Perceptual Multistability as Markov Chain Monte Carlo Inference

1. Perceptual Multistability asMarkov Chain Monte Carlo Inference

2. …what?

3. Outline Construct rational model of visual process. • Algorithmic/computational model of mental processing • Not about neurons • Bayesian inference promising, computationally infeasible • Explain existing results • Multistability • Focus on binocular rivalry

4. Your brain is flat out making stuff up Sensory inputs fundamentally impoverished. • Reconstructing 3D world from 2D vision Bayesian inference promising • Belief (posterior) computed from sensory inputs (likelihood) and plausible world structures (prior) • Effective in practice • Requires approximation • Prior proposed approximation don’t represent uncertainty

5. Lying to your brain Binocular rivalry arises from presenting inconsistent images to each eye.

6. Lying to your brain Binocular rivalry arises from presenting inconsistent images to each eye.

7. Your brain is flat out making stuff up Sensory inputs fundamentally impoverished. • Reconstructing 3D world from 2D vision Bayesian inference promising • Belief (posterior) computed from sensory inputs (likelihood) and plausible world structures (prior) • Effective in practice • Requires approximation • Prior proposed approximation don’t represent uncertainty

8. Your brain knows it’s making stuff up Sensory inputs fundamentally impoverished. • Reconstructing 3D world from 2D vision Bayesian inference promising • Belief (posterior) computed from sensory inputs (likelihood) and plausible world structures (prior) • Effective in practice • Requires approximation • Prior proposed approximation don’t account for multistability

9. Outline New model of approximation (MCMC) • Exploration of hypothesis space corresponds to switches in multistability. • MCMC gives accurate predictions of state distributions!

10. Modelling the visual process Modelling visual process as Bayesian inference:

11. Modelling the visual process Relationship among pixels: Retinal stimulus Latent image Outlier process

12. Accurately confused Binocular rivalry:

13. Accurately confused

14. Accurately confused

15. Accurately confused Travelling waves:

16. Accurately confused

17. Summary Rational model of visual process multistability • Simple model of visual process (Bayesian) • Standard approximation techniques from machine learning (MCMC) • Accurately predicts experimental results, including multistability in binocular rivalry • Provides high-level intuition of neurally-plausible explanations.

18. Math

19. Math