Fast But Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction

The language abilities of young and adult learners range from memorizing specific items to finding statistical regularities between them (item-bound generalization) and generalizing rules to novel instances (category-based generalization). 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 time: the rate of encoding information by a time-sensitive mechanism. The goal of this study is to test the channel capacity-based hypothesis of our model: if the input entropy per second is higher than the maximum rate of information transmission (bits/second), which is determined by the channel capacity, the encoding method moves gradually from item-bound generalization to a more efficient category-based generalization, so as to avoid exceeding the channel capacity. 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 channel capacity 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 category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in complex non-adjacent dependency aXb grammar impeded the item-bound generalization of the specific a_b 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 item-bound to category-based generalization, it impedes the item-bound generalization of the specific a_b frames, and that it facilitates category-based generalization both for the intervening Xs 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 input entropy and the maximum rate of information transmission

The Role of Vocabulary in the Context of the Simple View of Reading

The simple view of reading posits that reading comprehension can be decomposed into a print-specific component (concerning decoding and sight word reading) and a language comprehension component (concerning verbal and metalinguistic skills not related to print). One might properly consider lexical skills, indexed by vocabulary measures, part of the language component; however, vocabulary measures end up taking up substantial amounts of print-dependent reading comprehension variance, presumably because of the interrelations among semantic, orthographic, and phonological specification of lexical entries. In the present study we examined the role of vocabulary in the prediction of reading comprehension by testing alternative formulations within the context of the simple view. We used cross-sectional and (1-year) longitudinal data from 436 children in Grades 3-6 attending regular classrooms. We quantified the proportion of variance accounting for reading comprehension that could be attributed to vocabulary measures. We then tested a latent variable model positing a mediating position for vocabulary against a model with lexically based covariation among the simple view components. We discuss the results in an attempt to bring together the simple view with the lexical quality hypothesis for reading comprehension. © 2013 Copyright Taylor and Francis Group, LLC