Gureckis Todd M, Love Bradley C
Department of Psychology University of Texas at Austin.
Infancy. 2004 Mar;5(2):173-198. doi: 10.1207/s15327078in0502_4. Epub 2004 Mar 1.
Computational models of infant categorization often fail to elaborate the transitional mechanisms that allow infants to achieve adult performance. In this article, we apply a successful connectionist model of adult category learning to developmental data. The Supervised and Unsupervised Stratified Adaptive Incremental Network (SUSTAIN) model is able to account for the emergence of infants' sensitivity to correlated attributes (e.g., has wings and can fly). SUSTAIN offers 2 complimentary explanations of the developmental trend. One explanation centers on memory storage limitations, whereas the other focuses on limitations in perceptual systems. Both explanations parallel published findings concerning the cognitive and sensory limitations of infants. SUSTAIN's simulations suggest that conceptual development follows a continuous and smooth trajectory despite qualitative changes in behavior and that the mechanisms that underlie infant and adult categorization might not differ significantly.
婴儿分类的计算模型常常未能详细阐述使婴儿达到成人表现水平的过渡机制。在本文中，我们将一个成功的成人类别学习联结主义模型应用于发展数据。监督与非监督分层自适应增量网络（SUSTAIN）模型能够解释婴儿对相关属性（如有翅膀且会飞）敏感性的出现。SUSTAIN为这种发展趋势提供了两种互补的解释。一种解释集中在记忆存储限制上，而另一种则侧重于感知系统的限制。这两种解释都与已发表的关于婴儿认知和感官限制的研究结果相呼应。SUSTAIN的模拟表明，尽管行为上有质的变化，但概念发展遵循连续且平滑的轨迹，并且婴儿和成人分类背后的机制可能没有显著差异。
