The real reason for brains Daniel Wolpert

I’m a neuroscientist and in neuroscience

we have to deal with many difficult

questions about the brain but I want to

start with the easiest question and the

question you really should have all

asked yourself at some point in your

life because it’s a fundamental question

if we want to understand brain function

and that is why do we and other animals

have brains not all species of our

planet have brains so if we want to know

what the brain is for let’s think about

why we evolved one

now you may reason that we have one to

perceive the world or to think and

that’s completely wrong if you think

about this question for any length of

time it’s blindingly obvious why we have

a brain with a brain for one reason and

one reason only and that’s to produce

adaptable and complex movements there is

no other reason to have a brain think

about it

movement is the only way you have of

affecting the world around you that’s

not quite true there’s one other way and

that’s through sweating but apart from

that everything else goes through

contraptions of muscles you think about

communication speech gestures writing

sign language they’re all mediated

through contractions of your muscles so

it’s really important to remember that

sensory memory and cognitive processes

are all important but they’re only

important to either drive or suppress

future movements there can be no

evolutionary advantage to laying down

memories of childhood or perceiving the

color of a rose if it doesn’t affect the

way you’re going to move later in life

now for those who didn’t believe this

argument we have trees and grass and our

planet without the brain but that

clinching evidence is this animal here

the humble cease were rudimentary animal

has a nervous system swims around in the

ocean in its juvenile life and on some

point of its life it implants on a rock

and the first thing it does in and

planting on that rock which it never

leaves is to digest its own brain and

nervous system for food so once you

don’t need to move you don’t need the

luxury of that brain and this is often

this animals often taking as an analogy

to what happens universities when

professors get tenure but that’s as

so I am a movement chauvinist I believe

movement is the most important function

in the brain that anyone tell you that’s

not true

now if movement is so important how well

are we doing understanding how the brain

controls movement and the answers we’re

doing extremely poor is a very hard

problem but we can look at how well

we’re doing by thinking about how well

we’re doing building machines what you

can do what humans can do think about

the game of chess how what are we doing

determining what piece to move where if

we pick garry kasparov here when he’s

not in jail against IBM’s deep blue well

the answer is IBM’s deep blue will

occasionally win and I think that IBM’s

deeply played probably anyone in this

room it would win every time that

problem is solved what about the problem

of picking up a chess piece dextrous

they manipulating and putting that back

down on the board if we put a five year

old Charles dexterity against the best

robots of the day the answers very

simple the child wins easily there’s no

competition at all now why is that top

problem so easy in the bottom problem so

hard one of the reasons is a very smart

five law could tell you the algorithm

for that top problem look at all

possible moves to the end of the game

and choose the one that makes you win so

it’s a very simple algorithm now of

course a lot of moves but with fast

computers as an approximation become

close to the optimal solution when it

comes to being dexterous it’s not even

clear where the algorithm is you have to

solve to be dexterous and we’ll see you

after both deceive and act on the world

which has a lot of problems but let me

show you cutting-edge robotics now a lot

of robotics is very impressive but

manipulation robotics is really some the

dark ages so this is the end of a PhD

project from one of the best robotics

institutes and the student is trained

this robot to pour this water into a

glass is a hard problem because the

water slosh is about but it can do it

but it doesn’t do it with any fact the

agility of a human now if you want this

robot to do a different task that’s

another three-year PhD program there is

no

no generalization at all from one task

to another in robotics now we can

compare this to cutting-edge human

performance so what I’m gonna share is

Emily Fox within the world record for

cup staffing now the Americans in the

audience will know all about cup

stacking it’s a high school sport where

you have 12 cups you have to stack and

unstack against the clock in a

prescribed order and this is her getting

the world record in real time

and she’s pretty happy we have no idea

what is going on inside her brain when

she does that and that’s what we’d like

to know so in my group what we try to do

is reverse engineer how humans control

movement and it sounds like an easy

problem you send a command down it

causes muscles to contract your arm or

body moves and you get sensory feedback

from vision from the skin from muscles

and so on

the trouble is these signals are not the

beautiful signals you want them to be so

one thing that makes controlling

movement difficult is for example

sensory feedback is extremely noisy now

by noise I do not mean sound we’re using

