Randeniya R, Vilares I, Mattingley J B, Garrido M I
Queensland Brain Institute, The University of Queensland, Australia.
Department of Psychology, University of Minnesota, United States of America.
Comput Psychiatr. 2021 Dec 29;5(1):140-158. doi: 10.5334/cpsy.69. eCollection 2021.
A general consensus persists that sensory-perceptual differences in autism, such as hypersensitivities to light or sound, result from an overreliance on new (rather than prior) sensory observations. However, conflicting Bayesian accounts of autism remain unresolved as to whether such alterations are caused by more precise sensory observations (precise likelihood model) or by forming a less precise model of the sensory context (hypo-priors model). We used a decision-under-uncertainty paradigm that manipulated uncertainty in both likelihoods and priors. Contrary to model predictions we found no differences in reliance on likelihood in autistic group (AS) compared to neurotypicals (NT) and found no differences in subjective prior variance between groups. However, we found reduced context adjustment in the AS group compared to NT. Further, the AS group showed heightened variability in their relative weighting of sensory information (vs. prior) on a trial-by-trial basis. When participants were aligned on a continuum of autistic traits, we found no associations with likelihood reliance or prior variance but found an increase in likelihood precision with autistic traits. These findings together provide empirical evidence for intact priors, precise likelihood, reduced context updating and heightened variability during sensory learning in autism.
人们普遍认为,自闭症患者的感官知觉差异,如对光线或声音的过度敏感,是由于过度依赖新的(而非先前的)感官观察结果所致。然而,关于自闭症的贝叶斯解释存在冲突,即这种改变是由更精确的感官观察(精确似然模型)还是由形成不太精确的感官背景模型(低先验模型)引起的,这一问题仍未得到解决。我们使用了一种不确定性决策范式,该范式同时操纵了似然性和先验性中的不确定性。与模型预测相反,我们发现自闭症组(AS)与神经典型组(NT)相比,在对似然性依赖方面没有差异,且两组之间的主观先验方差也没有差异。然而,我们发现与NT组相比,AS组的情境调整减少。此外,AS组在逐次试验中对感官信息(相对于先验信息)的相对权重表现出更高的变异性。当参与者按照自闭症特征的连续体进行排列时,我们发现与似然性依赖或先验方差没有关联,但发现随着自闭症特征的增加,似然性精度提高。这些发现共同为自闭症患者在感官学习过程中完整的先验、精确的似然性、减少的情境更新和更高的变异性提供了实证证据。
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