A computational psychiatric approach to understanding category representation in autism spectrum disorder

归类是一项重要的认知技能，它使我们能够构建世界，将先前获得的知识转移到新的情境中，并与环境快速互动。为了确定刺激是否属于给定的类别，可以将该项目与存储的类别样本或该类别(原型)的抽象平均值进行比较。重要的是，个体必须在两种策略之间灵活选择，因为最优的表征策略取决于特定的分类问题，自闭症谱系障碍(ASD)患者倾向于关注细节，而较少关注背景信息。尽管在许多分类任务中表现完好，但早期证据表明，注重细节的风格会导致原型抽象的特定困难，这可能是社交困难的先兆。然而，尚不清楚这些困难是否可以通过偏向基于样本的类别表征来弥补。计划中的项目将采用一种高度敏感的、成熟的方法，通过将认知建模与多变量神经成像分析相结合来区分基于样本和基于原型的类别表征。这种方法将使分类策略能够在个体层面上解开。多层次多元回归分析将被用来调查正式的自闭症诊断和自闭症特征的程度是否对分类方式的选择做出独立的贡献，以及这些影响是否对抑郁和焦虑的群体差异具有健壮性。结果将与社会类别学习任务的表现和临床测量(即社会功能、重复行为/受限兴趣、生活质量)有关。类别知识的有效获取也受到对感觉区域的自上而下的影响，例如，先前的类别知识被用来指导知觉加工。在患有自闭症的个体和具有高度自闭症特征的神经典型者中，感觉处理通常较少受到先前经验的调节(低优先理论)。间接证据表明，这一现象延伸到了类别学习，并可以解释为什么自闭症患者的类别知识习得有时会放缓。因此，计划中的项目将采用一种成熟的范式，以揭示抽象类别知识自上而下影响点状图案的感觉类别表征的调制。研究结果将与社会范畴学习任务中的低优先级测量和临床测量相关。拟议的项目旨在促进对ASD基本认知过程的理解，以及它们与典型ASD症状的关系。潜在的临床意义包括确定ASD诊断的神经元生物标记物，以及建议ASD患者适应(社交技能)训练。

Categorization is a vital cognitive skill that allows us to structure the world, transfer previously acquired knowledge to new situations and interact quickly with the environment. To decide whether a stimulus belongs to a given category, the item can be either compared to stored category exemplars or to the abstract average of the category (the prototype). Importantly, individuals have to choose flexibly between the two strategies, as the optimal representational strategy is dependent on the particular categorization problem, Individuals with autism spectrum disorder (ASD) tend to focus on details and to pay less attention to contextual information. Despite intact performance in many categorization tasks, early evidence suggests that the detail-focused style leads to specific difficulties in the abstraction of prototypes, which could be a precursor to social difficulties. However, it is unclear whether these difficulties are compensated by a bias towards exemplar-based category representation.The planned project will employ a highly sensitive, well-established approach to differentiate exemplar- and prototype-based category representation by the combination of cognitive modeling with multivariate neuroimaging analyses. This approach will enable to disentangle categorization strategies on the individual level. Hierarchical multiple regression analyses will be used to investigate whether formal ASD diagnosis and the degree of autistic traits make independent contributions to the choice of categorization style and whether these effects are robust against group differences in depression and anxiety. Results will be related to performance in a social category learning task and clinical measures (i.e. social functioning, repetitive behavior/restricted interests, quality of life).The efficient acquisition of category knowledge is also facilitated by top-down influences on sensory areas, such that prior category knowledge is used to guide perceptual processing. In individuals with ASD and neurotypical persons high in autistic traits, sensory processing is often less modulated by prior experience in (‘hypo-prior’-theory). Indirect evidence suggests that this phenomenon extends to category learning and could explain why category knowledge acquisition in ASD is sometimes slowed down. The planned project will therefore employ a well-established paradigm suitable to uncover the modulation of sensory category representation of dot-patterns by top-down influences of abstract category knowledge. Results will be related to measures of hypo-priors in a social category learning task and clinical measures.The proposed project seeks to advance the understanding of basic cognitive processes in ASD and how they related to classical ASD symptoms. Potential clinical implications include the identification of neuronal biomarkers for ASD diagnosis and suggestions for adaptations of (social skills) trainings for individuals with ASD.