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Sequence Memory Constraints Give Rise to Language-Like Structure through Iterated Learning. | LitMetric

AI Article Synopsis

  • This paper investigates how human language can develop through cultural evolution, influenced by memory limitations.
  • The researchers designed a unique experiment where participants learn and recall sequences of non-linguistic information, which in turn shape the learning experience of subsequent participants.
  • The findings indicate a significant increase in structural complexity over generations of recalls, mirroring the statistical properties found in actual languages, suggesting that cultural transmission may play a key role in language development.

Article Abstract

Human language is composed of sequences of reusable elements. The origins of the sequential structure of language is a hotly debated topic in evolutionary linguistics. In this paper, we show that sets of sequences with language-like statistical properties can emerge from a process of cultural evolution under pressure from chunk-based memory constraints. We employ a novel experimental task that is non-linguistic and non-communicative in nature, in which participants are trained on and later asked to recall a set of sequences one-by-one. Recalled sequences from one participant become training data for the next participant. In this way, we simulate cultural evolution in the laboratory. Our results show a cumulative increase in structure, and by comparing this structure to data from existing linguistic corpora, we demonstrate a close parallel between the sets of sequences that emerge in our experiment and those seen in natural language.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5261806PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168532PLOS

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