During their first year, infants learn to name objects. To do so, they need to segment speech, extract the label and map it to the correct referent. While children successfully do so in the wild, previous results suggest they struggle to simultaneously learn segmentation and object-label pairings in the lab. Here, we ask if some of children’s difficulty is related to the uniform distribution they were exposed to, since it differs from that of natural language, and has high entropy (making it less predictable). Will a low entropy distribution facilitate children’s performance in these two tasks? We looked at children’s (mean age=10;4 years) simultaneous segmentation and object-label mapping of words in an artificial language task. Low entropy (created by making one word more frequent) facilitated children's performance in both tasks. We discuss the importance of using more ecologic stimuli in the lab, specifically- distributions with lower entropy.

Do language learners benefit from exposure to input that is more predictable and has lower entropy? Frequency is known to facilitate learning (more frequent words acquired earlier). However, frequency is only one measure of the distributional structure of the linguistic input. Here, we show that entropy also impacts language learning: adults show better word segmentation in an artificial language when the sequence has lower entropy (created by making one word more frequent). Segmentation improved both for the language as a whole, and for the less frequent words, despite appearing half the number of times. These results illustrate the facilitative effect of entropy reduction on language learning. Theoretically, they show that the effect of frequency is relative, not absolute, and that language learners are sensitive to more complex measures of the environment. Methodologically, they suggest that the prevalent use of uniform distributions in word segmentation studies may underestimate learners’ abilities.

Humans can extract co-occurrence regularities from their environment, and use them for learning. This statistical learningability (SL) has been studied extensively. However, almost all SL studies present the regularities to be learned in uniformfrequency distributions (each unit appears equally often). In contrast, real-world learning environments, including thewords children hear and the objects they see, are not uniform, and consequently more predictable than lab-based ones.Recent research shows that word segmentation in children and adults is facilitated after exposure to a Zipfian distribution.Here, we ask if this effect is domain-general by testing children and adults on a visual SL task. Both children and adultsperformed better in the Zipfian distribution compared to the uniform one, overall, and for low-frequency triplets. Theseresults illustrate the impact of distribution predictability on learning across modality and age, and point to the possiblelearnability advantage of skewed distributions in the real-world.

Children are exposed to Zipfian word distributions during language acquisition. Previous studies suggest that such skewed environments confer a learnability advantage in tasks that require the learner to identify the units to be learned, as in word-segmentation or cross-situational learning. The facilitative effect has been attributed to contextual facilitation from the high frequency items in learning the lower frequency items, or to improved error-based learning due to the lower entropy of Zipfian distributions. Here, we ask whether Zipfian distributions facilitate learning beyond unit-identification, as expected under the improved error-based learning explanation. We tested children’s learning of noun-referent mappings in a learning task where each item was presented in isolation during training. Children’s learning was improved overall, and for low frequency items, in two skewed environments with different entropy levels compared to a uniform environment. These results extend the facilitative effect of Zipfian distributions to additional learning tasks, beyond unit identification.