Ong Jia Hoong, Zhang Lei, Liu Fang
Department of Psychology, School of Social Sciences, Nottingham Trent University, Nottingham, UK.
Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK.
Mol Autism. 2025 Mar 4;16(1):15. doi: 10.1186/s13229-025-00651-7.
According to recent models of autism, autistic individuals may find learning probabilistic cue-outcome associations more challenging than deterministic learning, though empirical evidence for this is mixed. Here we examined the mechanism of probabilistic learning more closely by comparing autistic and non-autistic adults on inferring a target cue from multiple cues or integrating multiple target cues and learning from associations with various predictive strengths.
52 autistic and 52 non-autistic participants completed three tasks: (i) single-cue probabilistic learning, in which they had to infer a single target cue from multiple cues to learn cue-outcome associations; (ii) multi-cue probabilistic learning, in which they had to learn associations of various predictive strengths via integration of multiple cues; and (iii) reinforcement learning, which required learning the contingencies of two stimuli with a probabilistic reinforcement schedule. Accuracy on the two probabilistic learning tasks was modelled separately using a binomial mixed effects model whereas computational modelling was performed on the reinforcement learning data to obtain a model parameter on prediction error integration (i.e., learning rate).
No group differences were found in the single-cue probabilistic learning task. Group differences were evident for the multi-cue probabilistic learning task for associations that are weakly predictive (between 40 and 60%) but not when they are strongly predictive (10-20% or 80-90%). Computational modelling on the reinforcement learning task revealed that, as a group, autistic individuals had a higher learning rate than non-autistic individuals.
Due to the online nature of the study, we could not confirm the diagnosis of our autistic sample. The autistic participants were likely to have typical intelligence, and so our findings may not be generalisable to the entire autistic population. The learning tasks are constrained by a relatively small number of trials, and so it is unclear whether group differences will still be seen when given more trials.
Autistic adults showed similar performance as non-autistic adults in learning associations by inferring a single cue or integrating multiple cues when the predictive strength was strong. However, non-autistic adults outperformed autistic adults when the predictive strength was weak, but only in the later phase. Autistic individuals were also more likely to incorporate prediction errors during decision making, which may explain their atypical performance on the weakly predictive associations. Our findings have implications for understanding differences in social cognition, which is often noisy and weakly predictive, among autistic individuals.
根据近期的自闭症模型，自闭症个体可能会发现学习概率性线索-结果关联比确定性学习更具挑战性，尽管关于这一点的实证证据并不一致。在此，我们通过比较自闭症和非自闭症成年人在从多个线索中推断目标线索、整合多个目标线索以及从具有不同预测强度的关联中学习等方面，更深入地研究概率性学习的机制。
52名自闭症参与者和52名非自闭症参与者完成了三项任务：（i）单线索概率性学习，即他们必须从多个线索中推断出单个目标线索以学习线索-结果关联；（ii）多线索概率性学习，即他们必须通过整合多个线索来学习具有不同预测强度的关联；（iii）强化学习，这需要根据概率性强化计划学习两种刺激之间的偶然性。使用二项混合效应模型分别对两项概率性学习任务的准确性进行建模，而对强化学习数据进行计算建模以获得预测误差整合（即学习率）的模型参数。
在单线索概率性学习任务中未发现组间差异。在多线索概率性学习任务中，对于预测性较弱（40%至60%之间）的关联，组间差异明显，但对于预测性较强（10%至20%或80%至90%）的关联则不明显。强化学习任务的计算建模表明，总体而言，自闭症个体的学习率高于非自闭症个体。
由于研究是在线进行的，我们无法确认自闭症样本的诊断。自闭症参与者可能具有正常智力，因此我们的研究结果可能无法推广到整个自闭症群体。学习任务受试验次数相对较少的限制，因此尚不清楚增加试验次数时是否仍会出现组间差异。
在预测强度较强时，自闭症成年人在通过推断单个线索或整合多个线索来学习关联方面表现与非自闭症成年人相似。然而，在预测强度较弱时，非自闭症成年人的表现优于自闭症成年人，但仅在后期阶段。自闭症个体在决策过程中也更有可能纳入预测误差，这可能解释了他们在预测性较弱的关联上的非典型表现。我们的研究结果对于理解自闭症个体在社交认知方面的差异具有启示意义，社交认知往往具有噪声且预测性较弱。
