Conscious and unconscious inference Karl Friston University College London Overview The statistics of life Markov blankets and ergodic systems simulations of a primordial soup The anatomy of inference ID: 759928
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Slide1
How much about our interaction with – and experience of – our world can be deduced from basic principles? This talk reviews recent attempts to understand the self-organised behaviour of embodied agents, like ourselves, as satisfying basic imperatives for sustained exchanges with the environment. In brief, one simple driving force appears to explain many aspects of action and perception. This driving force is the minimisation of surprise or prediction error. In the context of perception, this corresponds to Bayes-optimal predictive coding that suppresses exteroceptive prediction errors. In the context of action, motor reflexes can be seen as suppressing proprioceptive prediction errors. We will look at some of the phenomena that emerge from this scheme, such as hierarchical message passing in the brain and the ensuing perceptual (unconscious) inference. My special focus will be the nature of prior beliefs that underlie our (conscious) sampling of the world to resolve uncertainty..
Conscious and unconscious inference
Karl Friston, University College London
Slide2Overview
The statistics of life
Markov blankets and ergodic systems simulations of a primordial soupThe anatomy of inference graphical models and predictive coding canonical microcircuitsAction and perception inference and consciousness simulations of saccadic searches
Slide3“How can the events in space and time which take place within the spatial boundary of a living organism be accounted for by physics and chemistry?” (Erwin Schrödinger 1943)
The Markov blanket as a statistical boundary
(parents, children and parents of children)
Internal states
External states
Sensory
states
Active states
Slide4The Markov blanket in biotic systems
Active states
External states
Internal states
Sensory states
Slide5T
he Fokker-Planck equation
And its solution in terms of curl-free and divergence-free components
lemma
:
any (ergodic random) dynamical system (
m
) that possesses a Markov blanket will appear to engage in active inference
Slide6But what about the Markov blanket?
Reinforcement learning, optimal control
and expected utility theory
Information theory and minimum redundancy
Self-organisation, synergetics and homoeostasisBayesian brain, active inference and predictive coding
ValueSurpriseEntropyModel evidence
Pavlov
Haken
Helmholtz
Barlow
Perception
Action
Slide7Overview
The statistics of life
Markov blankets and ergodic systems simulations of a primordial soupThe anatomy of inference graphical models and predictive coding canonical microcircuitsAction and perception inference and consciousness simulations of saccadic searches
Slide8Position
Simulations of a (prebiotic) primordial soup
Weak electrochemical attraction
Strong repulsion
Short-range forces
Slide9Element
Adjacency matrix
20
40
60
80
100
120
20
40
60
80
100
120
Markov
Blanket
Hidden states
Sensory states
Active states
Internal states
Markov
Blanket
= [
B ·
[
eig
(
B
) >
τ
]
]
Markov blanket matrix: encoding the children, parents and parents of children
Finding the (principal) Markov blanket
A
Does
action maintain
the structural and functional integrity of the Markov
blanket (
a
utopoiesis
) ?
Do internal states appear to infer the hidden causes of sensory states
(
active inference
) ?
Slide10Autopoiesis, oscillator death and simulated brain lesions
Slide11Decoding through the Markov blanket and simulated brain activation
100
200
300
400
500
-0.4
-0.3
-0.2
-0.1
0
Time
Motion of external state
True and predicted motion
-5
0
5
-8
-6
-4
-2
0
4
6
8
Position
Position
Predictability
2
Time
Modes
Internal states
100
200
300
400
500
5
10
15
20
25
30
Christiaan
Huygens
Slide12The existence of a Markov blanket necessarily implies a partition of states into internal states, their Markov blanket (sensory and active states) and external or hidden states.
