leonid i. perlovsky ci culture - 02mci03-perlovsky.qxd.pdfaddress some of the limitations of the...

Leonid I. PerlovskyHarvard University and the US Air ForceResearch Laboratory, USA

1. Introduction

Our ability for language is so easily acquired that wedo not even notice the underlying mystery. Thisintroduction raises a few questions related to lan-guage, emphasizing related scientific difficulties. The

rest of the paper reviews contemporary state of knowledge and

some of the proposed answers. A comprehensive reviewwould take books, so only some general ideas will be high-lighted in what follows. Language is a most important mecha-nism of transmitting cultural information through generations.Languages are created by cultures, but cultures are sustained bylanguages. To sustain this process, a language has to be learnedby every individual human being anew. Every child in every

YAKOV VINKOVETSKY, METAPHYSICAL COMPOSITION

Abstract: The knowledge instinct is a fun-damental mechanism of the mind that dri-ves evolution of higher cognitive functions.Neural modeling fields and dynamic logicdescribe it mathematically and relate to lan-guage, concepts, emotions, and behavior.Perception and cognition, consciousnessand unconscious, are described, while over-coming past mathematical difficulties ofmodeling intelligence. The two mainaspects of the knowledge instinct determin-ing evolution are differentiation and syn-thesis. Differentiation proceeds from vagueand unconscious states to more crisp andconscious, from less knowledge to moreknowledge; it separates concepts fromemotions. Its main mechanism is language.Synthesis strives to achieve unity and mean-ing of knowledge; it is necessary for resolv-ing contradictions, concentrating will andfor purposeful actions. Synthesis connectslanguage and cognition. Its main mecha-nisms are emotionality of languages and thehierarchy of the mind. Differentiation andsynthesis are in complex relationship ofsymbiosis and opposition. This leads tocomplex dynamics of evolution of con-sciousness and languages. Its mathematicalmodeling predicts evolution of cultures.We discuss existing evidence and futureresearch directions.

Digital Object Identifier 10.1109/MCI.2007.901396

ISSN1556-603X/07/$25.00©2007IEEE AUGUST 2007 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 25

26 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2007

corner of the world succeeds in this learning, even withoutformal schooling. How is such a complicated fit, which cannotbe accomplished by the best computers and algorithms accom-plished with ease by every child?

In the 1950s Noam Chomsky recognized that kids learnlanguage without having enough “training samples” for everyrule of grammar. This has been termed the “language acquisi-tion problem.” Chomsky guessed that it is possible because weare born with a specific language-learning module in ourbrain. But what exactly is inborn? There are thousands of dif-ferent languages on Earth. How it is possible that a child of anyethnicity can be raised in any community in an opposite cor-ner of the world, by native or adoptive parents and learn thelanguage of the surrounding community?

What comes first language or cognition? Do we use lan-guage just to name what we have already understood, or isthinking equivalent to language, to properly naming things?Two standard extreme stereotypes may come to mind. Agenius scientist discovers deepest laws of nature in his garage,but has neither desire nor need to explain it in plain languageto other people. Another is of a glib politician who speaks onautopilot, without thinking. Are we like one of those, or dolanguage and cognition come together? If so, what are theresponsible mechanisms of the mind? The next chapter touch-es this complex question.

Language is closely associated with symbolic ability. A word“symbol” involves some mystery. National and religious sym-bols can make war and peace, can inspire people to greatdeeds. At the same time computer screen icons, mathematicalnotations, and traffic signs are also called symbols. How couldthis be? What are the mechanisms of the mind involved inperceiving and creating symbols?

By ages of five or seven, most kids can talk about virtuallyeverything existing in surrounding culture. Does it mean theyreally understand it? A teenager is sure that she mastered theworld. Many of us would be glad to go back and change costlymistakes we did at some points in our lives. But what exactlychanged? Linguists tell us that our knowledge of language doesnot change much after seven. How does our cognitionchange, if language remains the same?

In an engineering class you can recognize a problem, forwhich you do not know a solution. But in real life this doesnot happen too often. Usually we know what to do. Look-ing back years later, we may admit that our knowledge wasnot perfect. But usually it seems that we know all theanswers, or at least we know alternatives that should be eval-

uated. Psychologists tell us this happensbecause such are mechanisms of con-sciousness. We are conscious about whatis more or less clear in our minds. Weare not conscious about alternatives,which we do not know about. This maysound like a tautology, but this is a rootof many problems, scientific and per-sonal. What are neural mechanisms of

conscious and unconscious decisions? In this paper we con-sider mechanisms of interaction of language and cognition,conscious and unconscious.

