Manning Catherine, Wagenmakers Eric-Jan, Norcia Anthony M, Scerif Gaia, Boehm Udo
Department of Experimental Psychology, University of Oxford, Oxford, UK.
University of Amsterdam, Amsterdam, The Netherlands.
Comput Brain Behav. 2021;4(1):53-69. doi: 10.1007/s42113-020-00087-7. Epub 2020 Jun 19.
Children make faster and more accurate decisions about perceptual information as they get older, but it is unclear how different aspects of the decision-making process change with age. Here, we used hierarchical Bayesian diffusion models to decompose performance in a perceptual task into separate processing components, testing age-related differences in model parameters and links to neural data. We collected behavioural and EEG data from 96 6- to 12-year-old children and 20 adults completing a motion discrimination task. We used a component decomposition technique to identify two response-locked EEG components with ramping activity preceding the response in children and adults: one with activity that was maximal over centro-parietal electrodes and one that was maximal over occipital electrodes. Younger children had lower drift rates (reduced sensitivity), wider boundary separation (increased response caution) and longer non-decision times than older children and adults. Yet, model comparisons suggested that the best model of children's data included age effects only on drift rate and boundary separation (not non-decision time). Next, we extracted the slope of ramping activity in our EEG components and covaried these with drift rate. The slopes of both EEG components related positively to drift rate, but the best model with EEG covariates included only the centro-parietal component. By decomposing performance into distinct components and relating them to neural markers, diffusion models have the potential to identify the reasons why children with developmental conditions perform differently to typically developing children and to uncover processing differences inapparent in the response time and accuracy data alone.
随着年龄增长，儿童对感知信息的决策速度更快且更准确，但决策过程的不同方面如何随年龄变化尚不清楚。在此，我们使用分层贝叶斯扩散模型将一项感知任务中的表现分解为不同的处理组件，测试模型参数中与年龄相关的差异以及与神经数据的关联。我们收集了96名6至12岁儿童和20名成年人完成一项运动辨别任务时的行为和脑电图数据。我们使用一种组件分解技术来识别两个与反应锁定的脑电图组件，儿童和成年人在做出反应之前都有逐渐增强的活动：一个在中央顶叶电极上活动最强，另一个在枕叶电极上活动最强。年幼儿童的漂移率较低（敏感性降低）、边界分离较宽（反应谨慎性增加）且非决策时间比年长儿童和成年人更长。然而，模型比较表明，儿童数据的最佳模型仅包括年龄对漂移率和边界分离的影响（而非非决策时间）。接下来，我们提取了脑电图组件中逐渐增强活动的斜率，并将这些斜率与漂移率进行协变分析。两个脑电图组件的斜率都与漂移率呈正相关，但包含脑电图协变量的最佳模型仅包括中央顶叶组件。通过将表现分解为不同组件并将它们与神经标记相关联，扩散模型有可能识别出发育状况儿童与典型发育儿童表现不同的原因，并揭示仅在反应时间和准确性数据中不明显的处理差异。
