Language Evolution and Computation Bibliography

Modern semiotics is a branch of logics that formally defines symbol-based communication. In recent years, the semiotic classification of signs has been invoked to support the notion that symbols are uniquely human. Here we show that alarm-calls such as those used by African vervet monkeys (Cercopithecus aethiops), logically satisfy the semiotic definition of symbol. We also show that the acquisition of vocal symbols in vervet monkeys can be successfully simulated by a computer program based on minimal semiotic and neurobiological constraints. The simulations indicate that learning depends on the tutor-predator ratio, and that apprenticegenerated auditory mistakes in vocal symbol interpretation have little effect on the learning rates of apprentices (up to 80\% of mistakes are tolerated). In contrast, just 10\% of apprentice-generated visual mistakes in predator identification will prevent any vocal symbol to be correctly associated with a predator call in a stable manner. Tutor unreliability was also deleterious to vocal symbol learning: a mere 5\% of ``lying'' tutors were able to completely disrupt symbol learning, invariably leading to the acquisition of incorrect associations by apprentices. Our investigation corroborates the existence of vocal symbols in a non-human species, and indicates that symbolic competence emerges spontaneously from classical associative learning mechanisms when the conditioned stimuli are self-generated, arbitrary and socially efficacious. We propose that more exclusive properties of human language, such as syntax, may derive from the evolution of higher-order domains for neural association, more removed from both the sensory input and the motor output, able to support the gradual complexification of grammatical categories into syntax.

Here, we study a communication model where signals associate to stimuli. The model assumes that signals follow Zipf's law and the exponent of the law depends on a balance between maximizing the information transfer and saving the cost of signal use. We study the effect of tuning that balance on the structure of signal-stimulus associations. The model starts from two recent results. First, the exponent grows as the weight of information transfer increases. Second, a rudimentary form of language is obtained when the network of signal-stimulus associations is almost connected. Here, we show the existence of a sudden destruction of language once a critical balance is crossed. The model shows that maximizing the information transfer through isolated signals and language are in conflict. The model proposes a strong reason for not finding large exponents in complex communication systems: language is in danger. Besides, the findings suggest that human words may need to be ambiguous to keep language alive. Interestingly, the model predicts that large exponents should be associated to decreased synaptic density. It is not surprising that the largest exponents correspond to schizophrenic patients since, according to the spirit of Feinberg's hypothesis, i.e. decreased synaptic density may lead to schizophrenia. Our findings suggest that the exponent of Zipf's law is intimately related to language and that it could be used to detect anomalous structure and organization of the brain.

A Saussurean communication system exists when an entire communicating population uses a single ``language'' that maps states unambiguously onto symbols and then back into the original states. This paper describes a number of simulations performed with a genetic algorithm to investigate the conditions necessary for such communication systems to evolve. The first simulation shows that Saussurean communication evolves in the simple case where direct selective pressure is placed on individuals to be both good transmitters and good receivers. The second simulation demonstrates that, in the more realistic case where selective pressure is only placed on doing well as a receiver, Saussurean communication fails to evolve. Two methods, inspired by research on the Prisoner's Dilemma, are used to attempt to solve this problem. The third simulation shows that, even in the absence of selective pressure on transmission, Saussurean communication can evolve if individuals interact multiple times with the same communication partner and are given the ability to respond differentially based on past interaction. In the fourth simulation, spatially organized populations are used, and it is shown that this allows Saussurean communication to evolve through kin selection.

Evolution of symbolic language and grammar is studied in a network model. Language is expressed by words, i.e. strings of symbols, which are generated by agents with their own symbolic grammar system. Agents communicate with each other by deriving and accepting words via rewriting rule set. They are ranked according to their communicative effectiveness: an agent which can derive less frequent and less acceptable words and accept words in less computational time will have higher scores. They can evolve by mutational processes, which change rewriting rules in their symbolic grammars. Complexity and diversity of words increase in the course of time. The emergence of modules and loop structure enhances the evolution. On the other hand, ensemble structure lead to a net-grammar, restricting individual grammars and their evolution.

Much animal communication takes place via symbolic codes, where each symbol's meaning is fixed by convention only and not by intrinsic meaning. It is unclear how understanding can arise among individuals utilizing such arbitrary codes, and specifically, whether evolution unaided by individual learning is sufficient to produce such understanding. Using a genetic algorithm implemented on a computer, I demonstrate that a significant though imperfect level of understanding can be achieved by organisms through evolution alone. The population as a whole settles on one particular scheme of coding/decoding information (there are no separate dialects). Several features of such evolving systems are explored and it is shown that the system as a whole is stable against perturbation along many different kinds of ecological parameters.