Interactions Between Linear Order and Lexical Distributions in Artificial Language Learning

How do children learn the appropriate scope of linguistic generalizations? One proposal is that prediction error and cue competition enable them to implicitly reduce their uncertainty about the various cues to linguistic patterns. Previous work has employed artificial language studies to test the predictions of error-driven models against the performance of (adult) human participants (Ramscar et al., 2010). A critical prediction of these models - that linear relations between linguistic and environmental cues can critically affect generalization - has received much empirical support. For example, Vujovic et al. (2021) found that suffixing languages supported the learning of discriminating cues, and overgeneralization avoidance, better than equivalent prefixing languages. The current study addresses a limitation of previous studies: the use of unnatural flat distributions, which contrast to the skewed distributions ubiquitous in natural language. Although some of our results are consistent with model predictions, there were divergences. Possible reasons for these are discussed.