8 th Biannual Scientific Meeting on Attention RECA

8 th Biannual Scientific Meeting on Attention “RECA VIII” Attention, uncertainty and free-energy Karl Friston Abstract We suggested recently that attention can be understood as inferring the level of uncertainty or precision during hierarchical perception. In this talk, I will try to substantiate this claim using neuronal simulations of directed spatial attention and biased competition. These simulations assume that neuronal activity encodes a probabilistic representation of the world that optimises free-energy in a Bayesian fashion. Because free-energy bounds surprise or the (negative) log evidence for internal models of the world, this optimisation can be regarded as evidence accumulation or (generalised) predictive coding. Crucially, both predictions about the state of the world generating sensory data and the precision of those data have to be optimised. Here, we show that if the precision depends on the states, one can explain many aspects of attention. We illustrate this in the context of the Posner paradigm, using simulations to generate both psychophysical and electrophysiological responses. These simulated responses are consistent with attentional bias or gating, competition for attentional resources, attentional capture and associated speed-accuracy tradeoffs. Furthermore, if we present both attended and nonattended stimuli simultaneously, biased competition for neuronal representation emerges as a principled and straightforward property of Bayes-optimal perception.

“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” - Hermann Ludwig Ferdinand von Helmholtz Richard Gregory Geoffrey Hinton From the Helmholtz machine to the Bayesian brain and self -organization Thomas Bayes Richard Feynman Hermann Haken

Overview Ensemble dynamics Entropy and equilibria Free-energy and surprise The free-energy principle Perception and generative models Hierarchies and predictive coding Perception Birdsong and categorization Simulated lesions Attention Uncertainty and precision Modeling the Posner paradigm Behavioral and ERP simulations

temperature What is the difference between a snowflake and a bird? Phase-boundary …a bird can act (to avoid surprises)

What is the difference between snowfall and a flock of birds? Ensemble dynamics, clumping and swarming …birds (biological agents) stay in the same place They resist the second law of thermodynamics, which says that their entropy should increase

But what is the entropy? …entropy is just average surprise High surprise (I am never here) Low surprise (we are usually here) This means biological agents must self-organize to minimise surprise. In other words, to ensure they occupy a limited number of states (cf homeostasis).

But there is a small problem… agents cannot measure their surprise ? But they can measure their free-energy, which is always bigger than surprise This means agents should minimize their free-energy. So what is free-energy?

What is free-energy? …free-energy is basically prediction error sensations – predictions = prediction error where small errors mean low surprise

More formally, External states in the world Sensations Action Internal states of the agent (m) Free-energy is a function of sensations and a proposal density over hidden causes and can be evaluated, given a generative model (Gibbs Energy) or likelihood and prior: So what models might the brain use?

Hierarchal models in the brain lateral Backward (modulatory) Forward (driving)

The proposal density and its sufficient statistics Laplace approximation: Perception and inference Activity-dependent plasticity Synaptic activity Synaptic efficacy Functional specialization Attention al gain Synaptic gain Attention and salience Enabling of plasticity Learning and memory

So how do prediction errors change predictions? sensory input Forward connections convey feedback Adjust hypotheses Prediction errors Predictions prediction Backward connections return predictions …by hierarchical message passing in the brain

David Mumford More formally, Synaptic activity and messagepassing Forward prediction error Backward predictions Synaptic plasticity cf Hebb's Law Synaptic gain cf Predictive coding cf Rescorla-Wagner

Summary Biological agents resist the second law of thermodynamics They must minimize their average surprise (entropy) They minimize surprise by suppressing prediction error (free-energy) Prediction error can be reduced by changing predictions (perception) Prediction error can be reduced by changing sensations (action) Perception entails recurrent message passing in the brain to optimise predictions Predictions depend upon the precision of prediction errors

