative to the benefits okay.
Now leaving aside this kind of a process let
us talk about the sensory processes here so
the sensory processes basically you know they
transmit a particular, value to the decision
making modules the decision processes if this
value is considerably higher the decision
is more likely to yield a yes response because
you have enough evidence to say yes okay,
about any decision about the fact that there
is light in the room about the fact that there
is a you know a particular kind of temperature
and thereon those kind of things obviously.
You evaluate the costs and benefits if this
value given by the sensory processes to the
decision making process is low the evidence
is less then basically what will happen is
that you are more likely to eat a no response
okay, once again take after taking into account
the costs and the benefits.
Now what determines the quality of this signal
you know what determines what kind of signal
that is coming in, signal detection theory
has two assumptions first is that it has used
that there is always noise present you know
there is always a disturbance that can be
confused with signals and is always present
whenever a human being attempts to you know
detect any kind of signal and this and this
the source of this noise could be anything
it could be environmental changes it could
be equipment changes maybe you are measuring
you know temperature.
For example you different kinds of thermometers
there will be some degree of error you know
this is concept of 0 error in the physical
measurement instruments it could be spontaneous
neural activity, because here the measurement
is not actually an external device the measurement
is you yourself maybe you are not attentive
enough at that point in time maybe you are
just slightly tired and so somebody is speaking
and you cannot really attend to it very you
know attentively say okay.
So in that sense it is very possible that
somebody is telling you something and you
kind of you just mind your mind wandered somewhere
and you missed the details of what was said
a lot of time that might happen, when somebody
is giving out the shopping list you kind of
you know slightly not concentrating and some
of the important ingredients are left out
and you know athlete that might lead to problems
there could be other sources of this kind
of you know as noise as well say for example
you know different kind of experimental manipulations
can be done with a receiver let us look at
one of this kind of experimental manipulation.
Imagine if you are sitting in a soundproof
booth wearing headphones you know it is a
soundproof booth you are just given headphones
and you have been asked to decide whether
you have heard a faint tone combined with
white noise or you only heard white noise
maybe it could be just a machine generated
tone like something like that and it is you
know mixed with white noise which is again
system generated noise does not have a lot
of meaning a trial might begin by presentation.
Of a flashlight that is to gain your attention
to get you ready then what you hear is a burst
of white noise which now may or may not contain
the faint tone signal, now you have to decide
whether this white noise contain that signal
or not you would say yes, if you think a tone
signal was present you would say no if you
think it was not.
Now signal detection theory in these kind
of scenarios assumes that any stimulus even
noise produces what is called a distribution
of evidence yeah there will be different points,
so each the evidence on each trial will be
just one point, but you actually go through
many such trials so there will be a distribution
of all of these you know points, so basically
what will happen and say for example also
since evidence will not be directly observed
that solution for stimulus trials and noise
trials a both will be hypothetical.
What you might have is that the evidence would
try for which only noise occurred will tend
to be small there will be less evidence there,
so that over many trials a hypothetical distribution
with a very small mean will use service if
you are trying to just draw a distribution
of the noise trials a very few trials had
noise a very small mean and a very small distribution
will be there if you think of trials where
noise and signal that is the faint tone work
both presented you will basically have a larger
distribution with a greater mean again form
the word many trials what you are still talking
about that experiment now.
So you will have two distributions one will
be the noise distribution then there will
be the signal plus noise distribution since
these two distributions way anyways overlap
somewhere in the middle, some values of evidence
will be slightly ambiguous you know those
are values where you are not really sure about
whether there was noise or whether there was
only noise or whether it was signal in the
noise as well here is what this distribution
might look like.
So you have a noise distribution you have
signal plus noise distribution and you have
a distance between the means of these two
distributions this distance basically is called
D ‘ which is basically your sensitivity
okay we talked about this very shortly.
So how do you decide you know whether there
was noise present or not you need to set some
criterion that beyond this point I will say
that yes the signal was present where before
this point as I say no the signal is not present,
so a criterion was therefore we set to determine
whether you will give ayes response or a no
response the position for this criterion is
basically set up by what is called the decision
process if the constant benefits analysis
kind of you know says favors a liberal decision
policy you know things like going on.
