Hua Zihui, Li Tianbi, Shi Ruoxi, Wei Ran, Yi Li
School of Psychological and Cognitive Sciences & Beijing Key Laboratory of Behavior and Mental Health, Peking University, 5 Yiheyuan Road, Beijing, 100871, China.
Department of Psychology, School of Education Science, Qingdao University, Qingdao, China.
Mol Autism. 2025 Aug 4;16(1):39. doi: 10.1186/s13229-025-00674-0.
Language difficulties are common in autism, with several theoretical perspectives proposing that difficulties in forming and updating predictions may underlie the cognitive profile of autism. However, research examining prediction in the language domain among autistic children remains limited, with inconsistent findings regarding prediction efficiency and insufficient investigation of how autistic children incrementally integrate multiple semantic elements during language processing. This study addresses these gaps by investigating both prediction efficiency and incremental processing strategy during spoken language comprehension in autistic children compared to neurotypical peers.
Using the visual world paradigm, we compared 45 autistic children (3-8 years) with 52 age-, gender-, and verbal IQ-matched neurotypical children. Participants viewed arrays containing a target object and three semantically controlled distractors (agent-related, action-related, and unrelated) while listening to subject-verb-object structured sentences. Eye movements were recorded to analyze fixation proportions. We employed cluster-based permutation analysis to identify periods of sustained biased looking, growth curve analysis to compare fixation trajectories, and divergence point analysis to determine the onset timing of predictive looking.
Both groups demonstrated predictions during spoken language comprehension and employed similar incremental processing strategies, showing increased fixations to both target objects and action-related distractors after verb onset despite the latter's incompatibility with the agent. However, autistic children exhibited reduced prediction efficiency compared to neurotypical peers, evidenced by significantly lower proportions of and slower growth rates in fixations to target objects relative to unrelated distractors, and delayed onset of predictive looking. Reduced prediction efficiency was associated with higher levels of autism symptom severity in the autistic group and increased autistic traits across both groups, with autism-related communication difficulties showing the most robust associations.
Our sample included only autistic children without language impairments, limiting generalizability to the broader autism spectrum. The task employed only simple sentence structures in controlled experimental settings, which may not fully capture language processing patterns in naturalistic communication contexts.
While autistic children employ similar incremental processing strategies to neurotypical peers during language comprehension, they demonstrate reduced prediction efficiency. Autism symptom severity and autistic traits varied systematically with prediction efficiency, with autism-related communication difficulties showing the strongest associations. These findings enhance our understanding of language processing mechanisms in autism and suggest that interventions targeting language development might benefit from addressing prediction efficiency, such as providing additional processing time and gradually increasing the complexity of semantic integration tasks.
语言困难在自闭症中很常见，有几种理论观点认为，形成和更新预测的困难可能是自闭症认知特征的基础。然而，关于自闭症儿童语言领域预测的研究仍然有限，关于预测效率的研究结果不一致，并且对自闭症儿童在语言处理过程中如何逐步整合多个语义元素的研究不足。本研究通过调查自闭症儿童与神经典型同龄人在口语理解过程中的预测效率和增量处理策略，填补了这些空白。
使用视觉世界范式，我们将45名自闭症儿童（3至8岁）与52名年龄、性别和言语智商匹配的神经典型儿童进行了比较。参与者在听主谓宾结构的句子时，观看包含一个目标物体和三个语义控制的干扰物（与施事相关、与动作相关和不相关）的阵列。记录眼动以分析注视比例。我们采用基于聚类的置换分析来识别持续偏向注视的时间段，采用生长曲线分析来比较注视轨迹，并采用分歧点分析来确定预测性注视的开始时间。
两组在口语理解过程中都表现出预测能力，并采用了类似的增量处理策略，尽管动作相关干扰物与施事不兼容，但在动词出现后，对目标物体和动作相关干扰物的注视都增加了。然而，与神经典型同龄人相比，自闭症儿童的预测效率较低，相对于不相关干扰物，对目标物体的注视比例显著较低，注视增长率较慢，且预测性注视的开始时间延迟，这证明了这一点。预测效率降低与自闭症组中较高水平的自闭症症状严重程度以及两组中自闭症特征的增加有关，其中与自闭症相关的沟通困难显示出最强烈的关联。
我们的样本仅包括无语言障碍的自闭症儿童，限制了对更广泛自闭症谱系的普遍性。该任务仅在受控实验环境中使用简单的句子结构，可能无法完全捕捉自然交流环境中的语言处理模式。
虽然自闭症儿童在语言理解过程中采用了与神经典型同龄人类似的增量处理策略，但他们的预测效率较低。自闭症症状严重程度和自闭症特征与预测效率系统地变化，其中与自闭症相关的沟通困难显示出最强的关联。这些发现加深了我们对自闭症语言处理机制的理解，并表明针对语言发展的干预措施可能会从解决预测效率中受益，例如提供额外的处理时间并逐渐增加语义整合任务的复杂性。