the engineering on neuroscience sense

meaning a random noise corrupting a

signal so the old days before digital

radio when you were tuning in your radio

and you heard on the station you wanted

to hear that was the noise but it more

generally this noise is something to

corrupts the signal so if example if you

put your hand under a table and try to

localize it with your other hand you can

be off by several centimeters due to the

noise in sensory feedback similarly when

you put motor output on movement output

it’s extremely noisy forget if I try to

hit the bull’s eye and ask just aim for

the same spot over and over again you

have a huge spread due to movement

variability and more than that the

outside world or task was both ambiguous

and variable that teapot could be full

it could be empty it changes over time

so we work a whole sensory movement

tasks soup of noise now this noise is so

great that society places a huge premium

on those of us you can reduce the

consequences of noise so if you’re lucky

enough to be able to knock a small white

ball into a hole several hundred yards

away using a long metal stick our

society will willing to reward you with

hundreds of millions of dollars

now what I want to convince you is the

brain also goes to a lot of effort to

reduce the negative consequences of this

sort of noise and variability and to do

that I’m gonna tell you about a

framework which is very popular in

statistics and machine learning of the

last 50 years called Bayesian decision

theory and it’s more recently a unifying

way to think about how the brain deals

with uncertainty and the fundamental

idea is you want to make inferences and

then take actions so let’s think about

the inference you want to generate

beliefs about the world so what a

beliefs beliefs could be but where are

my arms and space am I looking at a cat

or a fox but we’re gonna represent

beliefs with probabilities so we’re

gonna rips into belief with a number

between 0 & 1 0 meaning I do not believe

it at all 1 means that AB see certain

and numbers in between gives you the

gray levels of uncertainty and the key

idea to Bayesian inference is you have

two sources of information from which to

make your inference you have data and

data in neuroscience is sensory input so

I have sensory input which I can take in

to make beliefs but there’s another

source of information and that’s

effectively prior knowledge your cue leg

knowledge throughout your life in

memories and the point about Bayesian

decision theory is it gives you the

mathematics of the optimal way to

combine your prior knowledge with

sensory evidence to generate new beliefs

and I put the formula up there I’m not

gonna explain to you what that formulas

but it’s very beautiful and it has real

beauty and real explanatory power and

what it really says is what want to

estimate is the probability of different

beliefs given your sensory input so let

me give you an intuitive example imagine

you’re playing tennis live play tennis

and you want to decide where the ball is

going to bounce as it comes over the net

towards you there are two sources of

information based rule tells you there’s

sin through evidence you can use visual

information auditory information and

that might tell you it’s real and that

red spot but you know that your senses

are not perfect and therefore there’s

some variability where it’s going to

land shown by that cloud of red

representing numbers between 0.5 and

maybe 0.1

sad information is available on the

current shot but there’s another source

of information not available on the

current shot but only available by

repeated experience in the game of

tennis and that’s what the ball doesn’t

bounce with equal

for the court during the match if you’re

playing against a very good opponent

they may distribute that green area

which is the prior distribution making

it hard for you to return now both these

sources of information

carry important information and what

Bayes rule says they should mark are the

numbers in the red by the numbers and

the green to get the numbers in the

yellow which have the ellipsis and

that’s my belief so it’s the optimal way

of combining information now I wouldn’t

tell you all this if it wasn’t a few

years ago we should exactly what people

do when they learn new movement skills

what it means is we really are Bayesian

inference machines as we go around we

learn about statistics of the world and

lay that down but we also learn about

how noisy our own sensory apparatus is

and then combine those in a real

Bayesian way now a key part to the

Bayesian is this part of the formula and

what this part really says is I have to

predict the probability of different

sensory feedbacks given my beliefs so

that really means I have to make

predictions of the future and I want to

convince you the brain does make

predictions of the sensory feedback its

going to get and moreover it profoundly

changes your perceptions by what you do

and to do it I’ll tell you about how the

brain deals with Cynthia so you send a

command out you get sensory feedback

back and that transformation is governed

by the physics of your body and your

sensory apparatus but you can imagine

looking inside the brain and his inside

the brain you might have a little

predictor a neural simulator of the

physics of your body in your senses so

as you send the movie command down you

tap a copy of that off and run it into

