raw_transcripts/lecture_9.txt

All right.

Good morning.

You know, I have a mini stroke or did I

say some names outside before anyone else say some names?

Oh, no.

No.

Okay.

Maybe I just had a mini stroke.

Okay.

I'm just trying to work out what happened there.

So it's really my pleasure to raise a volume.

So.

Is that better?

I can't tell from down here.

Okay.

It's my pleasure to try and take you through this

today.

When I was a young graduate student, actually, this field

had just started.

And one of the papers I'll introduce you to.

Was a real key moment in that development.

I remember talking to one of the authors of that

paper and.

Been blown away by the idea that perhaps we could

track.

The evolution of a decision in the brain.

I think we think it's fairly commonplace to look at

this now, but back then this seemed like there's something

beyond.

Our abilities.

So in the last couple of lectures, what I hope

I hope you understand is that signals from the senses,

like the eye I communicated to the brain along parallel

pathways of showing you that these signals are represented in

early brain areas, the primary cortical areas, for example, in

the form of topographic maps of the sensory periphery.

I'm telling you also the higher brain areas are called

high brain areas seem to transform those topographic maps of

the century periphery into frames or reference frames in which

we can make actions frames that are behaviour useful more

so those now probable graphic maps.

I've also shown you to some degree that Apple Motor

areas can use these spatial representations to guide movement bands.

So what we skipped over and what is still really

one of the fundamental unknowns about neuroscience is what's in

between these two things.

I illustrated to you that there are some cells that

we will call sensory motor neurones that give both sensory

input and have motor related glands activity.

They seem to be particularly important in this process.

And in this lecture we'll go through a particular class,

those neurones that sit in this whole area of the

brain called the lateral imprint area, which we discovered a

little bit in the last lecture.

And the question really is how do we decide which

action to execute?

There are many different actions we could possibly execute.

How do we decide among these options?

What I want to try and introduce you to is

the idea that we can actually practice evolution decisions.

We still don't know how we do decide, I should

say, but we're getting closer and closer to understanding that

fundamental point about behaviour.

To try and illustrate this, we have to settle on

a on a definition of a decision that we can

actually explore experimentally.

And I'm just going to take you through that in

the next couple of slides.

There are many different ways we can think about decisions,

but we need to think about ones in which we

can try and work out what is the neural basis

of this decision.

This slide represents two possible kinds of decisions that you

might make by commonly in the top in going to

a restaurant, in this case a pizza restaurant, you look

at the menu, you're trying to work out what it

is you would like to eat.

You surveyed the different options you take into account your

previous experience and biases.

You look for evidence in the form of the different

ingredients in the in the menu.

For example, you deliberate about what it is you would

like.

I'm asking my mom, in which case you just get

the same thing every time.

You learn what's available.

Understand the differences and deliberate.

This is a decision that we could perhaps explore.

Similarly, if you're a goalkeeper in football and it's your

job to try and save a penalty, then that task

to try and stop that ball going into the goal,

by the way, it takes about 0.3 of a second

for the ball to get from the penalty spot past

the goalkeeper, but not very long at all.

Your task is to try and evaluate the sensory evidence

or maybe the pattern of steps that kick the ball

is going to is using the direction that they're coming

from.

Maybe analyse that, that that particular player before and you've

worked out that they like to kick it up in

the top right of the net.

You have to try and make a rapid a very

rapid decision.

You have to execute that decision within about 50 or

80 milliseconds of holding it very, very quickly and strongly

that the evidence you have for the hypothesis that you

are testing it quickly to make that decision.

So these are two forms of decision, the latter where

there's very little action leaping left or leaping right or

staying put is an easier one to study in the

context of operating systems and the kind of decision that

we're going to try and explore in this lecture.

So a definition of the kinds of decisions that I

would like to use is that a decision, is a

commitment to a proposition or the selection of an action,

a process that results in the overt act of choosing

based on evidence of prior knowledge and belief.

Overt is easier.

Is a decisions that we can look at objectively rather

than just subjectively.

As Jeffrey saw, one of the pioneers of this field

is puts it where choice refers directly to the final

commitment, the one among the alternative actions, decisions referred most

directly to the deliberation preceding the action.

So it's not the actual action we choose, but the

process of deliberating among the possible actions that we undertake.

Or as Golden seven.

Mike Catlin is another leader, as is his former.

First of all, decision is the deliberative process that results

in the commitment to categorical propositions that the right an

apt analogy for judge or jury that may take time

to weigh evidence for Internet of interpretations and or possible

ramifications before settling on a verdict.

In these kinds of definitions is that these decisions take

time.

You accumulate evidence and you take time to make them

be deliberate.

And the fact that we're taking time to make them

means that we can look for the signals of that

process in the brain.

If there was no time in which you needed to

make that decision, you wouldn't know what to look for

in the brain.

But it takes time to accumulate evidence, and that's something

we can can't find.

