Coll Michel-Pierre, Whelan Emily, Catmur Caroline, Bird Geoffrey
Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2, UK.
Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Road, Oxford OX2, UK.
Cognition. 2020 Jun;199:104236. doi: 10.1016/j.cognition.2020.104236. Epub 2020 Feb 19.
Bayesian accounts of perception, in particular predictive coding models, argue perception results from the integration of 'top-down' signals coding the predicted state of the world with 'bottom-up' information derived from the senses. This integration is biased towards predictions or sensory evidence according to their relative precision. Recent theoretical accounts of autism suggest that several characteristics of the condition could result from atypically imprecise top-down, or atypically precise bottom-up, signals, leading to a bias towards sensory evidence. Whether the integration of these signals is intact in autism, however, has not been tested. Here, we used hierarchical frequency tagging, an EEG paradigm that allows the independent tagging of top-down and bottom-up signals as well as their integration, to assess the relationship between autistic traits and these signals in 25 human participants (13 females, 12 males). We show that autistic traits were selectively associated with atypical precision-weighted integration of top-down and bottom-up signals. Low levels of autistic traits were associated with the expected increase in the integration of top-down and bottom-up signals with increasing predictability, while this effect decreased as the degree of autistic traits increased. These results suggest that autistic traits are linked to atypical precision-weighted integration of top-down and bottom-up neural signals and provide additional evidence for a link between atypical hierarchical neural processing and autistic traits.
贝叶斯感知理论，尤其是预测编码模型，认为感知是由对世界预测状态进行编码的“自上而下”信号与源自感官的“自下而上”信息整合而成的。根据它们的相对精度，这种整合偏向于预测或感官证据。近期关于自闭症的理论表明，该病症的几个特征可能源于自上而下信号的非典型不精确性或自下而上信号的非典型精确性，从而导致偏向于感官证据。然而，自闭症患者中这些信号的整合是否完好尚未得到验证。在此，我们使用了分层频率标记，这是一种脑电图范式，能够独立标记自上而下和自下而上的信号及其整合情况，以评估25名人类参与者（13名女性，12名男性）的自闭症特征与这些信号之间的关系。我们发现自闭症特征与自上而下和自下而上信号的非典型精度加权整合存在选择性关联。自闭症特征水平较低时，随着可预测性增加，自上而下和自下而上信号的整合会出现预期的增加，而随着自闭症特征程度的增加，这种效应会减弱。这些结果表明，自闭症特征与自上而下和自下而上神经信号的非典型精度加权整合有关，并为非典型分层神经处理与自闭症特征之间的联系提供了额外证据。
