Translational Neuromodeling Unit (TNU), Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Zurich, Switzerland.
Laboratory for Social and Neural Systems Research, Department of Economics, University of Zurich, Zurich, Switzerland.
Eur J Neurosci. 2020 Dec;52(11):4432-4441. doi: 10.1111/ejn.13972. Epub 2018 Aug 10.
Current theories of object perception emphasize the automatic nature of perceptual inference. Repetition suppression (RS), the successive decrease of brain responses to repeated stimuli, is thought to reflect the optimization of perceptual inference through neural plasticity. While functional imaging studies revealed brain regions that show suppressed responses to the repeated presentation of an object, little is known about the intra-trial time course of repetition effects to everyday objects. Here, we used event-related potentials (ERPs) to task-irrelevant line-drawn objects, while participants engaged in a distractor task. We quantified changes in ERPs over repetitions using three general linear models that modeled RS by an exponential, linear, or categorical "change detection" function in each subject. Our aim was to select the model with highest evidence and determine the within-trial time-course and scalp distribution of repetition effects using that model. Model comparison revealed the superiority of the exponential model indicating that repetition effects are observable for trials beyond the first repetition. Model parameter estimates revealed a sequence of RS effects in three time windows (86-140, 322-360, and 400-446 ms) and with occipital, temporoparietal, and frontotemporal distribution, respectively. An interval of repetition enhancement (RE) was also observed (320-340 ms) over occipitotemporal sensors. Our results show that automatic processing of task-irrelevant objects involves multiple intervals of RS with distinct scalp topographies. These sequential intervals of RS and RE might reflect the short-term plasticity required for optimization of perceptual inference and the associated changes in prediction errors and predictions, respectively, over stimulus repetitions during automatic object processing.
目前关于物体感知的理论强调了知觉推断的自动性质。重复抑制（RS），即大脑对重复刺激的反应连续减少，被认为反映了通过神经可塑性对知觉推断的优化。虽然功能成像研究揭示了大脑区域对物体重复呈现的反应受到抑制，但对于日常物体的重复效应在单次试验内的时间进程知之甚少。在这里，我们使用事件相关电位（ERP）对与任务无关的手绘物体进行了研究，而参与者则参与了分心任务。我们使用三种广义线性模型来量化 ERP 在重复中的变化，这些模型在每个被试中分别通过指数、线性或分类的“变化检测”函数来模拟 RS。我们的目的是选择具有最高证据的模型，并使用该模型确定重复效应在单次试验内的时间进程和头皮分布。模型比较表明，指数模型具有优越性，表明重复效应可在第一次重复之外的试验中观察到。模型参数估计表明，在三个时间窗口（86-140、322-360 和 400-446ms）中存在 RS 效应序列，并且分别具有枕叶、颞顶叶和额颞叶分布。还观察到在枕颞传感器上存在重复增强（RE）的间隔（320-340ms）。我们的结果表明，对与任务无关的物体的自动处理涉及多个具有不同头皮拓扑的 RS 间隔。这些 RS 和 RE 的顺序间隔可能反映了在自动物体处理过程中，对刺激重复进行优化所需的短期可塑性，以及分别与预测误差和预测相关的变化。
