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 () 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 .