Social scientists and futurist thinkers hypothesize about thefuture of societies. Material conditions, environment, andglobal warming occupy TV screens. Here we address spiritualconditions, emotionality of languages, interaction between lan-guages and cultures, and neural mechanisms in individualminds, which influence the course of civilizations. We getfrom neural mechanisms to evolution of cultures, identifyproperties of languages that defined historical cultural differ-ences, and identify measurable quantities predicting futureevolution of cultures.

2. Language: Nativism vs. CognitivismDuring the first half of the 20th century, there was little appreci-ation for complicated innate mechanisms of the mind. Therewas no mathematics adequate for findings of Sigmund Freudand Carl Jung. In the second half of the 20th century, computersand robotics gained importance. Huge efforts were expendedtoward making computers smarter by using statistical methods,neural networks, rule systems, and model systems. Knowledgeabout the human mind was used to enhance computer intelli-gence. But every mathematical method failed [1], [2].

When computers appeared in the 1950s, scientists weresure that soon they would exceed human intelligence by far. Itdid not happen. Every mathematical method failed. Theycould neatly solve “toy” problems, but could not scale up tothe real world. Self-learning algorithms, such as statistical pat-tern recognition and neural networks, seemed to learn every-thing on their own, but they needed “too many” trainingsamples. Rule systems required “too many” rules. Adaptivemodel systems were ready to combine advantages of rules withlearning, but they needed “too many” computations to matchmodels to data. After many years of research, the fundamentalmathematical reason for these difficulties has been graduallybecoming clearer. All mathematical approaches used logic atsome point. Rule systems used logic directly. Learning systemsused logic in training procedures; training samples had to bepresented one by one, like logical statements. Model systemsused logic to match models to data subsets. Fuzzy systems usedlogic to set the degree of fuzziness. It turned out that exponen-tial or combinatorial complexity of mathematical procedureswas related to what many consider the most fundamentalmathematical result of the 20th century: inconsistency of logicdiscovered by Gödel in the 1930s. Methods based on logic

What comes first language or cognition? Do we uselanguage just to name what we have already understood,or is thinking equivalent to language, to properlynaming things?

AUGUST 2007 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 27

could not tell the difference between cognition and language.Both used logical statements.

In the 1950s, Noam Chomsky moved linguistics towardstudies of innate mind mechanisms. This direction was latercalled nativism. Nativists used mathematics of logical rules,similar to artificial intelligence. In 1981, Chomsky proposed anew mathematical paradigm in linguistics, rules and parame-ters. This followed model-based systems emerging in engi-neering and mathematical studies of cognition. In 1995,Chomsky’s minimalist program called for simplifying rulestructure of the mind mechanism of language. It moved lan-guage closer to other mind mechanisms, closer to meanings,but stopped at an interface between language and cognition.Chomsky’s linguistics assumes that meanings appear indepen-dently from language.

Cognitive linguistics emerging in the 1970s intended toaddress some of the limitations of the nativist approach. Cog-nitive linguists wanted to unify language with cognition andexplain creation of meanings. They were looking for simplerinnate structures than those postulated by nativists. These sim-pler structures would be sufficient, scientists thought, becausethey would combine language and meaning. In works ofJackendoff (1983), Langacker (1988), Lakoff (1988), Talmy(1988) and others, it was recognized that old divisions domi-nating linguistics were stifling. Dichotomies of meanings(semantic-pragmatic) and dichotomies of hierarchical struc-tures (superordinate-subordinate) were limiting scientific dis-course and had to be overcome. Consider the followingopinions on meaning creation:

“in a hierarchical structure of meaning determination thesuperordinate concept is a necessary condition for thesubordinate one… COLOR is a necessary condition fordetermining the meaning of RED” (Jackendoff, 1983)

“The base of predication is nothing more than… domainswhich the prediction actually invokes and requires” (Lan-gacker, 1988)

