Skip to main content

The Predictive Visual Brain in Autism Spectrum Disorders

Periodic Reporting for period 1 - PVB-ASD (The Predictive Visual Brain in Autism Spectrum Disorders)

Reporting period: 2018-09-01 to 2020-08-31

"New theories of Autism Spectrum Disorders (ASD) based on Predictive Coding have been proposed in recent years. These theories are motivated by the increasing empirical support for predictive coding models of the brain and the unprecedented amount of autism symptoms that can be explained by this framework. Recently, Van de Cruys et al. (2014) proposed the ""High Inflexible Precision of Prediction Errors in ASD"" theory, suggesting that individuals with ASD have difficulties in differentiating random variability from relevant learnable regularities in the world, and that this may explain peculiar behavior in cognitive and perceptual tasks as well as some affective issues. This could be rooted in altered meta-learning processes (learning about what is ""learnable""). However, empirical assessment of some of the theory’s critical hypotheses is still missing. Here we assess these hypotheses using neuroimaging methods (EEG and fMRI) and tasks that integrate probabilistic learning and perceptual organization in vision. Our main goals were: A- Assess if meta-learning processes are affected in ASD and which neural networks are involved. B- Understand where and when these mechanisms are altered in the hierarchy of visual information processing. C- Identify potential neural markers of ASD and relating these to individual differences.

We have created multiple experimental protocols that assess the effects of expectations and intrinsic learning at multiple levels in the visual processing hierarchy. This way we can assess whether the possible alterations in predictive processing are affecting early, mid or late stages of processing (or are widespread). We developed and applied a battery of tests using a high-density EEG system and an extra experiment using fMRI.

Assessing predictive processing in Autism with methods that allow for the identification and location of possible deficits both in space and time can lead to the refinement of the contemporary predictive processing inspired models of Autism and help clinicians and caregivers. There is also some evidence in the literature that applying a predictive coding framework to ASD could lead to the identification of different ASD subgroups, aiding differential diagnosis."
We managed to extend the work initially proposed in the description of the action. We added new tasks to allow for a more detailed assessment of the cortical visual hierarchy (also allowing us to discuss whether the possible alterations found are task-specific or not).

There have been a few relevant delays in the development of the action given technical problems with equipment and the COVID-related lockdown of 2020 (delaying the finalization of data collection). This has naturally affected the dissemination of the results too. The process of dissemination will happen throughout the year 2021 and reach the goals initially planned.

Overall, participants with ASD are less affected by the hidden contingencies in the tasks (generally consisting of stimuli with higher or lower probabilities of presentation). This means that participants were less affected by presentation probability and therefore also less surprised by unlikely stimulus presentations. This effect was more clear for tasks and stimuli that targeted the early visual cortex. On top of this, we found no overall differences between average confidence measures for participants with ASD and neurotypicals.

At this stage, our results indeed suggest altered predictive processing in participants with ASD. The patients seem to be less efficient in the intrinsic learning of stimulus presentation contingencies. This seems to happen mostly at early visual processing stages. These results will be reassessed when the data collection is finished.
To our knowledge, this will be the first extensive assessment of amodal completion in ASD using neuroimaging techniques. This might also be one of the first attempts to systematically relate confidence measures (as a measure of metacognition) to behavioral performance and neuroimaging measures in a group of participants with ASD. The three EEG studies developed here represent a complementary assessment of prediction mechanisms across the hierarchy of cortical visual processing (as there are tasks that focus on early, intermediate and late stages of cortical visual processing). This allows us to paint a full picture of a possible differential effect of learning and expectations in multiple brain areas at multiple points in time. Given the fact that our understanding of predictive processing in ASD is still somewhat limited, the publication of these results will represent a critical innovation in the field.