So this is one way of trying to think about

the basic components of most of these kinds of decisions.

On the top is the context in which the decision

is being made.

On the bottom is kind of the architecture of the

process that helps that decision to be made.

For example, you may need a task and motivation and

will get to a very specific task and motivation in

the moment.

You need to generate hypotheses about the world.

You need to incorporate your beliefs and prior knowledge.

That's all the context of the task.

And the context of this kind of sensory guided motor

actions and the kinds of decisions that we're going to

make require you to take some sensory input, evaluate it,

transform that sensory input into a useful form of evidence,

something that you can act upon.

You need to generate what we will call a decision

variable.

That is a point at which after which you have

committed to a particular decision.

We'll go through this in the next episode.

You need to apply that decision rule that you need

to execute the motor out.

I challenge you to describe decisions and reforms that don't

fit into this general framework.

It doesn't cover every type of decision, but most of

these things are the key component that you think, well,

one thing that shouldn't fail.

And we're going to try and fill out these boxes

in terms of specific tasks that an animal or human

might do and which they have done for the last

20 years, ad infinitum.

So what I hope we will discover in the sector

is that humans and other animals accumulate evidence for decision

over time.

That decision is made when the accumulated evidence reaches a

criterion.

Consequently, harder decisions take longer to get right.

That by adjusting bias in criterion, we can change the

process of decision making from being safe and slow growing

fast.

And I'm going to show you that there's your signatures.

That is new activity that reflects these different parts of

the process and that you see this is a key

way that evidence can be identified early in the processing

hierarchy.

And and actually, Frank, Larry, as we discover among neurones

that both respond to sensory stimuli and predict motor outputs,

that is sensory motor neurones, these are the kinds of

neurones we explored in IP.

I said to, for example, that respond in both the

sensory stimulus that was being shown to the animal and

predicted the motor action that they were got from the.

So most of this work, especially in the early stages,

has been conducted in the context of moving your eyes.

This is something we do 2 to 3 times every

second of every day that we are waiting.

We move our eyes around.

We make seconds.

These are ballistic eye movements, very rapid eye movements that

move eye around so that we can bring some kind

of growth onto the central region of our eye over

here.

And we bring it on to the central region of

the eye, because that's where the high intensity of odour

receptors are, and that's where we would like to analyse

the visual image.

So we were very good at making these eye movements.

We make them very rapidly and we make them very

frequently.

There's a lot of the brains devoted to trying to

make them in the most optimal way possible.

This is one example of a kind of pass that

a monkey might do or a human might do in

trying to search the city.

Here the ice starts off in the centre of the

image.

Their task is simply to find out, see and to

look at that to.

Now, when you look at the centre of these things

in the periphery, they're quite hard to detect.

It's very hard to tell whether something is an Al

or a T when they can be repeated in whichever

way.

And so monkeys tend to enter humans and make eye

movements to see sequentially surveil the image.

And so these little movements between each of these objects

are these two kinds.

And in this case, the monkey eventually finds that he,

in fact, actually makes an eye movement away from the

team and come back to the team.

So these are these are the kinds of movements you

make all the day when you're reading, for example, or

looking at someone's face.

My kids do it as well, and they're very good

at it just as we are.

That in itself is still a quite a hard path

to try and pick apart because the eye is moving

around all over the place.

There's many different potential stimuli that could be present.

It's still pretty tricky.

We need to find simplify that task even further.

And a lot of the field over the last ten

years is settled on this equal task and you will

encounter it a lot in the literature.

So I want to take you through the task a

little bit.

It is still a decision making process.

There will be some sensory input.

There will be a motor output and there is some

decision making process in between those two things.

However, the task in this case is going to be.

Estimate which way this field of random dots will see

them in a second reading and then make an eye

movement accordingly.

So the dots are moving to the left, making eye

movement to the left.

The dots are moving to the right, making eye movement

to the right.

That is the simplest possible task with moments involved.

So an animal or a human can be looking at

an evening monitor to see a field of dots on

a moving and then have two potential outputs.

Move left and move right.

And look at these two different dots.

And indeed, after the dance, the emotion seems has gone

away.

They're allowed to move their eyes and make that appropriate

decision.

This is perhaps the simplest task we can look at,

and it has proved enormously fruitful to understand the very

basic aspects of decision making.

So if you go back to this general context of

what a decision might look like and then we try

to put this past in that in that framework, you

get something like the of the animal, the animal or

the human.

The task is simply are the dots moving left or

right?

And the motivation for animals is reward often also the

motivation for humans.

Amazon.

Humans generate two hypotheses on the basis of the sensory

evidence hypothesis.

One dog moving that told to the moving right.

And I can bring in to this task, although we

won't discuss it equally here.

They believe some families set up example.

You could train and even or train a monkey on

a task such that the you over represent the probability

of the adults moving right and perhaps the monkey will

learn or even will learn that it's more likely to

go right.

That might be a prior belief.

