Investigation of autistic traits through strategic decision-making in games with adaptive agents

Interpersonal Distance Theory of Autism and Its Implication for Cognitive Assessment, Therapy, and Daily Life

The interpersonal distance (IPD) theory provides a novel approach to studying autism spectrum disorder (ASD). In this article, we present recent findings on the neurobiological underpinnings of IPD regulation that are distinct in individuals with ASD. We also discuss the potential influence of environmental factors on IPD. We suggest that different IPD regulation may have implications for cognitive performance in experimental and diagnostic settings, may influence the effectiveness of training and therapy, and may play a role in the typical forms of social communication and leisure activities chosen by autistic individuals. We argue that reconsidering the results of ASD research through the lens of IPD would lead to a different interpretation of previous findings. Finally, we propose a methodological approach to study this phenomenon systematically.

Cognitive-attentional mechanisms of cooperation-with implications for attention-deficit hyperactivity disorder and cognitive neuroscience

People's cooperativeness depends on many factors, such as their motives, cognition, experiences, and the situation they are in. To date, it is unclear how these factors interact and shape the decision to cooperate. We present a computational account of cooperation that not only provides insights for the design of effective incentive structures but also redefines neglected social-cognitive characteristics associated with attention-deficit hyperactivity disorder (ADHD). Leveraging game theory, we demonstrate that the source and magnitude of conflict between different motives affected the speed and frequency of cooperation. Integrating eye-tracking to measure motivation-based information processing during decision-making shows that participants' visual fixations on the gains of cooperation rather than its costs and risks predicted their cooperativeness on a trial-by-trial basis. Using Bayesian hierarchical modeling, we find that a situation's prosociality and participants' past experience each bias the decision-making process distinctively. ADHD characteristics explain individual differences in responsiveness across contexts, highlighting the clinical importance of experimentally studying reactivity in social interactions. We demonstrate how the use of eye-tracking and computational modeling can be used to experimentally investigate social-cognitive characteristics in clinical populations. We also discuss possible underlying neural mechanisms to be investigated in future studies.

Invasive Computational Psychiatry

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

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.

Hippocampal contributions to social and cognitive deficits in autism spectrum disorder

Autism spectrum disorder (ASD) is characterized by hallmark impairments in social functioning. Nevertheless, nonsocial cognition, including hippocampus-dependent spatial reasoning and episodic memory, is also commonly impaired in ASD. ASD symptoms typically emerge between 12 and 24 months of age, a time window associated with critical developmental events in the hippocampus. Despite this temporal overlap and evidence of hippocampal structural abnormalities in ASD individuals, relatively few human studies have focused on hippocampal function in ASD. Herein, we review the existing evidence for the involvement of the hippocampus in ASD and highlight the hippocampus as a promising area of interest for future research in ASD.

Abnormal negative feedback processing in individuals with autistic traits in the Iowa gambling task: Evidence from behavior and event-related potentials

Value-based decision making plays an important role in social interaction. Previous studies have reported that individuals with autism spectrum disorder (ASD) exhibit deficits in terms of decision making. However, it is still unknown clearly whether individuals with high autistic traits within nonclinical populations employ abnormal neural substrates in value-based decision-making. To explore this issue, we investigated value-based decision making and its neural substrates in individuals with high and low autistic traits within a typically developing population who completed the revised Iowa gambling task (IGT) based on measurements of event-related potentials (ERPs). The IGT net scores were significantly lower in the group with high autistic traits than the group with low autistic traits in the fifth and sixth blocks. The ERP results showed that the feedback-related negativity (FRN) amplitude in individuals with high autistic traits allowed slight discrimination between positive and negative feedback in the low-risk option. The event-related spectral perturbations (ERSPs) and inter-trial coherence (ITC) of the theta-band frequency were also lower in the group with high autistic traits than the group with low autistic traits in the loss low-risk option. The results obtained in this study indicate that individuals with high autistic traits exhibit an unusual negative feedback process and relevant neural substrate. The FRN amplitude and theta-band oscillation may comprise a neural index of abnormal decision-making processes in individuals with high autistic traits. This study of a small sample may be considered an important step toward a more comprehensive understanding of the autism "spectrum" within a nonclinical population based on cognitive neuroscience.

Greater Individual Variability in Functional Brain Activity during Working Memory Performance in young people with Autism and Executive Function Impairment

BACKGROUND

Individuals with autism spectrum disorder (ASD) often present with executive functioning (EF) deficits, including spatial working memory (SWM) impairment, which impedes real-world functioning. The present study examined task-related brain activity, connectivity and individual variability in fMRI-measured neural response during an SWM task in older youth and young adults with autism and clinically significant EF impairment.

METHODS

Neuroimaging was analyzed in 29 individuals with ASD without intellectual disability who had clinically significant EF impairment on the Behavior Rating Inventory of Executive Function, and 20 typically developing controls (participant age range=16-34). An SWM N-Back task was performed during fMRI. SWM activity (2-Back vs. 0-Back) and task-related dorsolateral prefrontal cortex (DLPFC) connectivity was examined within and between groups. Variability of neural response during SWM was also examined.

RESULTS

During SWM performance both groups activated the expected networks, and no group differences in network activation or task-related DLPFC-connectivity were found. However, greater individual variability in the pattern of SWM activity was found in the ASD versus the typically developing control group.

CONCLUSIONS

While there were no group differences in SWM task-evoked activity or connectivity, fronto-parietal network engagement was found to be more variable/idiosyncratic in ASD. Our results suggest that the fronto-parietal network may be shifted or sub-optimally engaged during SWM performance in participants with ASD with clinically significant EF impairment, with implications for developing targeted interventions for this subgroup.