A blind date with somebody the criterion will
be set slightly further to the left and so
that most of the responses will use in yes
response most of the trials will lead in a
yes response if it is a conservative decision
for here, something very important the criterion
will move slightly towards the right and what
you will do is you will say move no responses
okay it basically depends on the value of
the decision.
So here is how you make an or really plot
the criterion you can move the criterion slightly
to the right to use more no responses like
you to the left to more yes responses, okay
this is this decision criteria is called ß
okay so it determines basically whether you
will make a yes response and we will make
a no response so if that is clear we can slightly
move further we can understand that you know
anyways any how this distribution turns up
there will be some errors of judgment you
know there will be some values where you not
really be clear if you detected the correct
signal for example this will be called a hit
if say for example if incorrectly.
Responded yes where there was no signal then
that scenario will be called a scenario of
false alarm, so if you are following a very
liberal decision-making side you are saying
yes to everything then what you what will
happen is you will have a lot of hits yes,
but you will have a lot of false alarms is
it because your tendency is to say yes to
most responses on the other hand if you follow
a conservative decision strategy will there
will be low number of fits and it will also
be low number of false alarms.
So you will probably have very few false alarms
but you will have a lot of misses as well
because you do not say yes when the signal
was there because you following you want to
be very really very sure of the presence of
the signal, now if you plot this you know
if you plot a function of hits as a function.
Of false alarms and as the criteria moves
from conservative to liberal we get a particular
figure this figure is basically known as the
receiver operating characteristic or the ROC
curve in this figure you can see and I will
just show you in a moment that both hits and
false alarms are in frequent are actually
infrequent.
At the lower left of the curve you can see
the figure here at the lower left side of
the curve but both hits and false alarms become
more and more frequent , if you move towards
the upper right side of this particular curve
okay so this is basically something which
tells you about the decision-making process
as well that whether you are following a conservative
process or a liberal process the slope of
this particular function will tell you two
things if there is a flat slope it will tell
you that.
You have been following a liberal decision-making
criterion if the slope is slightly steeper
usually it when you feel a conservative criterion,
that you have been you know having very few
hits but very few false alarms as well the
slope of this curve basically here such as
the ROC function is determined by a slope
of the line that is drawn as a tangent to
this curve and you can connect either of the
axis.
It might interact the x axis or the y axis
if it is the curve is too steep in probably
you know intersect with the y axis with the
x axis, if it is slightly flatter it kind
of you know intersect the y axis now the distance,
if you see this figure again.
The distance from the diagonal to this curve.
Tells us how far apart the noise and the signal
plus noise distributions are when these two
distributions are far apart they indicate
and indicating either more discerning will
signal or a more acute observer, so there
could be two things is not it either the signal
is very clear so that you can detect it all
the time either you very good at detecting
that signal, so whether it you know indicates
a very desirable signal or whether it indicates
a very acute observer the ROC curve moves
upward to the left.
Okay and away from the diagonal as shown by
the heavy ROC function you can see this one
here you can see one of these figures is slightly
higher dotted it's a slightly heavier line
the one at the top okay, when the signal is
less detectable or say for example the observer
is not very good at detecting that and the
distribution the distributions will be slightly
closer together so the ROC curve will move
slightly closer to the diagonals you can see
here.
That this will be slightly you know closer
to the diagonals which is the lighter ROC
function between C here so the D prime basically
is much smaller than the D prime in the earlier
part okay, so the Roc function basically tells
us about both it tells us about the sensory
processes you know that is the distance between
the signal plus noise and the noise and basically
are the noisy solutions it also tells us the
criteria which you have been following it
also tells us about the decision making us
which is ß.
Okay now what does this you know a signal
detection method or what does this theory
really have in for us okay one of the major
advantage of the signal detection methods
over a classical psychophysics psychophysical
procedures like we had in the last lecture.
Such as for example the method of limits is
that this ability to measure and quantify
both the sensitivity of the observer and the
responses, both can really be you know plotted
and figured out here I have not gone into
great detail about those calculations because
again you are just you know doing this an
introductory level you are not really going
into much more detail but those of you interested
that I can actually look into them and maybe
ask questions but this ability of really talking
about and quantifying.