That's not the general structure of these parts, but that's

something you might do to fiddle with the context and

biases, and I will bring it into a decision.

So that's the context of the past.

The decision making process is to analyse that visual motion,

which direction of the dots going.

Then transform that into a useful form of evidence generating

decision variable, apply a decision rule and then move your

eyes to the left or the right, and the path

to the next few slides is fine.

Go through this.

To be able to do that.

I need to tell you a couple of little things

about how we represent motion in the brain digital motion.

Then we're going to use this task, these dots that

you'll see moving into the next slide.

They've become very common.

The reason these dots are so common in these parts

is there are a large field of dots that can

move.

Either every dot moves to the left, for example, to

the right together.

That would we would call that a 100% coherence.

All the dots are moving in the same direction.

You can see that represented on the right here for

each of those dots as a little arrow and they're

all going in the same direction.

This is actually output Explorer.

So all adults can move together or only some of

the dots can move.

So you might vary the fraction moving in a particular

direction or if example, maybe 50% of the dots open

it up automatically.

Right.

And the other 50% adults.

Those dots are moving randomly.

And so those dots, they provide noise.

So by bearing the number of dots in a moving

in the same direction, we can vary the signal to

noise ratio with English.

This is very important.

Some decisions which are very easy to do and this

is very easy.

For example, 100% coherence are actually really hard to do

when there's only a small amount of signalling there.

So by adding noise, we can make this decision harder

and we can track things over a longer period of

time, for example, and we can ask how animals or

humans accumulate the evidence that they're about right.

Now, these dots were developed as a stimulus to explore

the responses of neurones in a very particular part of

the brain called Area MP, sometimes referred to as B

five.

This area, which was first discovered by senators that he

who is emeritus professor at UCL and at the time

I think was working over in Queen Square in the

history of neurology.

E contemporaneously with some researchers in the US in the

early 1970s discovered this tiny little part of the brain

that is several millimetres in size in monkeys where every

neurone in that part of the brain seemed to be

selected for the direction of motion of a visual stimulus.

In Sammy's work, this was in contradiction.

Its most effective area, he found, next to an area

that seemed to be selected for colour.

He was describing how different types of as different information

about the outside world had been encoded by visual cortex.

He found areas that responsive to motion, areas that were

supposed to be responsive to colour by not fluid depth

and other forms of information, which you will only learn

about later.

But the purpose here, our interest is in this area

empty.

By the way, the doublet empty stands for a middle

temporal area because on some in some monkeys, this part

of the brain is found in the middle temporal area.

In mechanics and humans.

It's found in a small suitcase inside of the brain.

You can't really see it here.

It's actually this little bit here.

This little area gets direct input from V1, the primary

visual cortex.

It is one of the most highly conserved parts of

the brain in primates.

Every single primate seems to have this part of the

cortex dedicated to being selected for visual motion.

And if you measure it from neurones in that part

of the brain, you get something like this activity shown

here.

If you're using a very strong stimulus, understand coherent dots.

The neurones are very selective for the direction and motion

of those dots.

These dashes here are meant to indicate the time of

appearances and action potential on one file of the stimulus

and can see that these neurones respond very well when

these talks are all moving together in one direction.

They are tuned for the direction of motion.

That is, even if the dots are moving in another

direction, they aren't responsive.

And they're also sensitive to the signal to noise ratio.

That is, if there are fewer dogs moving in the

same direction, they become progressively weaker.

And their response?

These are neurones in every empty.

They are based on neurones that we discover in a

second.

They don't seem to communicate much information about what an

animal would do with that signal.

They just represent a visual motion in the outside world.

We study these neurones that 15 or 20 years ourselves

in our lab.

It's an amazing part of the brain to report from.

What you will see here is actually the kinds of

stimuli that are used in the lab with different it's

quite hard to represent these moving stimuli.

So videos only really work.

One project is a little bit you need a high

frame rate presentation device to see it properly.

You see hopefully dots moving in one direction or another

direction down to left or up to the right, and

the fraction of dogs that are moving in the same

direction varies and find the file.

Well, you can.

Here is actually this is a video taken lots of

monkeys doing this task.

You can hear all the audio tones which indicate when

the start and finish is the monkey.

And in the background you hear the action potentials of

cells and area and to.

This.

So those are the action potentials that you hear about

recording played through an amplifier so the experiment can listen

to them.

That, by the way, is not available for the monkey

to do in the past.

It's in a soundproof chamber outside of this room.

So you can see that those neurones in area and

they respond very well when the when the stimulus goes

up into the right side, down to the left and

not at all when it goes down to the right,

but different neurones in area MP will prefer different motion

directions, some down to the left, some often to the

right, some often to the left.

Some down to the right, for example.

The second part of the of this task, that's the

sensory information that's coming in is representing an area MP.

It's representing the action potential that neurones in that area

produce.

And it's possible to train a monkey to do this

task to detect which or to report sorry which direction

in motion these dots are going in.

