Abstract and Keywords
This article investigates approaches adopted to explain the role of cultural transmission in linguistic structure. One of the approaches is to build working models of populations made up of individuals that interact and acquire language from each other, which uncovers the general relationship between learning biases/constraints and emergent language universals. There are three broad approaches to this kind of modeling that include computational/robotic, mathematical, and experimental approaches. There are a number of ways these models could be configured, but the iterated learning model (ILM) provides a framework, which characterizes many of them. The guiding principles for the ILM include that individuals are explicitly modeled, individuals learn by observing instances of behavior, and individuals also produce behavior as a result of learning that then goes on to be input to other individuals' learning. Researchers used a mathematical model of learning placed within the iterated learning framework to try and answer precisely how the nature of the learner impacts on the structure of language. A recent emerging trend is the use of experimental techniques with human participants to build close analogues to the computational and mathematical models of iterated learning in the laboratory. The technique offers several advantages such as it can be used to test the generality of conclusions from models in a situation where the prior bias is provided by real human biology. It can be used to analyze whether results such as the emergence of compositionality from a holistic protolanguage can really occur in a feasible timescale.
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