期刊
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
卷 372, 期 1711, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rstb.2016.0057
关键词
connectionist modelling; infant; statistical learning; chunking
类别
资金
- Economic and Social Research Council [RES-360-25-056]
- Agence Nationale de la Recherche Scientifique (ANR) [ANR-14-CE28-0017]
- Royal Society
- Agence Nationale de la Recherche (ANR) [ANR-14-CE28-0017] Funding Source: Agence Nationale de la Recherche (ANR)
Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue 'Newfrontiers for statistical learning in the cognitive sciences'.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据