Both sensitivity and response bias is really
important in many areas of Applied Psychology
say for example you know this ability to distinguish
between these two processes very important
I can take an example of say for example you
know if there is a you know if there is a
soldier at the border you know and it is basically
reading taking readings from that at all you
know whether a particular enemy is approaching
or whether enemy is not approaching basically
is a costly decision.
If the enemy is approaching and you miss it
you are kind of you know outputting everybody
else in danger if it is not the enemy and
you know shoot down somebody by mistake you
are still committing a grave crime so that's
kind of decision slightly expensive to make
so say for example if you are a doctor and
somebody comes and shows you there you know
x-ray report and you have to detect whether
there is a cancerous tumor present or not
it can again be very costly decision.
If you go by a very conservative study and
say yes there is tumor and you know there
is a poor patient who is come you are showing
him to you know a particular sort of buying
very expensive medicines etc.
Which will not need it in the first place
or if you actually follow a very conservative
stratagem you see no there is no tumor you
are fine and the person kind of and that ends
up if you are not dying because the tumor
was not diagnosed in time then also you are
committing a very grave mistake.
So in those kind of decisions you know in
those kind of scenarios signal detection kind
of methods are really very important and there
have they have been extensively used as well
I will take in example to a laboratory here,
say for example to determine how n lg6 work
clock in colleagues they conducted a number
of experiments on pain analgesia they wanted
to test whether how basically these analytics
like aspirin etc work.
So they basically decided to use signal detection
procedure instead of the classical psychophysics
methods and what they do was you know they
are basically in these experiments they use
something called a delimiter to evoke pain
by means of thermal stimulation, so it was
a instrument that was applied on the skin
a kind of you know delivered heat in some
sense and that could you know either lead
to pain or you know less pain or more pain
something like that initially Clark found
that analgesics such as aspirin reduced the
d-prime they reduce the.
Sensitivity of the observer which means the
drug basically you know is really reducing
or lowering down the equity of the sensory
system with the outcome being that you know
as the ability of the observer to distinguish
between painful and, non painful stimulus
is lowered down it might this kind of thing
might have its own benefits but then they
went on to investigate method play see booths
or acupuncture etc.
Altered d-prime or whether placebos or acupuncture
change the willingness of the participant
to report pain, in both these experiments
Clark found that Laci was an acupuncture basically
elevated the subjects of decision criterion,
so a stronger stimulation was needed for the
subject to say yes or for the subject to report
pain detection response now this actually
does not mean that Macy was an acupuncturist
cetera will not work but what they are doing
is they are working with a slightly different
method okay we are basically changing the
decision threshold okay they are not changing
the sensitivity of the observer the sensitivity
is still there but the decision threshold
is actually changed also drawing from earlier
work then by hardly increase 1952.
It was found that using methods was found
using methods of limits that suggestions if
you tell somebody the general you are not
feeling being a stuff like that also alters
absolute threshold, so given the kind of work
we saw just now a signal detection the method
used by Clark until if it is reasonable to
suppose that suggestion basically what it
did was it changed the absolute threshold
by altering the decision criterion of subject
so you are basically selecting to the person
that know you not be feeling pain know you
are not feeling.
Clean until the pain becomes unbearable for
the participant to actually report pain okay
so the same could be true for other kinds
of occurrences as well say, for example if
you have a knife observer somebody is not
being part of the experiment and you kind
of you know start this experiment with this
kind of participant you see that the naive
observers will have a very lower threshold
immediately say yes I felt faint I felt pain
something like that now this basically these
kind of things could not have been determined
using classical psychophysical methods.
You know you cannot know about decision criterion
etc.
Using classical psychophysical methods and
this in itself is a major advantage imagination
advantage of signal detection practice methods,
so coming to the close trying to sum up we
found out that signal detection.
The measures are a departure from the classical
psychophysical methods as they take into account
both the sensitivity of the observer and the
evidence provided by the stimulus and also
the decision-making processes, so it kind
of takes care of all of these three things
and they are better because they help us understand
the decision-making process of the participant
experiencing and reporting these sensations,
so this is basically you know the end of the
series about psychophysics in the next owner
to start talking about issues related to perception
thank.
