1. Baysian Approaches Kun Guo, PhD Reader in Cognitive Neuroscience School of Psychology University of Lincoln Quantitative Methods 2011

2. Example

3. Visual system interprets incoming retinal signals in the context of existing knowledge of the world. Some issues within this process: (1) signals are corrupted by variability of noise; (2) uncertainity in computation. Brain represents sensory information probabilistically, in the form of probability distribution. Ideal Bayesian Inference Observer: assigning probabilities to any degree of belief abouth the state of the world. Posterior ≈ Prior × Likelihood : Visual perception (Bayesian posterior probability of a scene) ≈ Prior probability of the state × Current input from the eye Vision is an act of interpretation

4. Thomas Bayes (1701 – 1761): English mathematician “An essay towards solving a problem in the doctrine of chances” was read to Royal Society in 1763 (doctrine of chance ~ theory of probability). Posterior ≈ Prior × Likelihood: a method of statistical inference to calculate the impact of evidence on beliefs. The probability is interpreted as a degree of belief (conditional probability distribution) rather than frequency. In application, the initial degree of belief is called prior and the updated degree of belief is called posterior. Widely applied in Science, Engineering, Medicine and Law, especially after 1950s. Bayes’ Theorem

5. P(A|B): posterior, the degree of belief in A after B is observed. P(A): prior, the degree of belief in A before B is observed. P(B|A)/P(B): likelihood, impact of B on the degree of belief in A. Bayesian Inference

6. Landing light Time Predictor 1 Predictor 2 Predictor 3 Target 200 ms Predictor 4 FP Stimulus sequence comprised four collinear bars (predictors) which appeared successively towards the foveal region, followed by a target bar with same or different orientation. Guo et al. (2004) Effects on orientation perception of manipulating the spatiotemporal prior probability of stimuli. Vision Research 44: 2349-2358Effects on orientation perception of manipulating the spatiotemporal prior probability of stimuli Applying Bayesian Inference in Vision research

7. Stimulus demonstration – non-collinear trial Applying Bayesian Inference in Vision research

8. Spatiotemporal structure of the priors Applying Bayesian Inference in Vision research

9. Spatiotemporal structure of the priors Applying Bayesian Inference in Vision research

10. Spatiotemporal structure of the priors Applying Bayesian Inference in Vision research

11. Real orientation difference   p   Distribution of perceived orientation difference C  l  p  t  Psychophysical function G  r  l  Noisy brain Representation (Likelihood)  r  G  p  Prior distribution  Guo et al., (2004) Vision Res. 44: 2349-2358 normal sequence — higher expectation of co-linearity — sharper prior distribution Applying Bayesian Inference in Vision research Free parameters: width of prior, width of likelihood, co-linearity threshold, fitting error

12. Different spatiotemporal structureDifferent experimental frequency Applying Bayesian Inference in Vision research

13. Learning and reasoning Language processing and acquisition Memory Vision Sensorimotor control Bayesian Applications in Psychology & Neuroscience Reference : Trends in Cognitive Sciences, 2006, Vol 10(7), “Special issue: Probabilistic models of cognition ”.