And this was something that was accomplished first in the

late 1980s and now has become very sound parts of

monkeys and humans.

And you can see here that if we put on

the x axis, the number of the fraction of Dr.

moving coherently at 1% or 10% or 100%.

This is the proportion correct on an author's choice task

where the animal has to report to the left, to

the right, example or two up to the right and

down to the left.

That as those as the number of dots moving coherently

increases, so does the fraction of times that the animal

gets its decision.

Right.

These animals are highly motivated because they are actually water

regulated and they're working for juice.

They just have very well, I've tried to do this

task many times.

I'm nowhere near as good as these monkeys.

These monkeys are getting almost 100% correct, about ten or

20%.

That's moving in the same direction.

To me, it takes 30 or 40%, 0% for.

But his monkeys are highly motivated.

So this is what we would call a psychometric function.

It simply says that the monkey gets better as the

numbers don't move in the same direction also increases.

All right.

So I thought then about the fact that this area

empty, which seems to be responsive to these dots, provides

potentially action potentials or neural activity that they may represent

the motion direction of these dots.

And we know that the monkey can actually detect which

motion direction is going in.

So then between these two things, between the sensory input

and this motor output.

So now I need to tell you that one of

the reasons we're looking at Area A is it has

a not only does it have a strong input from

the primary visual cortex, it also has a strong apple

to this whole area of the frontal cortex over the

lateral impropriety area.

And so what researchers thought back in the late 1990s,

and this is when I started to see this field.

Since the neurones in their empty seem to be encoding

information about the visual stimulus, not about monkey's behaviour.

What we should do is look at one of the

areas, the area empty sensing.

In this case there might be and see whether or

not the same as bear.

Maybe those neurones that are getting input from these neurones

in MP are actually closer to the decision making process

than those neurones in area.

So if I was to summarise the activity of neurones

in every MP, looks like then as a function of

time you get something on the left.

This is a schematic.

When the stimulus comes on the activity, those neurones increases.

And when the stimulus turns off, the activity resume decreases.

So this neurone is responsive to the visual stimulus.

And the amplitude of this on the number of action

potentials a neurone produces depends on the emotion strength that

stimulus.

So there's only a few dots moving in the right

direction.

And if you continue to produce, if there are lots

of dots moving the wrong direction, a lot of attention

to produce.

So these neurones encode both the direction of motion and

the signal strength of the incoming stimulus.

In Aria Lippi, however, he finds something quite different.

Now when the stimulus comes on, instead of an immediate

change in the activity of these neurones, you get a

slow ramping up of activity.

The slope of that ramp seems depend on how much

signal there is in the visuals inlets.

And that activity is actually sustained even after the stimulus

is going on.

So these neurones are quite different to the sensory neurones

in our MP which are providing input to them.

They're not as closely linked to the onset of this

stimulus.

Their activity persists after the offset of the stimulus.

And in between, they seem to show this ramping behaviour,

this accumulation of activity from low levels to high levels.

That depends on the signal strength, the visual signal strength.

This is the same kind of way that we've shown

other cells in the previous lectures.

And I'll go through the presentation here again and I

can shows the activity of a real neurone.

An area like a when it's recorded from in this

kind of past.

It's quite a busy slide, so I'll just take you

through its early.

First of all, the monkey in this case is looking

at a screen very much like what we saw before.

There are some dots moving on that screen and there

are a couple of choice targets left or right, for

example.

And it turns out that if you measure from neurones

an area like this, you find a region of the

visual fields where these neurones tend to respond to the

visual stimulus and also a region that these people field

where neurones will predict an upcoming movement.

In fact.

So it's half the dots.

Come on.

And then they go off.

When they go off, the monkey has to make an

argument to the left or the right reporting which motion

direction in which Lincoln.

In these pictures down the bottom here.

Each of these rows represents a single file in which

the animals doing this asked each of those dots the

time of occurrence of a single action Santa Cruz 11

year on and the average activity over many trials is

shown in it is found on the bottom.

So large fires mean more activity from the zero.

And you can see as shown in the schematic before

that has a seamless turns on.

In this case, there's no coherent motion and there's very

little response in the neurone does start to build up,

however.

And indeed that build up in activity sustained until the

moment that the animal makes that eye movement to the

left or the right.

Just before that movement is made, that activity goes away.

It's as if that activity is predicting when the movement

will occur.

So these neurones in our area, AYP, which is I

showing in the second have some sensory input, are also

predicting when an eye movement occur and indeed which location

visual space I'm moving towards.

So you can vary the signal as visual motion streamers

in the on the screen.

In this case, if you move it, for example, in

one direction, it's to say to the left.

Which would inform the animal that the eye movement should

be to the left that is away from the part

of visual space that these neurones represent.

Then you find actually that the activity of these neurones

is very low.

And indeed, when they make an eye movement, nothing much

changes.

