IEEETransactionsinEvolutionComputation,5,93-101,2001 EvolutionofCommunicationandLanguage UsingSignals,Symbols,andWords AngeloCangelosi categories that are based on iconic and categorical Abstract representations. Initially, each cognitive system builds an This paper describes different types of models for the iconicrepresentationoftheperceivedobject.Itcorresponds evolution of communication and language. It uses the to the sensory representation of an object, such as its distinction between signals, symbols, and words for the projection in theretina. The retinal imageofahorseisan analysis of evolutionarymodels of language.Inparticular,it iconic representation. Subsequently, this representation is show how evolutionary computation techniques, such as processed and used to build a categorical representation. Artificial Life,canbe used tostudytheemergenceofsyntax The object is represented by some essential (indexical) and symbols from simple communication signals. Initially, a computational model that evolves repertoires of isolated features that define its membership in a category. The signalsispresented.Thisstudyhassimulatedtheemergenceof category of horses is an example of categorical signalsfornamingfoodsinapopulationofforagers.Thistype representation. These categorical representations [12] are of model studies communication systems based on simple useful to sort out the extensive perceptual variability of signal-objectassociations.Subsequently,modelsthatstudythe objectsintherealworld.Indeed,theabilityofhumansand emergence of grounded symbols are discussed in general, animals to create categories, e.g. through categorical includingadetaileddescriptionofaworkontheevolutionof perception,constitutesthe“groundwork”ofcognition[11]. simplesyntacticrules.Thismodelfocusesontheemergenceof Upon this basis, it is possible to build more complex symbol-symbol relationships in evolved languages. Finally, cognitive skills, such as language. The next level of computationalmodelsofsyntaxacquisitionandevolutionare discussed. These different types of computational models representation is called symbolic. Symbolic representation provide an operational definition of the signal/symbol/word makes it possible for us to name and describe our distinction. The simulation and analysis of these types of environment, in terms of objects’ categories, their modelswillhelpunderstandingtheroleofsymbolsandsymbol memberships, and their invariant features. The word acquisitionintheoriginoflanguage. “horse”issuchatypeofsymbolicrepresentation.Symbolic representations can be combined together to describe new Index Terms: Evolution of language, Artificial Life, Symbol entities and relations. For example, the word “horse” and grounding,Neuralnetworks “stripe” can be used together to describe the concept of “zebra.” Symbols constitute the basis of language, I. INTRODUCTION especiallyinhumanlanguages. A. Icons,Indices,andSymbols Deacon[8,9]usesahierarchyofreferencingsystemsbased on the three levels of iconic, indexical, and symbolic Analyses of linguistic and communication systems are relationships.Iconsareassociatedwithentitiesintheworld mainly based on the semiotic distinction between icons, indices, and symbols. These distinctions, originally because of stimulus generalisation and conventional introduced by Peirce [22], have been reproposed and similarity.Indicesareassociatedtoworldentitiesbyspatio- temporal correlation or part-whole contiguity. They are slightlyrevisedinrecentlanguageoriginworks(e.g.,[12], [9]). Briefly, Peirce's original distinction between icons, typical of conditional learning based on simple stimulus indices,andsymbolsisbasedonthefactthatan"icon"has association. Indexical references are used in common animal communication systems. Symbols have referential physicalresemblancewiththeobjectitrefersto,an"index" relationships to indexical relationships, and also to other is associated intime/spacewithanobjects,anda"symbol" 1 isbasedonasocialconventionorimplicitagreement. symbols . Human languages are based on the use of such symbols. Harnad [11,12] distinguishes between three types of Recently,Deacon[9]proposedanexplanationoftheorigin representations that are used by natural (and artificial) cognitive systems to build mental representations and of language that is based on this hierarchical referencing classificationsoftheexternalenvironment.Hehypothesises system. His theory relies on the main distinction between communication with and without the use of symbolic thatsymbols(words)originatedasthenamesofperceptual representations to explain the evolutionary gap between Angelo Cangelosi, Centre for Neural and Adaptive Systems and Plymouth 1 Institute of Neuroscience, University of Plymouth, Plymouth PL4 8AA