Althaus Nadja, Gliozzi Valentina, Mayor Julien, Plunkett Kim
School of Psychology, University of East Anglia, Norwich Research Park, Norwich NR4 7TJ, UK.
Center for Logic, Language, and Cognition, Department of Computer Science, University of Torino, C.so Svizzera 185, 10149 Torino, Italy.
R Soc Open Sci. 2020 Oct 21;7(10):200328. doi: 10.1098/rsos.200328. eCollection 2020 Oct.
Recency effects are well documented in the adult and infant literature: recognition and recall memory are better for recently occurring events. We explore recency effects in infant categorization, which does not merely involve memory for individual items, but the formation of abstract category representations. We present a computational model of infant categorization that simulates category learning in 10-month-olds. The model predicts that recency effects outweigh previously reported order effects for the same stimuli. According to the model, infant behaviour at test should depend mainly on the identity of the most recent training item. We evaluate these predictions in a series of experiments with 10-month-old infants. Our results show that infant behaviour confirms the model's prediction. In particular, at test infants exhibited a preference for a category outlier over the category average only if the final training item had been close to the average, rather than distant from it. Our results are consistent with a view of categorization as a highly dynamic process where the end result of category learning is not the overall average of all stimuli encountered, but rather a fluid representation that moves depending on moment-to-moment novelty. We argue that this is a desirable property of a flexible cognitive system that adapts rapidly to different contexts.
对近期发生的事件，识别和回忆记忆更佳。我们探究婴儿分类中的近期效应，这不仅涉及对单个项目的记忆，还涉及抽象类别表征的形成。我们提出了一个婴儿分类的计算模型，该模型模拟了10个月大婴儿的类别学习。该模型预测，对于相同的刺激，近期效应超过了先前报道的顺序效应。根据该模型，测试时婴儿的行为应主要取决于最近训练项目的特征。我们在一系列针对10个月大婴儿的实验中评估了这些预测。我们的结果表明，婴儿的行为证实了该模型的预测。特别是，在测试中，只有当最后一个训练项目接近类别平均值而非远离它时，婴儿才会表现出对类别异常值而非类别平均值的偏好。我们的结果与将分类视为一个高度动态过程的观点一致，在这个过程中，类别学习的最终结果不是所遇到的所有刺激的总体平均值，而是一个根据即时新奇性而变化的灵活表征。我们认为，这是一个灵活认知系统的理想特性，该系统能迅速适应不同的情境。
