title: Symbols are not uniquely human.
abstract: 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 apprentice-generated 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.
authors.affiliation: International Institute of Neuroscience of Natal, Rua Professor Francisco Luciano de Oliveira, 2460, Bairro Candelária, 59066-060, Natal, RN, Brazil.
authors.forename: Sidarta
authors.initials: S
authors.lastname: Ribeiro
authors.forename: Angelo
authors.initials: A
authors.lastname: Loula
authors.forename: Ivan
authors.initials: I
authors.lastname: de Araújo
authors.forename: Ricardo
authors.initials: R
authors.lastname: Gudwin
authors.forename: João
authors.initials: J
authors.lastname: Queiroz
mesh_terms: D000818:Animals; D001921:Brain / Q000502:physiology; D002521:Cercopithecus; D003198:Computer Simulation; D006801:Humans; D007802:Language; D007858:Learning; D008037:Linguistics; D008954:Models, Biological; D008959:Models, Neurological; D008962:Models, Theoretical; D009046:Motor Neurons / Q000378:metabolism; D013561:Symbolism; D049490:Systems Biology; D014828:Vocalization, Animal