doi:10.1016/j.biosystems.2006.09.030
Copyright © 2006 Elsevier Ireland Ltd All rights reserved.
Symbols are not uniquely human
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Sidarta Ribeiroa, 1, Angelo Loulab, c, 1, Ivan de Araújoa, Ricardo Gudwinb and João Queirozb, d,
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aInternational Institute of Neuroscience of Natal (IINN), Rua Professor Francisco Luciano de Oliveira, 2460, Bairro Candelária, 59066-060, Natal, RN, Brazil
bDepartment of Computer Engineering and Industrial Automation, School of Electrical and Computer Engineering (FEEC), State University of Campinas (UNICAMP), Av. Albert Einstein 400, 13083-852 Campinas, SP, Brazil
cInformatics Area, Department of Exact Sciences (DEXA), State University of Feira de Santana (UEFS), Km 3, BR116, Campus Universitário, 44031-460 Feira de Santana, BA, Brazil
dInstitute of Biology, Federal University of Bahia (UFBA), Rua Barão de Geremoabo, 147 Campus de Ondina, 40170-290 Salvador, BA, Brazil
Received 1 June 2006;
revised 29 August 2006;
accepted 2 September 2006.
Available online 15 September 2006.
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.
Keywords: Symbols; Semiotic and neurobiological constraints; Computer simulation of symbol learning
Fig. 1. Concepts underlying a neurosemiotic simulation. (a) Minimum brain architecture chosen for our simulations, containing two domains for primary sensory representation (RD1S), one domain for secondary sensory association (RD2) and one domain for primary motor representation (RD1M). (b) Simplified representation of the minimum brain architecture, in which circles stand for domains of representation in the monkey brain (RDs). (c) Control architecture of the apprentice creature used in our simulations. Each contains multiple relays in RD1S and RD1M. (d) Each apprentice contains also some relays dedicated to the association between images and sounds, i.e. RD2. (e) Picture of the simulation console showing preys, predators, bushes and trees.
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Fig. 2. Storyboard of sign process of alarm call communication employing vervet monkeys’ minimum brain architecture. Each frame is constituted of letters, arrows and circles. T1, T2, T3, etc., represent instants in time. External objects presented to a monkey brain comprise predator images (a–c), corresponding alarm-calls vocalized by other monkeys (A–C), and reactive behaviors from neighboring monkeys that may be visible to other brains (F refers to “flee”; S refers to “stay”). Circles stand for domains of representation in the minimum brain (RDs). Circle colors indicate different types of neural representations according to their semiotic relationships—red for object and blue for interpretant. The white color designates a de-activated RD (circle) or the absence of an external object. Arrows represent signs, i.e. patterns of connectivity between brain areas, or between a brain area and the external world. Green arrows indicate instantiation of a connectivity pattern, i.e. the action of a sign translating from an object to an interpretant. Black arrows in T1 indicate latent (inactive) signs. Memory for a representation is indicated as letters outside the boundaries of circles in T1. Information about the particular identity of an external object is represented by outside letters in T1, T9 and T13 (arbitrary moments of occurrence); this information is preserved within the brain as indicated by letter inside circles thereafter. At T3, interpretants within RD1S become internal (neural) objects to be represented downstream, determining the repetition of T3 in the next frame, and so on. Every instantiation of a representation in RD2 causes a slight increase in memory of that representation. Every instantiation of a representation must be interpreted as either an Icon, or an Index, or a Symbol; memory of a representation (A) of the object (a) is defined as the probability of observing (A) given a certain context of object presentation that might or not include (a). In addition, external objects may also carry the capacity to signify reward or punishment. This capacity (object value) is defined as positive and negative variables that can increase or decrease the memory of associated representations. −S refers to negative value imposed on brain representations associated with “stay”; +F refers to positive value imposed on brain representations associated with “flee”. (a) Infant simultaneously presented with predator image and alarm call. (b) Adult simultaneously presented with predator image and alarm call. Once again an escape response is generated earlier (T9) than in infants (T17). This crucial symbolic step occurs in T5, when RD2 interprets the ascending iconic representation “A” as “AaF”). (c) Adult presented with an alarm-call only.
Fig. 3. Naïve preys develop vocal symbol learning spontaneously after a few thousand iterations. The number of iterations required for learning has an inverse relationship with the tutor–apprentice ratio.
Fig. 4. Vocal symbol learning is highly resistant to apprentice-generated auditory noise (a), but is strongly impaired by apprentice-generated visual mis-identification of predators (b) and by tutor unreliability (c).

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1 These authors contributed equally to this work.