Complexity, specificity, and the timescales of developing expectations in visual perception

Perception is strongly influenced by our expectations, especially under situations of uncertainty. A growing body of work suggests that perception is akin to Bayesian Inference in which expectations can be viewed as ‘prior’ beliefs that are combined via Bayes’ rule with sensory evidence to form the ‘posterior’ beliefs. In this thesis, I aim to answer open questions regarding the nature of expectations in perception, and, in particular, what the limits of complexity and specificity in developing expectations are, and how expectations of different temporal properties develop and interact. First, I conducted a psychophysical experiment to investigate whether human observers are able to implicitly develop distinct expectations using colour as a distinguishing factor. I interleaved moving dot displays of two different colours, either red or green, with different motion direction distributions. Results showed that statistical information can transfer from one group of stimuli to another but observers are also able to learn two distinct priors under specific conditions. In a collaborative work, I implemented an online learning computational model, which showed that subjects’ behaviour was not in disagreement with a near-optimal Bayesian observer, and suggested that observers might prefer simple models which are consistent with the data over complex models. Next, I investigated experimentally whether selective manipulation of rewards can affect an observer’s perceptual performance in a similar manner to manipulating the statistical properties of stimuli. Results showed that manipulation of the reward scheme had similar effects on perception as statistical manipulations in trials where a stimulus was presented but not in the absence of stimulus. Finally, I used a novel visual search task to investigate how expectations of different timescales (from the last few trials to hours to long-term statistics of natural scenes) interact to alter perception. Results suggested that recent exposure to a stimulus resulted in significantly improved detection performance and significantly more visual ‘hallucinations’ but only at positions at which it was more probable that a stimulus would be presented. These studies provide new insights into the approximations that neural systems must make to implement Bayesian inference. Complexity does not seem to necessarily be a prohibitive factor in learning but the system also factors the provided evidence and potential gain in regards to learning complex priors and applying them in distinct contexts. Further, what aspects of the statistics of the stimuli are learned and used, and how selective attention modulates learning can crucially depend on specific task properties such as the timeframe of exposure, complexity, or the observer’s current goals and beliefs about the task