Laboratory of Experimental Psychology, KU Leuven, Leuven, Belgium.
Leuven Autism Research (LAuRes), KU Leuven, Leuven, Belgium.
Autism Res. 2021 Jul;14(7):1484-1495. doi: 10.1002/aur.2511. Epub 2021 Apr 2.
Bayesian predictive coding theories of autism spectrum disorder propose that impaired acquisition or a broader shape of prior probability distributions lies at the core of the condition. However, we still know very little about how probability distributions are learned and encoded by children, let alone children with autism. Here, we take advantage of a recently developed distribution learning paradigm to characterize how children with and without autism acquire information about probability distributions. Twenty-four autistic and 25-matched neurotypical children searched for an odd-one-out target among a set of distractor lines with orientations sampled from a Gaussian distribution repeated across multiple trials to allow for learning of the parameters (mean and variance) of the distribution. We could measure the width (variance) of the participant's encoded distribution by introducing a target-distractor role-reversal while varying the similarity between target and previous distractor mean. Both groups performed similarly on the visual search task and learned the distractor distribution to a similar extent. However, the variance learned was much broader than the one presented, consistent with less informative priors in children irrespective of autism diagnosis. These findings have important implications for Bayesian accounts of perception throughout development, and Bayesian accounts of autism specifically. LAY SUMMARY: Recent theories about the underlying cognitive mechanisms of autism propose that the way autistic individuals estimate variability or uncertainty in their perceptual environment may differ from how typical individuals do so. Children had to search an oddly tilted line in a set of lines pointing in different directions, and based on their response times we examined how they learned about the variability in a set of objects. We found that autistic children learn variability as well as typical children, but both groups learn with less precision than typical adults do on the same task.
自闭症谱系障碍的贝叶斯预测编码理论提出,获得能力受损或更广泛的先验概率分布形状是该病症的核心所在。然而,我们对于儿童如何学习和编码概率分布仍然知之甚少,更不用说自闭症儿童了。在这里,我们利用最近开发的分布学习范式来描述自闭症儿童和非自闭症儿童如何获取有关概率分布的信息。我们对 24 名自闭症儿童和 25 名匹配的神经典型儿童进行了研究,让他们在一组具有不同方向的干扰线中寻找一个异常目标,这些干扰线的方向是从高斯分布中采样的,并且在多次试验中重复出现,以便学习分布的参数(均值和方差)。我们可以通过引入目标-干扰者角色反转,同时改变目标和之前干扰者均值之间的相似性,来测量参与者编码分布的宽度(方差)。两组在视觉搜索任务中的表现相似,并且对干扰分布的学习程度相似。然而,所学到的方差比呈现的方差要宽得多,这与自闭症诊断无关的儿童的信息性先验较差一致。这些发现对整个发展过程中的感知贝叶斯解释以及自闭症的贝叶斯解释具有重要意义。