your neural simulator to anticipate the

sensory consequences of reaction so as I

shake this ketchup bottle I get some

true sensory feedback as a function of

time on the bottom row and if I’ve got a

good predictor it predicts the same

thing well why would I bother doing that

I’m gonna get the Cynthia feedback

anyway well there’s good reasons imagine

as I shake the ketchup bottle someone

very kindly comes up with me and taps it

on the back for me now I get an extra

source of sensory information due to the

external act so I get to sources I get

you

tapping on it and I get me shaking it

but for my senses point of view that is

combined together into one source of

information now the good reason to

believe that you would want to be able

to distinguish external events from

internal events because external events

are actually much more behaviour elevant

than feeding everything that’s going on

inside my body so one way to reconstruct

that is to compare the prediction which

is only based on your movie commands

with the reality and any discrepancy

should hopefully be external so as I go

around the world I’m making predictions

of what I should get subtract them off

everything leftover is external to me

what evidence is there for this well

there’s one very clear example where a

sensation directed by myself feels very

different than though generated by

another person and so we decided the

most obvious case of start was with

tickling it’s been known for a long time

you can’t tickle yourself as well as

other people can but it hasn’t really

been shown it’s because you have a

neural simulator simulating your own

body and subtracting off that sense so

we can bring the experiments in the 21st

century by applying robotic technology

for this problem and effective what we

have is some sort of stick in one hand

attached to a robot and they’re going to

move that back and forward and then

we’re going to crack that with a

computer and use it to control another

robot which is going to tickle their

palm with another stick and we’re gonna

ask them to rape a bunch of things

including pictures actually just one

part of our study and here I’ve taken

away the robots but basically people

move with their right arm sinusoidal

back and forward and we replay that to

the other hand with a time delay either

no time delay in which case light would

just dip in your palm or with a time

delay over ten to ten or three tenths of

a second so the important point here is

the right hand always does the same

thing sinusoidal movement the left hand

always its same input sinusoidal tickle

all playing with is a temporal causality

and as we go from naught to point one

second it becomes more ticklish as we’re

from point one to point two it becomes

more tips again and by 0.2 of a second

it’s equivalently ticklish to the robot

just typically without you doing

anything so whatever is responsible for

this pencil ation is extremely tightly

coupled a temporal causality and based

on this other studies we really

convinced ourselves in the field that

the brain is making precise

predictions and subtracting them off

from the sensations now I have to admit

these are the worst studies my lab has

ever run because the tickle session the

palm comes engaged with large numbers of

subjects at these stars making them

significant so we were looking for a

much more objective way to assess this

phenomena and in the intervening years I

had two daughters and once the news

about children on back seats of cars on

long journeys

they’ve get into fights which started

with one limiting something to the other

the other than retaliation that quickly

escalates and children can’t identify

its which escalate in terms of force now

when I scream at my children to stop

sometimes they would both say to me the

other person hit them harder now I

happen to know my children don’t lie so

I thought was a nurse and it was

important how can I explain how they

were telling inconsistent truths and we

had pot size based on the tickling study

that when one child hits another they

generate the movement command they

predict the sensory consequences and

subtract it off so they actually think

they fit the person less hard than they

have rather liked the tickling where’s

the passive recipient doesn’t make the

prediction fuels the full blow so if

they retaliate with the same force the

first place more thinkers mean escalated

so we decided to test this in the lab

now we we don’t work with children we

don’t work with hitting but the concept

is identical we bring in two adults and

we tell them they’re gonna play a game

and so his player 1 and player 2 sitting

opposite each other and the game is very

simple we started with a motor with a

little leave and little force transducer

and we use this motor to apply a force

down to the player ones fingers for

three seconds and then it stops and

that’s players being told remember the

experience with that force and use your

other finger to apply the same force

down to the other subjects finger

through a force transducer and they do

that and player two’s been told remember

the experience of that force use your

other hand to apply the falls back down

and so they take it in turns to apply

the force they’ve just experienced back

and forward but critically they’re

briefed about the rules of the game and

separate rooms so they don’t know the

rule that other person is playing by and