If, on the other hand, you put a lot of

thoughts moving in that direction to the right, predicting that

the movement should go to the right, which is also

happens before this neurone.

The preferred location of the eye movement is a good

deal.

You find that the activity goes up substantially, is sustained

through the time by movement and then dies away.

So these neurones encoding three different things.

They seem to be encoding aspects of the visual stimulus,

the coherence of the dots and when and where the

eyes move.

They are sensory motor neurones like those we discussed in

Area 80 on Friday.

So this activity predicts the direction of the movement, as

I showed you here, which.

If the animal was making it immune to the right.

In this case, the activity is high because making my

move to the left, the activity is low.

The activity signals the direction of the eye movement.

It signals also the time of the current, the eye

movement.

And also the activity also depends on the actual strength

of the visual signal.

If there was a lot of speed in the same

direction, the activity is not.

The activity is lower.

So these new ones are really integrating lots of different

types of things, integrating visual sensory information and predicting where

an eye when and where an eye will move.

They are that boundary, the interface between sensation and motor

output.

Despite his own father.

The simple decision is whether I should be moved right

or left.

We'll hear his indistinguishable in his past.

But a visual motion into the Michael, where I've also

said to you that neurones in our IP may participate

in making decisions about where to move the ice.

And the logic, the reasons for making that claim that

these neurones in our area may participate in making these

decisions.

Is that they are not sensory neurones because their activity

build up slowly and is continued after the cessation of

the visual stimulus.

They also are not sensory neurones because their activity predicts

the time and direction of a subsequent eye movement.

Even when the sensory information is ambiguous.

I didn't show you here.

But those neurones are also not just motor neurones because

their activity depends on the visual stimulus.

So if example this number of dots moving in the

same direction changes the.

I give you the neurones that I have a sensory

representation.

And what I only alluded to here and haven't really

shown to you that the differences in their activity emerged

early on in the response way before my movement was

actually made.

So these two different sets of evidence that you can

see in this one task said if these neurones are

neither sensory neurones nor motor neurones, but something at the

interface between them.

And for that reason they are hypothesised to be closely

involved in the formation of decisions about where to move

the eyes because they integrate.

They say that the bridge between sensory and motor activity.

So we just come out of size and go back

to the semantic illustration of what these two areas are

providing.

In this particular past year.

The area Empty streets.

Visual Sensory Stimulus Responses to visual Motion.

I've suggested the area.

Let me instead show you something that's more related to

the behaviour output, or at least the interface between sensory

and behaviour.

So how does this fit into the kinds of parts

that we were describing for?

We can start to think about the activity in aerospace

as potentially representing the decision variables.

So.

I said that there needs to be a decision variable.

When we make a decision, we need to collapse that

decision onto something as simple, as simple enough space in

which we can make a decision, for example.

We may want to say simply that if I produced

at least five action potentials, that I am confident about

the sensory input and I would like to make the

decision to move my eyes around.

In that case, the number of action tools that I

produce is a decision variable.

It is not enough of them.

I won't make that decision.

It is too many of them.

There's more than enough of them.

I will make that decision.

The decision variable.

So we could argue that the activity of neurones in

LP is actually a decision variable itself.

And further, we could say that when we apply criteria

to that decision variable, that is a particular threshold, let's

say five action potentials.

Once I pass that bacteria, I will make that decision.

So we go back to this description of what a

decision might look like.

We have, as I said before, the task is moving

left and right some hypotheses that we generate some belief

and find knowledge that we're not really going into.

And here we have the analysis of visual motion and

a decision in the end to move the eyes left

or right.

It should be.

Another thing here is useful form of evidence is actually

the output of every empty decision.

Variable is potentially the activity of neurones in every LP,

and the decision rule is simply that when the activity

of neurones in area LP exceed a certain value, then

I move my direction in my eyes and the direction

indicated by those neurones.

So this is a simple architecture for making a very

simple decision.

But we start to learn some really interesting things from

this.

For example, in the next few slides.

What I want to show you is that the predictions

of this kind of model.

Are the simple decisions in the course of simple decisions

and hard decisions.

There are compromises between the speed and accuracy of the

decision.

I showed you this graph before it.

In the context of these tasks, monkeys and humans are

very capable of making correct decisions when the number of

dots moving in the right direction is enough and we

get it right 100% of the time, and when it's

not enough, we get it right on time, which is

chance.

And in between we have a graded performance.

What I didn't show you was that if you looked

at the reaction time and given all monkeys that it

takes the time it takes to make these decisions.

The report was moving left to right.

This also varies if the motion hearings.

It takes longer.

That's at the left.

It takes longer to make the decision when there's very

few dots moving in the right direction and takes less

time to make decision when there's a lot of moving.

And we might think that the difference between the minimum

amount of time it takes to make a response, it

might be simply in our time, it takes me to

trigger a motor action.

That difference between the minimum and maximum amount of time

make a decision for people thinking we're deliberating about what

the information is.

The evidence is that is provided by those you can

the spring.

