When Learners Surpass their Sources: Mathematical Modeling of Learning from an Inconsistent Source

TL;DR

This paper introduces a novel algorithm that models how learners can successfully acquire complex language rules from inconsistent sources, explaining real-world language learning phenomena without additional biases.

Contribution

The paper presents a new algorithm demonstrating how learners can surpass their imperfect sources in language acquisition without extra biases, supported by empirical data from ASL learning.

Findings

The algorithm exhibits a frequency-boosting property for common forms.

It explains how a learner can master language from imperfect input.

It accounts for features observed in Simon's ASL learning process.

Abstract

We present a new algorithm to model and investigate the learning process of a learner mastering a set of grammatical rules from an inconsistent source. The compelling interest of human language acquisition is that the learning succeeds in virtually every case, despite the fact that the input data are formally inadequate to explain the success of learning. Our model explains how a learner can successfully learn from or even surpass its imperfect source without possessing any additional biases or constraints about the types of patterns that exist in the language. We use the data collected by Singleton and Newport (2004) on the performance of a 7-year boy Simon, who mastered the American Sign Language (ASL) by learning it from his parents, both of whom were imperfect speakers of ASL. We show that the algorithm possesses a frequency-boosting property, whereby the frequency of the most common form of the source is increased by the learner. We also explain several key features of Simon's ASL.