Language Evolution and Computation Bibliography

Linguistic meaning is a convention. This article investigates how such conventions can arise for color categories in populations of simulated 'agents'. The method uses concepts from evolutionary game theory: A language game where agents assign names to color patches and is played repeatedly by members of a population. The evolutionary dynamics employed make minimal assumptions about agents' perceptions and learning processes. Through various simulations it is shown that under different kinds of reasonable conditions involving outcomes of individual games, the evolutionary dynamics push populations to stationary equilibria, which can be interpreted as achieving shared population meaning systems. Optimal population agreement for meaning is characterized through a mathematical formula, and the simulations presented reveal that for a wide variety of situations, optimality is achieved.

The evolution of color categorization is investigated using artificial agent population categorization games, by modeling observer types using Farnsworth-Munsell 100 Hue Test performance to capture human processing constraints on color categorization. Homogeneous populations of both normal and dichromat agents are separately examined. Both types of populations produce near-optimal categorization solutions. While normal observers produce categorization solutions that show rotational invariance, dichromats' solutions show symmetry-breaking features. In particular, it is found that dichromats' local confusion regions tend to repel color category boundaries and that global confusion pairs attract category boundaries. The trade-off between these two mechanisms gives rise to population categorization solutions where color boundaries are anchored to a subset of locations in the stimulus space. A companion paper extends these studies to more realistic, heterogeneous agent populations [J. Opt. Soc. Am. A 26, 1424-1436 (2009)].

The evolution of color categorization is investigated using computer simulations of agent population categorization games. Various realistic observer types are implemented based on Farnsworth-Munsell 100 Hue Test human performance data from normal and anomalous trichromats, dichromats, and humans with four retinal photopigments. Results show that (i) a small percentage of realistically modeled deficient agents greatly affects the shared categorization solutions of the entire population in terms of color category boundary locations; (ii) for realistically modeled populations, dichromats have the strongest influence on the color categorization; their characteristic forms of color confusion affect (i.e., attract or repel) color boundary locations and accord with our findings for homogeneous dichromat populations [J. Opt. Soc. Am. A26, 1414-1423 (2009)]; (iii) adding anomalous trichromats or trichromat aexpertsa does not destabilize the solutions or substantially alter solution structure. The results provide insights regarding the mechanisms that may constrain universal tendencies in human color categorization systems.

Specifying the factors that contribute to the universality of color categorization across individuals and cultures is a longstanding and still controversial issue in psychology, linguistics, and anthropology. The present article approaches this issue through the simulated evolution of color lexicons. It is shown that the combination of a minimal perceptual psychology of discrimination, simple pragmatic constraints involving communication, and simple learning rules are enough to evolve color naming systems. Implications of this result for psychological theories of color categorization and the evolution of color naming systems in human societies are discussed.