The effect of labels on nonlinguistic representations is the focus of substantial theoretical debate in the developmental literature. A recent empirical study demonstrated that ten-month-old infants respond differently to objects for which they know a label relative to unlabeled objects. One account of these results is that infants’ label representations are incorporated into their object representations, such that when the object is seen without its label, a novelty response is elicited. These data are compatible with two recent theories of integrated label-object representations, one of which assumes labels are features of object representations, and one which assumes labels are represented separately, but become closely associated across learning. Here, we implement both of these accounts in an auto-encoder neurocomputational model. Simulation data support an account in which labels are features of objects, with the same representational status as the objects’ visual and haptic characteristics. Then, we use our model to make predictions about the effect of labels on infants’ broader category representations. Overall, we show that the generally accepted link between internal representations and looking times may be more complex than previously thought.

中文翻译：

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神经计算模型捕捉学习标签对婴儿对象和类别表示的影响

标签对非语言表征的影响是发展文献中大量理论争论的焦点。最近的一项实证研究表明，相对于未标记的物体，十个月大的婴儿对他们知道标签的物体的反应不同。这些结果的一个解释是婴儿的标签表示被合并到他们的对象表示中，这样当看到没有标签的对象时，就会引发新奇的反应。这些数据与最近两种集成标签-对象表示的理论兼容，其中一种假设标签是对象表示的特征，另一种假设标签单独表示，但在学习过程中密切相关。在这里，我们在自动编码器神经计算模型中实现了这两个帐户。模拟数据支持这样一种说法，其中标签是对象的特征，与对象的视觉和触觉特征具有相同的表示状态。然后，我们使用我们的模型来预测标签对婴儿更广泛的类别表示的影响。总的来说，我们表明，内部表征和观看时间之间普遍接受的联系可能比以前认为的更复杂。