Roete Ingeborg, Frank Stefan L, Fikkert Paula, Casillas Marisa
Language Development Department, Max Planck Institute for Psycholinguistics.
Centre for Language Studies, Radboud University.
Cogn Sci. 2020 Dec;44(12):e12924. doi: 10.1111/cogs.12924.
We trained a computational model (the Chunk-Based Learner; CBL) on a longitudinal corpus of child-caregiver interactions in English to test whether one proposed statistical learning mechanism-backward transitional probability-is able to predict children's speech productions with stable accuracy throughout the first few years of development. We predicted that the model less accurately reconstructs children's speech productions as they grow older because children gradually begin to generate speech using abstracted forms rather than specific "chunks" from their speech environment. To test this idea, we trained the model on both recently encountered and cumulative speech input from a longitudinal child language corpus. We then assessed whether the model could accurately reconstruct children's speech. Controlling for utterance length and the presence of duplicate chunks, we found no evidence that the CBL becomes less accurate in its ability to reconstruct children's speech with age.
我们用一个英语的纵向儿童-照顾者互动语料库训练了一个计算模型（基于组块的学习者；CBL），以测试一种提出的统计学习机制——反向过渡概率——是否能够在儿童发育的最初几年里以稳定的准确率预测儿童的言语产出。我们预测，随着儿童年龄的增长，该模型对儿童言语产出的重建准确性会降低，因为儿童逐渐开始使用抽象形式而非来自其言语环境的特定“组块”来生成言语。为了验证这一想法，我们用一个纵向儿童语言语料库中最近遇到的和累积的言语输入来训练该模型。然后，我们评估该模型是否能够准确重建儿童的言语。在控制话语长度和重复组块的存在后，我们没有发现证据表明CBL随着年龄增长在重建儿童言语的能力上变得不那么准确。