ForacritiqueofDeacon'suseoftheterm"reference"for (UK)[email protected] describingrelationshipsbetweensymbolssee[15] animal and human communication systems. In fact, a theroleofthesemodelsforunderstandingtheevolutionof variety of animal communication systems exist and have cognition would be diminished. Instead, evolutionary and beenstudiedindetail[14].Thereisnoapparentcontinuity artificial life methodologies overcome the symbol between animal communication systems and complex grounding problem. For example, neural networks can be humanlanguages.Noanimal“simplelanguages”havebeen usedformodellingorganisms'neuralandcognitivesystems discovered, i.e., communication systems using some to build iconic and categorical representations. elementaryformsofwordcombinationsorsyntax.Thelack Subsequently, these representations can be used for high- of simplelanguageshelps explain thegapbetween animal levelsymbolicrepresentations.Infact,simulatedorganisms and human communication. Deacon [8,9] ascribes this to can use symbols whose semantic referents are made upof thesymbolacquisitionproblem.Indeed,themaindifference categorical representations, such as the internal between animal and human communication pertains to representations of a neural network.Organisms'iconicand symbolic references. There is a significant difference categorical representations can be activated by the actual between the animal indexical referencing systemof simple presence of their referents in the organism's world, object-signal associations and that of humans’ symbolic therefore by directly grounded symbols in the external associations.Inanimals,simpleassociationsbetweenworld world. entitiesandsignals (e.g.,monkeys’calls)aremostlyinnate andcanbeexplainedbymeremechanismsofrotelearning Thedifferencebetweendifferenttypesofassociations(e.g., and conditional learning. A Vervet monkey always uses a simpleindexicalrelationshipsversussymbolicassociations) call in association with a specific predator [5]. Instead, and their relation to the computationalmodels oflanguage symbolic associations havedoublereferences,onebetween evolutionisrepresentedgraphicallyinFigure1.Theobjects the symbol and the object, and the second between the and symbols used in this example are takenfrom Savage- symbol itself and other symbols. When a complex set of Rumbaugh & Rumbaugh’s [24] experimental stimulus set logical andsyntacticalrelationshipsexistbetweensymbols, in ape language research. In this figure, the upper level wecancallthemwordsanddistinguishgrammaticalclasses always refers to the linguistic representations of of words. A language-speaking human knowsthataword signals/symbols,whilstthelowerlevelreferstotheobjects refers to an object and also that the same word has present in the environment. Note that the relationship grammatical relationships with other words. Due to the between symbols and objects, which constitutes the possible combinatorial interrelationships between words, groundingofsymbolsintoentitiesoftherealworld,isnota there canbe anexponential growthof referencewith each direct link between mental symbols and real objects. newlyaddedword.Syntaxallowsthecombinationofmore Instead,itisalinkbetweenmentalentities(thesymbolsor wordstoexpressnewmeanings.Therefore,eachnewword words) and other mental entities (such as concepts) that of the lexicon can be used to exponentially increase the constitute the semantic reference. Therefore, the objects in overallnumberofmeaningsthatthelanguagecanexpress. the lower level refer to a semantic categorisation that organismscanmakeoftheseobjects.Theserepresentations B. Signals,Symbols,andWordsinLanguageEvolution aremediated bythe organisms' sensorimotorandcognitive In the last decade, computational modelling has been abilities. Solidfoods, such asabanana and anorange, are applied to the study of the evolution of language and represented with a link between themselves, and the two communication. These models deal with different types of drinks are also linked. In fact, apes group food together communication systems. Some rely on the use of simple because they require similar sensorimotor behaviour. In signals, while others use symbolic communication systems Rumbaugh'sexperiments,apesobtainfoodusingavending or complex syntactical structures. Amongst the different machine that gives solid food and pours drinks. Therefore types of computational approaches, evolutionary animals learn that foods are "given," while drinks are computation techniques, such as the synthetic approach of "poured." artificiallife[20,25],canbeusedtostudytheemergenceof communication. This approach permits the study of the Figure 1a represents a communication system based on different stages of semiotic complexity, from simple grounded signals. Communication relies on simple