Humans are capable of extracting recurring patterns from their environment via statistical learning (SL), an ability thought to play an important role in language learning and learning more generally. While much work has examined statistical learning in infants and adults, less work has looked at the developmental trajectory of SL during childhood to see whether it is fully developed in infancy or improves with age, like many other cognitive abilities. A recent study showed modality‐based differences in the effect of age during childhood: While visual SL improved with age, auditory SL did not. This finding was taken as evidence for modality‐based differences in SL. However, since that study used auditory linguistic stimuli (syllables), the differential effect of age may have been driven by stimulus type (linguistic vs. non‐linguistic) rather than modality. Here, we ask whether age will affect performance similarly in the two modalities when non‐linguistic auditory stimuli are used (familiar sounds instead of syllables). We conduct a large‐scale study of children's performance on visual and non‐linguistic auditory SL during childhood (ages 5–12 years). The results show a similar effect of age in both modalities: Unlike previous findings, both visual and non‐linguistic auditory SL improved with age. These findings highlight the stimuli‐sensitive nature of SL and suggest that modality‐based differences may be stimuli‐dependent, and that age‐invariance may be limited to linguistic stimuli.

There is growing evidence that cognitive biases play a role in shaping language structure. Here, we ask whether such biases could contribute to the propensity of Zipfian word-frequency distributions in language, one of the striking commonalities between languages. Recent theoretical accounts and experimental findings suggest that such distributions provide a facilitative environment for word learning and segmentation. However, it remains unclear whether the advantage found in the laboratory reflects prior linguistic experience with such distributions or a cognitive preference for them. To explore this, we used an iterated learning paradigm—which can be used to reveal weak individual biases that are amplified overtime—to see if learners change a uniform input distribution to make it more skewed via cultural transmission. In the first study, we show that speakers are biased to produce skewed word distributions in telling a novel story. In the second study, we ask if this bias leads to a shift from uniform distributions towards more skewed ones using an iterated learning design. We exposed the first learner to a story where six nonce words appeared equally often, and asked them to re-tell it. Their output served as input for the next learner, and so on for a chain of ten learners (or ‘generations’). Over time, word distributions became more skewed (as measured by lower levels of word entropy). The third study asked if the shift will be less pronounced when lexical access was made easier (by reminding participants of the novel word forms), but this did not have a significant effect on entropy reduction. These findings are consistent with a cognitive bias for skewed distributions that gets amplified over time and support the role of entropy minimization in the emergence of Zipfian distributions.