The Iterated Classification Game: A New Model of the Cultural Transmission of Language

Abstract

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language.

A simulation study exploring the role of cultural transmission in language evolution

“…Individuals in those models store all linguistic knowledge acquired in previous transmission, even if some knowledge is acquired based on a small number of observations. Still other models (Vogt 2005;Swarup and Gasser 2009) simulate both the learning and competition mechanisms, which may show similar results as our model.…”

Structural Drift: The Population Dynamics of Sequential Learning

“…There are various simulation approaches to ILM with both single and multiple agents based on, for example, neural networks and Bayesian inference, as well as experiments with human subjects. We suggest that structural drift could also serve as the basis for single-agent ILM experiments, as found in Swarup et al [40] , where populations of alleles in the former are replaced by linguistic features of the latter. The benefits are compelling: an information-theoretic framework for quantifying the trade-off between learner bias and transmission bottleneck pressures, visualization of cultural evolution via the CE diagram, and decomposition of the time-to-stasis of linguistic features in terms of isostructural subspaces as presented above.…”

Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games

“…The bidirectional negotiation of the code that takes place during transmission in signaling games may well account for this. Social interaction and repeated communication with the partner may boost the receiverʼs learning [73], leading to a rapid convergence to a shared symbol-state mapping [69]. As a result, a more faithful reproduction of the code is expected after a few generations.…”