Children Learn Words Better in Low Entropy

AbstractDuring 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.

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