what we measure is the force as a

function of turns and if we look at what

we start with a quarter of a Newton

there a number of turns

perfect would be that red line and

we see in all pairs of subjects is this

a 70% escalation and force on each go so

it really suggests when you’re doing

this basically studying others we’ve

done that the brain is canceling the

sensory consequences and under

estimating the force its producing so

release shows the brain makes

predictions and fundamental changes for

persons so we’ve made inferences we’ve

done predictions now we have to generate

actions and what Bayes rule says has

given my beliefs the action some sense

be optimal but we’ve got a problem

tasks a symbolic I want a drink I want a

dance

but the movement system has to contract

600 muscles in particular sequence and

there’s a big gap between the task and

the movement system so it can be briefed

and infinitely may different ways to

think about just the point the point

movement I could choose these two paths

and have infinite number of paths having

chosen a particular path I can hold my

hand on that path was instantly made

different joint configurations and I can

hold my arm to particular joint

integration but they’re very stiff or

very relaxed so I have a huge amount of

choice to make now it turns out we are

extremely stereotypical we all move the

same way pretty much and so it turns out

we’re so stereotypical our brains have

got dedicated neural circuitry to decode

this therapy so if I take some dots and

set them in motion with biological

motion your brains have circularly doing

understand instantly what’s going on now

this is a bunch of dots moving you will

know what this person is doing with a

happy sad old yarn a huge amount of

information if these dots were cars

going on a racing circuit you would have

actually no idea what’s going on so why

is it we move the particular ways we do

well let’s think about what really

happens maybe we don’t all quite move

the same way maybe there’s variation in

the population and maybe those who move

better than others have got more chance

in your children into the next

generation so an eeveelution rescales

movements get better

perhaps through life movements get

better through learning

so what is it about a moon which is good

or bad imagine I want to intercept this

ball here are two possible paths to that

ball well if I choose the left-hand path

I can work out the forces required in

one of my muscles of the function of

time but there’s noise added to this so

what I actually get basically the lovely

smooth desired force it’s a very noisy

version so I played the same command

through many times I will get a

different noisy version each time

because noise changes each time so what

I can show you here is how the

variability the movement will evolve if

I choose that way if I choose a

different way of moving on the right for

example then I’ll have a different

command different noise playing through

a nonlinear system very complicated all

we can be sure us is the variability

will be different if I move in this

particular way I end up with a smaller

variability across many movements so if

I choose between those two I would

choose the right one because it’s less

variable and the fundamental idea is you

want to plan your movement so as to

minimize the negative consequence of the

noise and one intuition to get is that

actually the amount of noise or

variability as I show here gets a bigger

as the force gets bigger so you want to

avoid big forces as one principle so

we’ve shown that using this we can

expend a huge amount of data that

exactly people are going about their

lives planning movements so as to

minimize negative consequences of noise

so I hope I’ve given to you brain is

there and evolved to control movement

and it’s an intellectual challenge to

understand how we do that but it also is

relevant for disease and rehabilitation

there are many diseases which affect

movement and hopefully if we understand

how we control movement we can apply

that to robotic technology and finally I

want to remind you when you see animals

do what I look like very simple tasks

the actual complexity of what’s going on

aside their brain is really quite

dramatic thank you very much

question two though so you’re you’re a

movement chauvinist

does that mean that you think that the

other things that we think our brains

about the kind of the dreaming the

yearning the falling in love and all

these things are a kind of sideshow and

accident never accident I think they’re

all important to drive the right movie

behavior to get reproduction of them so

I think I think people who study

sensation or memory without realizing

why you’re laying down memories of

childhood that’s that we forget most of

our childhood for example it’s probably

fine cuz it doesn’t affect our movements

later in life you only just store things

which are really gonna affect movement

so you think that people thinking about

the brain and consciousness generally

could get real insight by saying where

does movement play and you’re scared so

people have found out for example that

the studying vision and the absence of

realizing why you have vision is a

mistake you have to study a vision with

the rise ation of how the movement

system is going to use vision and it

uses it very differently once you think

of it that way well that was quite

fascinating thank you very much indeed