Beyond the data gap: Children create languages, violate their input statistics, and exhibit critical periods

Area of Science:

• Cognitive Science
• Developmental Psychology
• Computational Linguistics

Background:

• Recent advancements in language models (LMs) suggest that domain-general statistical learning mechanisms might explain human language acquisition.
• This perspective posits that complex language abilities can emerge solely from exposure to data, mirroring LM functionalities.

Purpose of the Study:

• To critically evaluate the hypothesis that human language acquisition is solely driven by domain-general statistical learning, as exemplified by LMs.
• To identify key differences between human children and LMs in language learning processes.

Main Methods:

• Comparative analysis of human child language development and LM learning capabilities.
• Examination of phenomena such as over-imitation, developmental trajectories, and critical periods in language acquisition.

Main Results:

• Children demonstrate an ability to surpass their linguistic input, a feat not typically observed in current LMs.
• Human language learning exhibits distinct developmental trajectories and is characterized by the presence of a critical period, unlike LMs.
• LMs, despite their success, do not yet replicate these crucial aspects of child language acquisition.

Conclusions:

• The success of LMs does not definitively prove that human language acquisition relies exclusively on domain-general statistical mechanisms.
• Significant discrepancies between child development and LM learning, particularly regarding input surpassing and critical periods, suggest that innate, language-specific learning mechanisms may play a vital role in human language acquisition.