Special treatment of prediction errors in autism spectrum disorder

Computational components of visual predictive coding circuitry

If a full visual percept can be said to be a 'hypothesis', so too can a neural 'prediction' - although the latter addresses one particular component of image content (such as 3-dimensional organisation, the interplay between lighting and surface colour, the future trajectory of moving objects, and so on). And, because processing is hierarchical, predictions generated at one level are conveyed in a backward direction to a lower level, seeking to predict, in fact, the neural activity at that prior stage of processing, and learning from errors signalled in the opposite direction. This is the essence of 'predictive coding', at once an algorithm for information processing and a theoretical basis for the nature of operations performed by the cerebral cortex. Neural models for the implementation of predictive coding invoke specific functional classes of neuron for generating, transmitting and receiving predictions, and for producing reciprocal error signals. Also a third general class, 'precision' neurons, tasked with regulating the magnitude of error signals contingent upon the confidence placed upon the prediction, i.e., the reliability and behavioural utility of the sensory data that it predicts. So, what is the ultimate source of a 'prediction'? The answer is multifactorial: knowledge of the current environmental context and the immediate past, allied to memory and lifetime experience of the way of the world, doubtless fine-tuned by evolutionary history too. There are, in consequence, numerous potential avenues for experimenters seeking to manipulate subjects' expectation, and examine the neural signals elicited by surprising, and less surprising visual stimuli. This review focuses upon the predictive physiology of mouse and monkey visual cortex, summarising and commenting on evidence to date, and placing it in the context of the broader field. It is concluded that predictive coding has a firm grounding in basic neuroscience and that, unsurprisingly, there remains much to learn.

Decision-making in autism: A narrative review

LAY SUMMARY

Many autistic people report difficulties with real-life decision-making. However, when doing decision-making tests in laboratory experiments, autistic people often perform as well or better than non-autistic people. We review previously published studies on autistic people's decision-making, across different types of tests, to understand what type of decision-making is more challenging. To do this, we searched four databases of research papers. We found 104 studies that tested, in total, 2712 autistic and 3189 comparison participants on different decision-making tasks. We found that there were four categories of decision-making tests that were used in these experiments: perceptual (e.g. deciding which image has the most dots); reward learning (e.g. learning which deck of cards gives the best reward); metacognition (e.g. knowing how well you perform or what you want); and value-based (e.g. making a decision based on a choice between two outcomes that differ in value to you). Overall, these studies suggest that autistic and comparison participants tend to perform similarly well at perceptual and reward-learning decisions. However, autistic participants tended to decide differently from comparison participants on metacognition and value-based paradigms. This suggests that autistic people might differ from typically developing controls in how they evaluate their own performance and in how they make decisions based on weighing up the subjective value of two different options. We suggest these reflect more general differences in metacognition, thinking about thinking, in autism.

10 years of Bayesian theories of autism: A comprehensive review

Ten years ago, Pellicano and Burr published one of the most influential articles in the study of autism spectrum disorders, linking them to aberrant Bayesian inference processes in the brain. In particular, they proposed that autistic individuals are less influenced by their brains' prior beliefs about the environment. In this systematic review, we investigate if this theory is supported by the experimental evidence. To that end, we collect all studies which included comparisons across diagnostic groups or autistic traits and categorise them based on the investigated priors. Our results are highly mixed, with a slight majority of studies finding no difference in the integration of Bayesian priors. We find that priors developed during the experiments exhibited reduced influences more frequently than priors acquired previously, with various studies providing evidence for learning differences between participant groups. Finally, we focus on the methodological and computational aspects of the included studies, showing low statistical power and often inconsistent approaches. Based on our findings, we propose guidelines for future research.

Toward a Multidimensional Understanding of Misophonia Using Cluster-Based Phenotyping

Misophonia is a condition characterized by hypersensitivity and strong emotional reactivity to specific auditory stimuli. Misophonia clinical presentations are relatively complex and reflect individualized experiences across clinical populations. Like some overlapping neurodevelopmental and neuropsychiatric disorders, misophonia is potentially syndromic where symptom patterns rather than any one symptom contribute to diagnosis. The current study conducted an exploratory k-means cluster analysis to evaluate symptom presentation in a non-clinical sample of young adult undergraduate students (N = 343). Individuals participated in a self-report spectrum characteristics survey indexing misophonia, tinnitus severity, sensory hypersensitivity, and social and psychiatric symptoms. Results supported a three-cluster solution that split participants on symptom presentation: cluster 1 presented with more severe misophonia symptoms but few overlapping formally diagnosed psychiatric co-occurring conditions; cluster 3 was characterized by a more nuanced clinical presentation of misophonia with broad-band sensory hypersensitivities, tinnitus, and increased incidence of social processing and psychiatric symptoms, and cluster 2 was relatively unaffected by misophonia or other sensitivities. Clustering results illustrate the spectrum characteristics of misophonia where symptom patterns range from more “pure” form misophonia to presentations that involve more broad-range sensory-related and psychiatric symptoms. Subgroups of individuals with misophonia may characterize differential neuropsychiatric risk patterns and stem from potentially different causative factors, highlighting the importance of exploring misophonia as a multidimensional condition of complex etiology.