Department of Computer Science and Center for Logic, Language and Cognition (LLC), University of Turin.
Cogn Sci. 2024 Oct;48(10):e13499. doi: 10.1111/cogs.13499.
We propose a simple computational model that describes potential mechanisms underlying the organization and development of the lexical-semantic system in 18-month-old infants. We focus on two independent aspects: (i) on potential mechanisms underlying the development of taxonomic and associative priming, and (ii) on potential mechanisms underlying the effect of Inter Stimulus Interval on these priming effects. Our model explains taxonomic priming between words by semantic feature overlap, whereas associative priming between words is explained by Hebbian links between semantic representations derived from co-occurrence relations between words (or their referents). From a developmental perspective, any delay in the emergence of taxonomic priming compared to associative priming during infancy seems paradoxical since feature overlap per se need not be learned. We address this paradox in the model by showing that feature overlap itself is an emergent process. The model successfully replicates infant data related to Inter Stimulus Interval effects in priming experiments and makes testable predictions.
我们提出了一个简单的计算模型，该模型描述了 18 个月大婴儿的词汇语义系统组织和发展的潜在机制。我们重点关注两个独立的方面：（i）潜在的机制是分类和联想启动的发展，以及（ii）潜在的机制是刺激间隔对这些启动效应的影响。我们的模型通过语义特征重叠来解释单词之间的分类启动，而单词之间的联想启动则通过来自单词（或其指称物）之间共现关系的语义表示之间的赫布链接来解释。从发展的角度来看，与婴儿期相比，分类启动的出现延迟相对于联想启动似乎是矛盾的，因为特征重叠本身不必被学习。我们通过表明特征重叠本身是一个涌现过程，在模型中解决了这个悖论。该模型成功地复制了与联想启动实验中刺激间隔效应相关的婴儿数据，并提出了可测试的预测。
