Symbols are not uniquely human

• Published in: BioSystems Vol. 90; no. 1; pp. 263 - 272
• Main Authors: Ribeiro, Sidarta, Loula, Angelo, de Araújo, Ivan, Gudwin, Ricardo, Queiroz, João
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01-07-2007
• Subjects: Animals; Brain - physiology; Cercopithecus; Computer Simulation; Computer simulation of symbol learning; Humans; Language; Learning; Linguistics; Models, Biological; Models, Neurological; Models, Theoretical; Motor Neurons - metabolism; Semiotic and neurobiological constraints; Symbolism; Symbols; Systems Biology; Vocalization, Animal; Semiotic and neurobiological constraints; Computer simulation of symbol learning; Symbols
• ISSN: 0303-2647; 1872-8324
• DOI: 10.1016/j.biosystems.2006.09.030

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.