Vergeer Mark, Kogo Naoki, Nikolaev Andrey R, Alp Nihan, Loozen Veerle, Schraepen Brenda, Wagemans Johan
Laboratory of Experimental Psychology, Department of Brain & Cognition, KU Leuven, Belgium; Department of Psychology, University of Minnesota Twin Cities, USA.
Laboratory of Experimental Psychology, Department of Brain & Cognition, KU Leuven, Belgium.
Vision Res. 2018 Nov;152:91-100. doi: 10.1016/j.visres.2018.01.007. Epub 2018 Feb 28.
Shape perception is intrinsically holistic: combinations of features give rise to configurations with emergent properties that are different from the sum of the parts. The current study investigated neural markers of holistic shape representations learned by means of categorization training. We used the EEG frequency tagging technique, where two parts of a shape stimulus were 'tagged' by modifying their contrast at different temporal frequencies. Signals from both parts are integrated and, as a result, emergent frequency components (so-called, intermodulation responses, IMs), caused by nonlinear interaction of two frequency signals, are observed in the EEG spectrum. First, participants were trained in 4 sessions to discriminate highly similar, unfamiliar shapes into two categories, defined based on the combination of features. After training, EEG was recorded while frequency-tagged shapes from either the trained or the untrained shape family were presented. For all IMs combined, no learning effects were detected, but post hoc analyses of higher-order IMs revealed stronger occipital and occipito-temporal IMs for both trained and untrained exemplars of the trained shape family as compared to the untrained shape family. In line with recent findings, we suggest that the higher-order IMs may reflect high-level visual computations, like holistic shape categorization, resulting from a cascade of non-linear operations. Higher order frequency responses are relatively low in power, hence results should be interpreted cautiously and future research is needed to confirm these effects. In general, these findings are, to our knowledge, the first to show IMs as a neural correlate of perceptual learning.
特征组合会产生具有涌现属性的构型，这些属性不同于各部分之和。当前的研究调查了通过分类训练学习到的整体形状表征的神经标记。我们使用了脑电图频率标记技术，通过在不同时间频率下改变形状刺激的两个部分的对比度来对其进行“标记”。来自两个部分的信号被整合，结果，在脑电图频谱中观察到由两个频率信号的非线性相互作用引起的涌现频率成分（所谓的互调响应，IMs）。首先，参与者接受了4节训练课程，以将高度相似、不熟悉的形状区分为两类，这两类是根据特征组合定义的。训练后，在呈现来自训练形状家族或未训练形状家族的频率标记形状时记录脑电图。对于所有组合的互调响应，未检测到学习效应，但对高阶互调响应的事后分析显示，与未训练形状家族相比，训练形状家族的训练和未训练范例在枕叶和枕颞互调响应方面更强。与最近的研究结果一致，我们认为高阶互调响应可能反映了高级视觉计算，如整体形状分类，这是由一系列非线性操作产生的。高阶频率响应的功率相对较低，因此对结果的解释应谨慎，需要未来的研究来证实这些效应。总的来说，据我们所知，这些发现首次表明互调响应是感知学习的神经关联。
