Radulescu Silvia, Kotsolakou Areti, Wijnen Frank, Avrutin Sergey, Grama Ileana
Utrecht Institute of Linguistics-OTS, Utrecht University, Utrecht, Netherlands.
Amsterdam Centre for Language and Communication, Faculty of Humanities, University of Amsterdam, Amsterdam, Netherlands.
Front Psychol. 2021 Nov 11;12:661785. doi: 10.3389/fpsyg.2021.661785. eCollection 2021.
The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them () and generalizing rules to novel instances (). Both external factors, such as input variability, and internal factors, such as cognitive limitations, have been shown to drive these abilities. However, the exact dynamics between these factors and circumstances under which rule induction emerges remain largely underspecified. Here, we extend our information-theoretic model (Radulescu et al., 2019), based on Shannon's noisy-channel coding theory, which adds into the "formula" for rule induction the crucial dimension of : the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the based hypothesis of our model: if the is higher than the maximum rate of information transmission (bits/second), which is determined by the , the encoding method moves gradually from to a more efficient , so as to avoid exceeding the . We ran two artificial grammar experiments with adults, in which we sped up the bit rate of information transmission, crucially not by an arbitrary amount but by a factor calculated using the formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove the tendency of learners toward , as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency grammar impeded the of the specific frames, and led to poorer learning, at least judging by our accuracy assessment method. This finding could show that, since increasing the bit rate of information precipitates a change from to , it impedes the of the specific frames, and that it facilitates both for the intervening and possibly for a/b categories. Thus, sped up bit rate does not mean that an unrestrainedly increasing bit rate drives rule induction in any context, or grammar. Rather, it is the specific dynamics between the and the maximum .
年轻学习者和成年学习者的语言能力范围涵盖从记忆特定项目到找出它们之间的统计规律（），以及将规则推广到新的实例（）。外部因素，如输入的变异性，和内部因素，如认知限制，都已被证明会驱动这些能力。然而，这些因素之间的确切动态关系以及规则归纳出现的具体情况在很大程度上仍未明确说明。在这里，我们扩展了基于香农噪声信道编码理论的信息论模型（拉杜列斯库等人，2019年），该模型在规则归纳的“公式”中加入了一个关键维度：由时间敏感机制编码信息的速率。本研究的目的是检验我们模型的一个基本假设：如果高于由确定的信息传输最大速率（比特/秒），编码方法就会逐渐从转变为更有效的，以避免超过。我们对成年人进行了两项人工语法实验，在实验中我们加快了信息传输的比特率，关键是并非任意加快，而是通过使用先前数据的公式计算得出的系数来加快。我们发现，如我们的模型所预测的，在基于重复的XXY语法中增加信息传输比特率会促使学习者倾向于。相反，我们发现，在复杂的非相邻依存语法中增加信息传输比特率会阻碍特定框架的，并且导致学习效果较差，至少从我们的准确性评估方法来看是这样。这一发现可能表明，由于提高信息比特率会促使从转变，它会阻碍特定框架的，并且它会促进中间的以及可能的a/b类别的。因此，加快比特率并不意味着在任何语境或语法中无限制地提高比特率都会驱动规则归纳。相反，这是和最大之间的特定动态关系。