And in the context of the model I'm showing you,

we can think of then evidence being accumulated over time.

We require a threshold to be reached, after which we'll

make the decision.

And when the sequence is moving, a lot of thoughts

are moving in the same direction.

That threshold is reached relatively quickly, or when only a

few dots are moving in the right direction, that threshold

is reached.

We slowly.

So this model, which is often called the drift diffusion

model or accumulation model or rate model, or is it

about faulty compounds or it simply predicts that hard decisions

take longer because the rate of accumulation of the decision

variable, the evidence is slower when when a stimulus has

less signal to noise ratio.

You can even step.

We can even start to dig down a bit further

into in this module about how the decisions actually might

be made.

I showed you that owns an area and keep people

in motion directions.

What?

I didn't show you.

But what I told you was that some of your

own example coercion happened to the left and some down

to the right.

Up into the right.

Down to the left, for example.

So to make this decision, what we would like to

do is compare the responses of neurones that are, say,

representing often to the right with those and in the

opposite direction.

Right versus left, for example.

So we might find you are preparing activity of neurones,

preparing right with motion activity, neurones, preparing network management.

The way to extract a decision variable, a useful form

of evidence from these neurones is simply to find a

difference in their activity.

What is one minus the other?

We just represent that with a minus sign and we

do that.

In an area of IP.

We expect that to be the difference between these neurones

activity, which initially starts off as zero stimulus and then

after stimulus turns on gradually or rapidly.

Starts to go in one direction or the other direction.

So, for example, in this case, this evidence area of

activity in area of IP will tend to go towards

evidence for a right wing motion.

So we can think of then of this area activity,

an area IP representing the accumulated evidence that is seen

as moving either right or left.

And further that we will apply criteria to that activity

generating IP such that when this activity reaches a certain

level, we will decide that the dots are moving to

the right or to the left.

And that active you accumulate over some time.

That time of accumulation will depend on the magnitude of

the evidence.

Yeah, so that's a really good question.

So the exact mechanism for how you subtract your rooms

can vary.

The simplest way to think about is you think back

a few lectures.

If you have glutamatergic outputs from some neurones and gabaergic

apples from other neurones and the Gabaergic and the glutamatergic

have different signs, one is positive, one is negative.

And so when you add those together, you actually have

a subtraction going on.

So if you inhibit activity from activity output of neurones

that are going right one direction, if you inhibit them

by the activity of neurones exerting liquid direction, you actually

have a function.

Antibody suppression.

So inhibition can do this infarction for you.

There are other ways of doing this, but that is

the most obvious way to find.

Those neurones are coming together in some form, invited together

into a light.

If some neurones are providing in addition to some neurone

defining excitation, you can subtract one from the other and

get this form of evidence.

Now the schematics are showing.

You going to show you the kind of real activity

on a trial by trial basis and every MP.

If you think about this, it's also schematic, but it's

a bit more realistic.

There's a lot of variability from moment to moment in

the activity of neurones and area and to.

So, for example, those neurones that were preferring right with

motion are no longer nice straight lines, but they will

be lines.

And you and Fred, right?

MARTIN And your friend Macklemore from Memphis.

In fact, these you still in the end get towards

the same value, but you got a lot of variance

in the game, for example, instead of having a straight

line here.

Absolutely.

Lines are important, first of all.

Now, the consequence of this noise that's happening on each

trial is variability in your firing is that sometimes you

might reach this criteria more quickly on than on other

times.

So for example, and because of that, we need to

make a decision about where we set the criterion and

what decision we're making.

It is evidence accumulates over time.

It will, in the end get to the right place.

If we make our criteria nice and high threshold, nice

and high, we will only.

Make a decision when the evidence is accumulated to a

really safe sure bet.

So we going to have a higher threshold and make

sure that we don't make the wrong decision.

If, on the other hand, we reduce our criteria, reduce

our threshold, we become sensitive to noise variability.

Some of the times that noise is fine, that variability

is fine.

So, for example, here we're still making the right decision

is going towards the right.

We're making it earlier because we're able to be more

sensitive to the early phase of the activity.

But we also occasionally make the wrong decision because actually

because that noise, that variability, the activity, the new ordinary,

empty, we're actually representing the wrong direction in motion at

that point in time.

So if we make our criteria really, really low, we're

going to be faster to make decisions because we need

less evidence to accumulate.

But we're not going to make we run the risk

of making the wrong decision.

So we transform a safe and slow decision and go

fast with every decision.

Just by simply changing the criteria which will apply to

activity of your.

The other way that we can try and change the

kind of responses we make is by adding bias to

the activity.

So, for example, we might come in with preconceptions and

don't move to the right.

We could somehow change the combinations we're making such that

the activity goes right with neurones ones closer to the

threshold that we've set.

We will then be very capable, very capable of detecting

dogs that move around very quickly.

With our bias allows us to make fast decisions.

