Infants develop complex visual understanding rapidly, even preceding of the acquisition of linguistic inputs. As computer vision seeks to replicate the human vision system, understanding infant visual development may offer valuable insights. In this paper, we present an interdisciplinary study exploring this question: can a computational model that imitates the infant learning process develop broader visual concepts that extend beyond the vocabulary it has heard, similar to how infants naturally learn? To investigate this, we analyze a recently published model in Science by Vong et al.,which is trained on longitudinal, egocentric images of a single child paired with transcribed parental speech. We introduce a training-free framework that can discover visual concept neurons hidden in the model's internal representations. Our findings show that these neurons can classify objects outside its original vocabulary. Furthermore, we compare the visual representations in infant-like models with those in moder computer vision models, such as CLIP or ImageNet pre-trained model, highlighting key similarities and differences. Ultimately, our work bridges cognitive science and computer vision by analyzing the internal representations of a computational model trained on an infant's visual and linguistic inputs.

本研究探讨了婴儿如何在未获得语言输入之前快速发展复杂的视觉理解，填补了认知科学与计算机视觉之间的研究空白。通过分析一项计算模型，该模型模仿婴儿学习过程，研究发现该模型能识别超出其原有词汇的视觉概念，展示了婴儿学习的独特能力。这一发现可能为计算机视觉系统的发展提供重要启示。 

发现婴儿学习中超越语言输入的隐含视觉概念