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.

来自计算神经科学的工具促进了对心理表征的神经相关性的研究。然而，对生命早期神经激活的代表性内容的访问仍然有限。我们询问是否可以从从 12-15 个月大的婴儿和成人对照收集的 EEG 数据中解码由复杂视觉刺激（动物、人体）引起的神经活动模式。我们在刺激开始后的每个时间点评估了个体婴儿和成人的成对分类准确性。在 500 毫秒内，两组的分类准确率都超过了概率。与成人相比，婴儿的神经表征在视觉域中不是线性可分的。代表在年龄组内相似，但不是跨年龄组。