These examples illustrate the difficulties encountered whenattempting to overcome old dichotomies. Both examples areinfluenced by unstated assumption that the mind works logical-ly. Attempts to implement mathematical mechanisms assumedby these examples would lead to combinatorial complexity. Toput it jovially, problems of meaning and hierarchy demand theold question about the chicken and the egg: what came first? Ifsuperordinate concepts come before subordinate ones, where dothey come from? Are we born with the concept COLOR inour minds? If predictions invoke domains, where do domainscome from? These questions with millennial pedigrees areanswered mathematically in the following sections, and brieflyhere: hierarchy and meaning emerge jointly with cognition andlanguage. In evolution and individual learning, superordinateconcepts (COLOR) are vaguer, less specific, and less consciousthan subordinate ones (RED). RED can be vividly perceived,

but COLOR can not be perceived. RED can be perceived byanimals. But, the concept COLOR can only emerge in thehuman mind, due to joint operation of language and cognition.

A controversy between nativists and cognitivists does notimply that linguists doubt the importance of innate mecha-nisms or the importance of learning and using language. Thecontroversy is about what exactly is innate. In “RethinkingInnateness: a Connectionist Perspective on Development,”Jeffrey Elman et al (1996) demonstrated that many aspects oflanguage acquisition can be explained within the neural net-work framework. Still, Elman emphasized a hard learned les-son: “there is no … general purpose learning algorithm thatworks equally well across domains” (1993, p.1).

Does it mean that our mind uses a huge number ofdiverse algorithms for language and cognition as proposed byM. Minsky (1988)? Or are there fundamental first principlesof the mind organization (see discussion in Perlovsky, 2004)?Among such principles is the evolution of abstract notions,from vague and fuzzy toward specific and concrete (Elman,1993, p.14; Olguin & Tomasello, 1993; Tomasello &Olguin, 1993). In the following sections we describe dynam-ic logic that systematically utilizes this principle. We will alsoaddress another important principle of the mind organizationbrought up by Nolfi, Elman, & Parisi (1994): learning ismotivated by internal drives.

3. The Knowledge InstinctTo satisfy any instinctual need—for food, survival, procreation—first and foremost we need to understand what isgoing on around us. The knowledge instinct is an inborn mech-anism in our minds, an instinctual drive for cognition, whichcompels us to constantly improve our knowledge of the world.

Humans and higher animals engage in exploratory behavior,even when basic bodily needs, like eating, are satisfied. Biolo-gists and psychologists have discussed various aspects of thisbehavior. Harry Harlow discovered a drive for positive stimula-tion [3]; David Berlyne emphasized curiosity as a desire foracquiring new knowledge [4]; Leon Festinger discussed thenotion of cognitive dissonance and human drive to reduce it[5]. Until recently, however, it was not mentioned among‘basic instincts’ on a par with instincts for food and procreation.

The fundamental nature of this mechanism became clear inthe result of mathematical modeling of the mind. Our knowl-edge always has to be modified to fit current situations. Wedon’t usually see exactly the same objects as in the past: angles,illumination, and surrounding contexts are usually different[6]–[8]. In fact, virtually all learning and adaptive algorithms(tens of thousands of publications) maximize correspondencebetween the algorithm internal structure (knowledge in a widesense) and objects of recognition. Internal mind representa-tions, or models, which our mind uses for understanding theworld, are in constant need of adaptation. Without adaptationof internal models we will not be able to understand theworld. We will not be able to orient ourselves or satisfy any ofthe bodily needs. Therefore, we have an inborn need, a drive,


an instinct to improve our knowledge; we call it the knowledgeinstinct. It is a foundation of our higher cognitive abilities, andit defines the evolution of consciousness and cultures.

4. Neural Modeling FieldsNeural Modeling Fields (NMF) is a neural architecture thatmathematically implements the knowledge instincts as well asmechanisms of the mind discussed by psychologists and cogni-tive scientists: emotions, concepts, adaptive behavior, and lan-guage. It is a multilevel, heterarchical system [9]. The mind isnot a strict hierarchy; there are multiple feedback connectionsamong adjacent levels, hence the term heterarchy. At each levelin NMF, there are concept-models encapsulating the mind’sknowledge; they generate so-called top-down neural signals,interacting with input, and bottom-up signals. These interac-tions are governed by the knowledge instinct, which drivesconcept-model learning, adaptation, and formation of newconcept models for better correspondence to the input signals.