Overview Ensemble dynamics Entropy and equilibria Free-energy and surprise The free-energy principle Perception and generative models Hierarchies and predictive coding Perception Birdsong and categorization Simulated lesions Attention Uncertainty and precision Modeling the Posner paradigm Behavioral and ERP simulations

Making bird songs with Lorenz attractors Syrinx Sonogram Frequency Vocal centre 0. 5 1 1. 5 time (sec) causal states hidden states

Predictive coding and message passing prediction and error 20 15 10 5 0 -5 10 20 30 40 50 60 causal states Backward predictions 20 15 stimulus 10 5000 5 4500 Forward prediction error 4000 3500 3000 2000 0. 2 0. 4 0. 6 time (seconds) 0. 8 -5 -10 hidden states 20 2500 0 15 10 5 0 -5 10 20 30 40 50 60

Frequency (Hz) Perceptual categorization Song a Song b time (seconds) Song c

Hierarchical (itinerant) birdsong: sequences of sequences sonogram Syrinx Frequency (KHz) Neuronal hierarchy 0. 5 1 1. 5 Time (sec)

Simulated lesions and false inference percept LFP Frequency (Hz) LFP (micro-volts) 60 40 20 0 -20 -40 no top-down messages LFP Frequency (Hz) LFP (micro-volts) 60 no structural priors 40 20 0 -20 -40 -60 no lateral messages Frequency (Hz) LFP (micro-volts) LFP no dynamical priors 0. 5 1 1. 5 time (seconds) 60 40 20 0 -20 -40 -60 0 500 1000 1500 2000 peristimulus time (ms)

Overview Perception Birdsong and categorization Simulated lesions Attention Uncertainty and precision Modeling the Posner paradigm Behavioral and ERP simulations Attention and precision first order predictions second order predictions

Forward prediction error Backward predictions precision and prediction error second order predictions (NMDA) first order predictions (AMPA)

A generative model of precision and attention target cue stimuli exogenous endogenous decay

Predictive coding hidden causes target hidden states cue hidden causes Parietal cortex Extrastriate cortex 1. 5 1 0. 5 stimuli Striate cortex 0 -0. 5 -1 -1. 5 100 200 300 400 500 time (ms) 600

prediction and error 1 prediction and error hidden states 1. 5 2 1 0. 5 1 1 0. 5 0 0 0 -0. 5 -1 -1. 5 hidden states 2 -1 100 200 300 400 500 600 -2 100 200 300 400 500 time (ms) 600 -1. 5 time (ms) 100 200 300 400 500 600 Inference with valid and invalid cues hidden causes 1. 5 1 1 stimuli 600 time (ms) Invalid cue 1. 5 0. 5 100 200 300 400 500 time (ms) Valid cue hidden causes -2 2 1 0. 5 0 0 -0. 5 -1 -1 0 -1 -1. 5 100 200 300 400 500 time (ms) 600 -2 100 200 300 400 500 time (ms) 600

Reaction times and conditional confidence validity costs and benefits Reaction time (ms) 400 350 300 invalid 250 Valid 100 200 300 and invalid cues 400 time (ms) 500 600 neutral valid

Behavioural simulations Simulated timing effects Empirical timing effects Foreperiod Invalid Neutral Valid 100 200 300 400 500 time (ms) 600 Posner et al, (1978)

Electrophysiological simulations prediction errors (sensory states) 3 0. 01 2 prediction errors (hidden states) 0. 005 1 0 0 -0. 005 -1 -2 -200 -100 0 -0. 01 100 200 300 -200 -100 0 100 200 300 Peristimulus time (ms) Valid Invalid N 1 P 1 2 V + P 3 0 Mangun and Hillyard (1991) 200 400 60 Peristimulus time (ms) 0

Thank you And thanks to collaborators: Rick Adams Jean Daunizeau Harriet Feldman Lee Harrison Stefan Kiebel James Kilner Jérémie Mattout Klaas Stephan And colleagues: Peter Dayan Jörn Diedrichsen Paul Verschure Florentin Wörgötter And many others