中国科学院心理健康重点实验室知识库

自闭症谱系障碍(autism spectrum disorders, ASD)是一种复杂的神经发育疾病,其典型特征是社交障碍、限制和重复的行为和兴趣.此外, ASD个体往往表现出感知功能的非典型性(如感觉超敏感或低敏感).先前的知觉理论只能部分解释ASD个体与典型发育个体(typically developing, TD)之间的差异. Pellicano和Burr提出了贝叶斯预测模型,通过贝叶斯计算建模和预测编码理论将感知过程概念化,从新的角度去理解这种差异.该模型描述了感觉自下而上和认知自上而下的过程如何共同作用,并以不同的方式塑造了ASD的感知.本文对贝叶斯模型进行了全面的回顾,其中最突出的两个理论是“弱先验假设”和“ASD预测误差高且不灵活精度的假设”(HIPPEA).我们从高级的社会认知功能上和不同的感觉通道角度上检验了这些理论.结果显示,目前支持贝叶斯预测理论的证据是混合的,有的研究支持ASD个体的先验不足或在不断变化的环境中调整这些预测的灵活性较差,也有的研究指出ASD个体能够学习预测,拥有完整的预测能力,贝叶斯先验的整合上没有差异,还有一些研究在行为与神经影像学发现不一致.尽管贝叶斯ASD理论很有前途,可以帮助我们更好地理解ASD患者的非典型感知觉,但它在实证方面还面临着挑战.基于此,我们提出了现有研究的局限和未来发展的建议.总之,贝叶斯预测模型自提出以来,已经得到了有效应用和不断的验证.然而,该理论仍然是一个不断发展的概念,未来还需要大量的研究来更新和改进.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by social impairments and restricted and repetitive behaviours and interests. Moreover, individuals with ASD often exhibit atypical perceptual features (i.e., hypersensitivity or hyposensitivity to stimuli). Previous perceptual models such as enhanced perceptual functioning (EPF) and weak central coherence (WCC) offer only partial explanations for differences between individuals with ASD and typically developing (TD) individuals. A novel approach to understanding these differences is the Bayesian framework, which conceptualizes perceptual processes through the lens of Bayesian computational modelling and predictive coding theory. This framework offers insight into how prediction errors (i.e., bottom-up sensory input) and priors (i.e., top-down cognitive processes) jointly influence perception in individuals with ASD in various ways. In this paper, we conducted a comprehensive review of prominent Bayesian models of ASD and delved into the intricate explanations provided by these models for both social and nonsocial symptoms of ASD. To evaluate the validity of these models, we also scrutinized a wide range of empirical evidence derived from behavioural and neuroimaging studies. We identified several notable Bayesian models in the literature, two of the most prominent being the "hypo-prior hypothesis" and the "high, inflexible precision of prediction errors in autism theory" (HIPPEA). Empirical studies have examined these theories at different levels of cognitive processing, ranging from higher-level social cognitive functions to sensory perception across multiple modalities, with varying designs and methodological details. Overall, these studies have provided equivocal support for Bayesian theories. While some studies have suggested that individuals with ASD have a lower weighting of prior expectations than TD individuals, other studies have reported inconsistent or even contrasting findings. Similarly, some studies have reported that individuals with ASD exhibit a decreased ability to adapt prediction error signals to varying contexts, whereas other studies have suggested that the neural coding of prediction errors remains intact in ASD. Furthermore, in some studies, behavioural findings were at odds with neuroimaging findings. These mixed outcomes may be attributed to participant heterogeneity, different learning timescales in the task, different presentation probabilities of stimuli material, and variations in how priors were operationalized. In addition, few empirical studies have made comparisons between different Bayesian theories of ASD or between Bayesian theories and traditional perceptual models, and most previous studies have struggled to distinguish between different types of priors. Although Bayesian theories of ASD are promising and may help us better understand atypical sensory perception in individuals with ASD, they face challenges on the empirical front. For example, there is a lack of comparisons between multiple theories within the same study, and there is a relative scarcity of current neuroscience research. At the theoretical level, following the proposal of the "hypo-priors" hypothesis in 2012, scholars have conducted further studies to develop the hypothesis and provide empirical validation. While the empirical findings have been heterogeneous, this hypothesis has the potential to enhance our comprehension of altered sensory perception in ASD individuals. Ongoing research endeavours will provide substantial empirical data, with ample opportunities to refine the hypothesis and investigatory approach. Subsequent research initiatives should include a comparative analysis of theoretical frameworks within Bayesian theories and expand the integration of neuroimaging studies. In summary, Bayesian theories have demonstrated practical utility and are supported by considerable evidence, thereby contributing to an enriched understanding of atypical sensory perception in individuals with ASD. Nevertheless, Bayesian theories remain an evolving concept, necessitating extensive future research to accommodate updates and refinements.