Fast but Not Furious. When Sped Up Bit Rate of Information Drives Rule Induction - Radulescu, Silvia; Kotsolakou, Areti; Wijnen, Frank; Avrutin, Sergey; Grama, Ileana (Frontiers in Psychology, 2021)
Young and adult learners’ language abilities 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, like input variability, and internal factors, like cognitive limitations, have been shown to drive these abilities. Yet the exact dynamics between these factors and the 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 by using the channel capacity formula on previous data. We found that increased bit rate of information transmission in a repetition-based XXY grammar drove learners’ tendency towards category-based generalization, as predicted by our model. Conversely, we found that increased bit rate of information transmission in a complex non-adjacent dependency aXb grammar impeded item-bound generalization of the specific a_b frames, which 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 item-bound generalization of the specific a_b frames, but 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.