Department of Psychology, American University, Washington, DC, 20016, USA; Center for Neuroscience and Behavior, American University, Washington, DC, 20016, USA.
Department of Psychology, Princeton University, Princeton, NJ, 08540, USA; Department of Linguistics and Cognitive Science, University of Delaware, Newark, DE, 19716, USA.
Dev Cogn Neurosci. 2020 Oct;45:100860. doi: 10.1016/j.dcn.2020.100860. Epub 2020 Sep 8.
Tools from computational neuroscience have facilitated the investigation of the neural correlates of mental representations. However, access to the representational content of neural activations early in life has remained limited. We asked whether patterns of neural activity elicited by complex visual stimuli (animals, human body) could be decoded from EEG data gathered from 12-15-month-old infants and adult controls. We assessed pairwise classification accuracy at each time-point after stimulus onset, for individual infants and adults. Classification accuracies rose above chance in both groups, within 500 ms. In contrast to adults, neural representations in infants were not linearly separable across visual domains. Representations were similar within, but not across, age groups. These findings suggest a developmental reorganization of visual representations between the second year of life and adulthood and provide a promising proof-of-concept for the feasibility of decoding EEG data within-subject to assess how the infant brain dynamically represents visual objects.
计算神经科学工具促进了对心理表现的神经相关性的研究。然而，早期获取神经激活的代表性内容仍然受到限制。我们想知道是否可以从 12-15 个月大的婴儿和成人对照组收集的 EEG 数据中解码复杂视觉刺激（动物、人体）引起的神经活动模式。我们评估了每个个体婴儿和成人在刺激后每个时间点的成对分类准确性。在两组中，分类准确性都在 500 毫秒内超过了随机水平。与成年人不同，婴儿的神经表现不能在视觉领域之间线性分离。在同一年龄组内的表现相似，但在不同年龄组之间则不然。这些发现表明，在生命的第二年和成年期之间，视觉表现存在发育性的重新组织，并为在个体内解码 EEG 数据以评估婴儿大脑如何动态地表示视觉对象的可行性提供了有希望的概念验证。