Our it also runs the risk of making the wrong

decision.

If, for example, these double blind gear activity would have

normally ended up being a st, the left was unfortunately

to the right.

Early on in the trial.

So again, we can transform a safe and slow decision

into a risky and fast decision, this time not by

changing the criteria that we're applying to the activity, but

by changing the bias that we put into the system

in the first place.

We might call this our five beliefs, our bias, or

whatever it is.

It's it's something that we can use to manipulate the

activities.

I want to spend so.

But hopefully outline to you there is that in this

very simple framework of understanding the decision and these very

simple, neurobiological driven models of making those decisions.

We actually have some profound insight about the process of

making decisions that we can have these safe and slow,

fast and risky ones.

We've actually been able to see how neurones that bridge

between sensation and motor activity might actually help revive that

season, although we still don't know.

How.

I just want to spend a few seconds, you know,

just describing some one of the other outcomes of that

kind of framework.

And that is one of the things we want to

do when we make decisions is learn from them.

We want to make better decisions in the future.

And.

The other thing that we want to do is and

make better decisions.

We kind of know how confident we were in those

decisions.

We also want to know.

We want to associate the decisions that we make with

the presence or absence of a reward that we get

from students.

So we all know how confident we are in the

season we're making so that we can learn from those

decisions.

We want to know if those decisions led to a

reward.

Strikingly, we've been able to make some progress in that

in the last ten years.

But this accumulation model actually predicts that a wave representing

confidence in the activity of neurones.

As I said before, we can have a decision variable

here which accumulates over time until we make a decision.

But if we look at the activity of neurones in

the brain over this period of time, we also find

another feature which would be very happy to be able

to answer in this context is that.

The certainty that we have and the variability in the

activity, those neurones changes as a function of time.

So not only are we getting a change in the

mean, that is the accumulation of evidence, we're also getting

a change in the variability of the activity of those

neurones.

And the consequence of that is that early on in

the trial, for example, we have a lot of variability

and we have much less confidence in our choices.

Much more uncertainty.

Whereas later on in time we have more certainty or

more confidence.

Now, of course, the variability of different.

We can actually measure the reduction here that we are

actually more confident later on.

Like more evidence because the variability reduces and the activity

of neurones and towards the main.

We actually measure some of these things by confidence in

humans and animals.

This is one really nice example of how to find

it in a child in this case.

We can measure the confidence the animal the child has

in the decision making without even asking.

And the rate at which a child will find very

difficult to do.

This task is really straightforward and really, really elegant.

A trial has shown two boxes through which they can

put their hands and is shown that there's a toy

in one of those boxes.

They are then required to.

A delay is then interposed between being shown that and

then being exposed to the two boxes again.

And then the account is required to indicate whether by

moving the hand towards the box which of the thing

before using.

And that's a simple task.

That's the simplest open task that the to do.

It is stunning when you do this task is to

do what you see represented here on the x axis

is the memorisation.

The way the time between being shown the and being

asked to complete the task, which ranges between three and

4 seconds in this experiment.

And the Y axis here is what we would call

the persistence.

That is how long the child leaves the hand in

the box scoring for the object.

The green dot here shows how long they leave the

hand boxes going.

By the way, he's not there.

There's not public approval.

When they make the correct decision and the read points

indicate how long they chooses to go where there may

be a decision.

Now, if the child had no representation of the confidence,

they hadn't made decisions.

These values should be the same.

You should explore as long whether you made the correct

or incorrect decision, you should explore the same outcome.

As strikingly, you find that the child exposed longer when

they've made the correct decision and when they're waiting for.

This implies that the chart has a representation of the

confidence in the decision they can somehow use.

Another elegant design is in monkeys, as shown here and

there.

And I'll just take you through this briefly.

But basically that same experimental design that we saw before

we realised the left or the right is now elaborated

slightly with one little change in the experimental design.

Now instead of just having left and right pockets to

move their eyes.

There's also another target that allows a monkey to make

a sure bet.

A sure bet is a small but consistent war.

So the monkey's unsure about the decisions that they're making.

They could take the show back because I know they'll

get a small reward if they're more confident that this

isn't the right thing.

It was more like a move to the left or

the right pocket, which will give a larger reward there

some risk.

And indeed, if you ask the monkey to do this

task, you find the data.

I won't go through the data particularly here, but it

is, as you would expect, Monkey makes more choices when

it is when the signal strength is lower and therefore

he's less likely to be concerned and less sure choices.

So the signal strength is higher is therefore likely to

be important.

So this is the probably the short target.

And the probability the short target decreases with this amount

of.

Signal strength.

Strikingly, if you look at the activity of the neurones

in this area of IP actually represent whether or not

the animal will choose the short target early on in

the trial.

This is a little bit tricky, so I'm just going

to show you this and describe it to do really.

This is the task here.

During this time here, there's a period of time here.

That's when the stimulus is on.

Now the dots come on and then they turn off.

