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Implicit learning in 3-year-olds with high and low likelihood of autism shows no evidence of precision weighting differences
Developmental Science ( IF 3.1 ) Pub Date : 2021-07-12 , DOI: 10.1111/desc.13158
Emma K Ward 1 , Jan K Buitelaar 1, 2 , Sabine Hunnius 1
Affiliation  

Predictive Processing accounts of autism claim that autistic individuals assign higher precision to their prediction errors than non-autistic individuals, that is, autistic individuals update their predictions more readily when faced with unexpected sensory input. Since setting the level of precision is a fundamental part of perception and learning, we propose that such differences should be detectable in various domains at a very early age, before clinical symptoms have fully emerged. We therefore tested 3-year-old younger siblings of autistic children, with a high likelihood of later receiving an autism diagnosis themselves, and low-likelihood children with an older sibling without autism. We used a novel implicit learning paradigm to examine the effect of sensory noise on the predictions participants built. In order to learn a sequence, our participants had to select which visual information to attend to and disregard low-level prediction errors caused by the sensory noise, which the theory claims is more difficult for autistic individuals. Contrary to the proposed higher precision-weighting of prediction errors in autism, the high-likelihood children did not show signs of updating their predictions more readily when we added sensory noise compared to the low-likelihood children, either in their reaction times or in the recurrence and determinism of their response locations. These results raise challenges for Predictive Processing theories of autism, specifically for the notion that prediction errors are inflexibly highly weighted by individuals with autism.

中文翻译:

自闭症可能性高低的 3 岁儿童的内隐学习没有显示出精确加权差异的证据

自闭症的预测处理帐户声称自闭症个体比非自闭症个体更精确地分配了他们的预测错误,也就是说,自闭症个体在面对意外的感觉输入时更容易更新他们的预测。由于设置精度水平是感知和学习的基本部分,我们建议在临床症状完全出现之前,应该在很早的时候在各个领域检测到这种差异。因此,我们测试了自闭症儿童的 3 岁弟弟妹妹,他们自己后来接受自闭症诊断的可能性很高,而年龄较大的弟弟妹妹没有自闭症的可能性低。我们使用一种新颖的内隐学习范式来检查感官噪声对参与者建立的预测的影响。为了学习序列,我们的参与者必须选择关注哪些视觉信息,并忽略由感觉噪声引起的低级预测错误,该理论声称这对自闭症个体来说更难。与提出的对自闭症预测误差进行更高精确加权的建议相反,与低可能性儿童相比,当我们添加感官噪音时,高可能性儿童并没有显示出更容易更新他们的预测的迹象,无论是在他们的反应时间还是在其响应位置的重复性和确定性。这些结果对自闭症的预测处理理论提出了挑战,特别是对于预测错误被自闭症患者高度重视的观点。
更新日期:2021-07-12
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