No increased circular inference in adults with high levels of autistic traits or autism

Autistic traits foster effective curiosity-driven exploration

Curiosity-driven exploration involves actively engaging with the environment to learn from it. Here, we hypothesize that the cognitive mechanisms underlying exploratory behavior may differ across individuals depending on personal characteristics such as autistic traits. In turn, this variability might influence successful exploration. To investigate this, we collected self- and other-reports of autistic traits from university students, and tested them in an exploration task in which participants could learn the hiding patterns of multiple characters. Participants' prediction errors and learning progress (i.e., the decrease in prediction error) on the task were tracked with a hierarchical delta-rule model. Crucially, participants could freely decide when to disengage from a character and what to explore next. We examined whether autistic traits modulated the relation of prediction errors and learning progress with exploration. We found that participants with lower scores on other-reports of insistence-on-sameness and general autistic traits were less persistent, primarily relying on learning progress during the initial stages of exploration. Conversely, participants with higher scores were more persistent and relied on learning progress in later phases of exploration, resulting in better performance in the task. This research advances our understanding of the interplay between autistic traits and exploration drives, emphasizing the importance of individual traits in learning processes and highlighting the need for personalized learning approaches.

A common computational and neural anomaly across mouse models of autism

Computational psychiatry has suggested that humans within the autism spectrum disorder (ASD) inflexibly update their expectations (i.e., Bayesian priors). Here, we leveraged high-yield rodent psychophysics (n = 75 mice), extensive behavioral modeling (including principled and heuristics), and (near) brain-wide single cell extracellular recordings (over 53k units in 150 brain areas) to ask (1) whether mice with different genetic perturbations associated with ASD show this same computational anomaly, and if so, (2) what neurophysiological features are shared across genotypes in subserving this deficit. We demonstrate that mice harboring mutations in Fmr1 , Cntnap2 , and Shank3B show a blunted update of priors during decision-making. Neurally, the differentiating factor between animals flexibly and inflexibly updating their priors was a shift in the weighting of prior encoding from sensory to frontal cortices. Further, in mouse models of ASD frontal areas showed a preponderance of units coding for deviations from the animals' long-run prior, and sensory responses did not differentiate between expected and unexpected observations. These findings demonstrate that distinct genetic instantiations of ASD may yield common neurophysiological and behavioral phenotypes.

A theory of autism bridging across levels of description

Autism impacts a wide range of behaviors and neural functions. As such, theories of autism spectrum disorder (ASD) are numerous and span different levels of description, from neurocognitive to molecular. We propose how existent behavioral, computational, algorithmic, and neural accounts of ASD may relate to one another. Specifically, we argue that ASD may be cast as a disorder of causal inference (computational level). This computation relies on marginalization, which is thought to be subserved by divisive normalization (algorithmic level). In turn, divisive normalization may be impaired by excitatory-to-inhibitory imbalances (neural implementation level). We also discuss ASD within similar frameworks, those of predictive coding and circular inference. Together, we hope to motivate work unifying the different accounts of ASD.

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.

Simulating Developmental and Individual Differences of Drawing Behavior in Children Using a Predictive Coding Model

Predictive coding has recently been proposed as a mechanistic approach to explain human perception and behavior based on the integration of perceptual stimuli (bottom-up information) and the predictions about the world based on previous experience (top-down information). However, the gap between the computational accounts of cognition and evidence of behavioral studies remains large. In this study, we used a computational model of drawing based on the mechanisms of predictive coding to systematically investigate the effects of the precision of top-down and bottom-up information when performing a drawing completion task. The results indicated that sufficient precision of both signals was required for the successful completion of the stimuli and that a reduced precision in either sensory or prediction (i.e., prior) information led to different types of atypical drawing behavior. We compared the drawings produced by our model to a dataset of drawings created by children aged between 2 and 8 years old who drew on incomplete drawings. This comparison revealed that a gradual increase in children's precision of top-down and bottom-up information as they develop effectively explains the observed change of drawing style from scribbling toward representational drawing. Furthermore, individual differences that are prevalent in children's drawings, might arise from different developmental pathways regarding the precision of these two signals. Based on these findings we propose a theory of how both general and individual development of drawing could be explained in a unified manner within the framework of predictive coding.