Because active states change – but are not changed by – external states they minimize the entropy of internal states and their Markov blanket. This means action will appear to maintain the structural and functional integrity of the Markov blanket (autopoiesis). Internal states appear to infer the hidden causes of sensory states (by maximizing Bayesian evidence) and influence those causes though action (active inference)
Slide13res extensa (extensive flow)
res cogitans (beliefs)
Belief production
Free energy functional
“I am [ergodic] therefore I think”
Slide14The statistics of life
Markov blankets and ergodic systems simulations of a primordial soupThe anatomy of inference graphical models and predictive coding canonical microcircuitsAction and perception inference and consciousness simulations of saccadic searches
Overview
Slide15“Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz
Thomas Bayes
Geoffrey Hinton
Richard Feynman
The Helmholtz
machine and the Bayesian brain
Richard Gregory
Hermann von Helmholtz
Slide16“Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz
Richard Gregory
Hermann von Helmholtz
Impressions on the Markov blanket…
Slide17Bayesian filtering and predictive coding
prediction
update
prediction error
Slide18Making our own sensations
Changing sensations
sensations – predictions
Prediction error
Changing predictions
Action
Perception
Slide19the
Descending
predictions
Ascending prediction errors
A s
imple hierarchy
what
where
Sensory fluctuations
Hierarchical generative models
Slide20frontal eye fields
geniculate
visual cortex
r
etinal
input
pons
oculomotor signals
Top-down or backward predictions
Bottom-up or forward prediction error
proprioceptive input
reflex arc
Perception
David Mumford
Predictive coding with reflexes
Action
Prediction error (superficial pyramidal cells)
Expectations (deep pyramidal cells)
Slide21Biological agents
minimize their average surprise (entropy)They minimize surprise by suppressing prediction errorPrediction error can be reduced by changing predictions (perception)Prediction error can be reduced by changing sensations (action)Perception entails recurrent message passing to optimize predictionsAction makes predictions come true (and minimizes surprise)
Slide22Overview
The statistics of life
Markov blankets and ergodic systems simulations of a primordial soupThe anatomy of inference graphical models and predictive coding canonical microcircuitsAction and perception inference and consciousness simulations of saccadic searches
Slide23salience
visual input
stimulus
sampling
Perception as hypothesis testing – saccades as experiments
Sampling the world to minimise expected uncertainty
“I am [ergodic] therefore I think”
“I think therefore I am [ergodic]”
Likelihood
Empirical priors
Prior beliefs
Slide24Frontal eye fields
Pulvinar salience map
Fusiform (what)
Superior colliculus
Visual cortex
oculomotor reflex arc
Parietal (where)
Slide25Visual samples
Conditional expectations about hidden (visual) states
And corresponding percept
Saccadic eye movements
Hidden (oculomotor) states
Saccadic fixation and salience maps
200
400
600
800
1000
1200
1400
-2
0
2
Action (EOG)
time (ms)
200
400
600
800
1000
1200
1400
-5
0
5
Posterior belief
time (ms)
v
s.
v
s.
Slide26“Each movement we make by which we alter the appearance of objects should be thought of as an experiment designed to test whether we have understood correctly the invariant relations of the phenomena before us, that is, their existence in definite spatial relations.”
‘The Facts of Perception’ (1878) in The Selected Writings of Hermann von Helmholtz, Ed. R. Karl, Middletown: Wesleyan University Press, 1971 p. 384
Hermann von Helmholtz
Slide27Thank youAnd thanks to collaborators:Rick AdamsAndre BastosSven BestmannHarriet BrownJean DaunizeauMark EdwardsXiaosi GuLee HarrisonStefan KiebelJames KilnerJérémie MattoutRosalyn MoranWill PennyLisa Quattrocki Knight Klaas StephanAnd colleagues:Andy ClarkPeter DayanJörn DiedrichsenPaul FletcherPascal FriesGeoffrey HintonJames HopkinsJakob HohwyHenry KennedyPaul VerschureFlorentin WörgötterAnd many others
Slide28Perception and Action:
The optimisation of neuronal and neuromuscular activity to suppress prediction errors (or free-energy) based on generative models of sensory data.
Learning and attention:
The optimisation of synaptic gain and efficacy over seconds to hours, to encode the precisions of prediction errors and causal structure in the sensorium. This entails suppression of free-energy over time.
Neurodevelopment:
Model optimisation through activity-dependent pruning and maintenance of neuronal connections that are specified epigenetically
Evolution:
Optimisation of the average free-energy (free-fitness) over time and individuals of a given class (e.g., conspecifics) by selective pressure on the epigenetic specification of their generative models.
Time-scale
Free-energy minimisation leading to…
Slide29Searching to test hypotheses – life as an efficient experiment
Free energy principle
minimise uncertainty