And then at some point in time after that, the

short target comes on.

And that's from Dave on this test line.

And then some time after that, the channel makes a

choice by moving their eyes.

You can ignore the street here for a moment and

just look at this bit during the presentation.

The stimulus.

It turns out if you divide those files into three

different parts, Monkey chooses the left target, choosing the right

target, which uses a short target.

The activity in this period before they even know that

the short target will be available, predicts the upcoming decision.

The implement.

This short target is only available on 57,000 unpredictable 50%

pilot.

And we did not know that that tiger will be

available when when this activity is developing.

And yet that activity sits between this activity, the left

and right eye movements, even before the animal knows the

target is available.

That is evidence of an activity representing the confidence the

animal has in the decision to bear out or.

I'm going to get this slide, but I encourage you

to write.

I read the papers.

I just want to end this by saying.

When we make a decision, we hope that that will

be the correct decision.

We hope to get reward for a couple of lecture

lectures ago, we discussed the part of the brain that

is actually important in generating rewarding signals eventual placement where.

And it turns out that that little area of the

brain provides broadcast signals about work and incorrect decisions to

the rest of the brain, including those areas like it

either involved in making these decisions.

And there's a beautiful set of data in the in

the in the technical area which shows.

When an animal is learning a task like the kind

of task of showing their.

That that the activity in the rental health area which

in start of encoding the reward that the animal gets

transitions to encoding the stimulus that will predict the reward.

And so the animal was able to use this teaching

signal from the entertainment area, its own signal to learn

how to make better decisions.

I just explain this diagram here to help you understand

what is going on.

Early on in the learning process, Dan was not provided

as it was.

It does get a reward, maybe a reward of juice,

for example.

And when that reward is provided, the activity of neurones

in the BTK monkey again increases.

The animals and learn to associate that the terms of

that reward that Jews with a previous occurrence of the

stimulus that predicts that reward.

This we were condition stimulus.

And after a long time of learning this relationship.

The animals have eaten during the VTR no longer respond

to the reward itself, but respond to the presence of

the stimulus, the conditions in this.

And indeed, if a reward is absent after presentation of

the conditions in which you see this produce and everything

approaching the EPA.

So the neurones in the mental states mental area are

also representing the outcomes of these decisions.

They're representing whether or not a stimulus will produce or

upcoming reward, and they're allowing animals to learn from that,

from that rewarding and rewarding scenario.

So what I hope I've shown you here then is

very simple decision architecture.

Face and sensory input.

Making eye movement has taught us a lot about how

to see the snake in the brain.

We've seen that some neurones that seem to sit at

the interface between sensory and motor outputs accumulate signals in

a way that is inconsistent with the idea that they

form in of themselves the activities of the client in

making this decision and that all we need to do

a set of criteria on the activity of neurones so

that we subsequently make a decision.

I've shown you that in addition seems to be representation

of confidence in the brain, a way that we can

be confident about whether or not we're making the right

decisions.

I'll show you also that in the reward circuits in

the brain, which allow us to learn about history and

experience of making those decisions.

How will these things come together?

This remains still a mystery.

How all these things are brought together, how they invade

consciousness, an awareness that only remains in these people.

Those signals are there.

This is what we've learnt over the last ten years,

and what I would find explained to you on Friday

is how those ending direct with the emotions that we

feel.

Thanks everyone.

I was.

Very.

Sure about a ton.

Of pressure placed on the monkeys by the experiment.

Oh, that's a really good question.

Right.

So.

So you could make these decisions in two context.

One is you got all the time in the world.

But make these decisions in context.

One is you have all the time in the world

and the other is you need to make it in

a set period of time.

Now, the context in this case for these animals is

provided not so much by the but sort of.

But these animals, there's two things that you might want

to try and make it fun.

First of all, the faster they make decisions, the quicker

they get.

And they figure they get to the next problem that

they have during the 7010 problem.

So that's one one plan.

The other one is that the stimulus plan.

So there's actually no further additional data from that.

So there's there's a concept that there's no additional evidence

coming in.

So you've already got all the evidence.

And if you make it faster decision, you get another

of.

So this is not like an election.

It's a what.

Is what what official news.

We don't get comparison with what I saw.

What is the model when you're on an area like

that is that when they're actually reaching a certain.

Probably about 16 foot about one in every six feet.

That is.

So whether this is some of this is certainly part

of the voting.

Record during the primaries.

What we do know is that the.

In the area like we predict.

When you.

And there is more or less in terms of the

number of actions that an optimist sits on.

Yeah, right.

And also this like I don't know how this relates

to the fact that the you're asking to have like

a preferred coin partner.

Yeah.

Sorry I struggle to work out between say so.

You're in the now and you were mysterious for a

long time when they seemed to actually.

Yeah.

So the fact that.

So when you want to do things to some kind.

Are you finding some kind of situation in which.

So it's actually.

A time.

Where the.

But then you the event.

Well.

Oh.