At a particular hierarchical level, we enumerate neuronsby index n = 1, . . . , N . These neurons receive bottom-upinput signals, X(n), from lower levels in the processinghierarchy. X(n) is a field of bottom-up neuronal synapseactivations, coming from neurons at a lower level. Eachneuron has a number of synapses; for generality, wedescribe each neuron activation as a set of numbers,X(n) = {X d(n), d = 1, . . . D}. Top-down, or priming sig-nals to these neurons, are sent by concept-models,Mh(Sh, n); we enumerate models by index h = 1, . . . H .Each model is characterized by its parameters, Sh ; in theneuron structure of the brain they are encoded by strengthof synaptic connections. Mathematically, we describe themas a set of numbers, Sh = {Sa

h, a = 1, . . . ,A}. Models repre-sent signals in the following way: say signal X(n) is comingfrom sensory neurons activated by object h, characterizedby a model Mh(Sh, n)) and parameter values Sh . Theseparameters may include position or orientation of an objecth. Model Mh(Sh, n) predicts a value X(n) of a signal at neu-ron n. For example, during visual perception, a neuron n inthe visual cortex receives a signal X(n) from the retina anda priming signal Mh(Sh, n) from an object-concept-modelh. A neuron n is activated if both the bottom-up signal,from lower level input, and top-down priming signal arestrong. Various models compete for evidence in the bot-tom-up signals, while adapting their parameters for a bettermatch, as described below. For concreteness I refer here toan object perception using a simplified terminology, as ifperception of objects occurs in a single level. The mostbenign everyday visual perception uses many levels fromretina to object perception. The NMF premise is that thesame laws describe the basic interaction dynamics at eachlevel. Perception of minute features, or everyday objects, orcognition of complex abstract concepts is due to the samemechanism described in this section. In perception, modelscorrespond to objects; in cognition, models correspond torelationships and situations.

The knowledge instinct drives learning, which is essentialfor perception and cognition. Learning increases a similaritymeasure between models and signals, L ({X}, {M}). The simi-larity measure is a function of model parameters and associa-tions between the input bottom-up signals and top-down,concept model signals. It can be written as follows [2]:

L ({X}, {M}) =∏

n ∈ N

∑

h ∈ H

r(h)1(X(n)|h). (1)

The product here is taken over signals n, and the sum is overvarious models h. Partial conditional similarities l(X (n)|h), orfor shortness l(n|h), measure how similar is signal n to the pre-diction of model h. The structure of (1) follows standard princi-ples of the probability theory: a summation is taken overalternatives, h, and various pieces of evidence, n, are multiplied.This expression is not necessarily a probability, but it has a prob-abilistic structure. In probability theory, a product of probabili-ties usually assumes that evidence is independent. Expression (1)contains a product over n, but it does not assume independenceamong various signals X(n). Partial similarities l(n|h) depend ondifferences between signals and models; these differences are dueto measurement errors and can be considered independent.Dependence among signals are due to models. Each modelM(hSh, n) predicts expected signal values in many neurons n.

The knowledge instinct demands maximization of the simi-larity (1) by estimating model parameters S and associating sig-nals with concepts. Note that all possible combinations ofsignals and models are accounted for in expression (1). Thiscan be seen by expanding a sum in (1), and multiplying all theterms; it would result in HN items, a very large number. Thatis the number of combinations between all signals (N) and allmodels (H). This very large number of combinations was asource of combinatorial complexity difficulties in developingintelligent algorithms and systems since the 1950s [1]. As dis-cussed, the problem was encountered in all methods of com-putational intelligence, and is related to the most fundamentalmathematical results of the 20th century, Gödel theory ofinconsistency of logic [10], [11].

Neural Modeling Fields is a heterarchical architecture mod-eling the mind. Its dynamics is driven by the knowledgeinstinct. Mathematically, it maximizes similarity between mod-els and data by fitting model parameters. The combinatorialcomplexity is overcome by dynamic logic, an iterative proce-dure that matches fuzziness-vagueness of similarity to uncer-tainty of models. These illogical operations result inapproximately logical solutions.

5. Dynamic Logic

5.1 From Vague to Concrete KnowledgeNMF solves the CC problem by using dynamic logic [1],[12]–[14]. Its important aspect is matching vagueness of simi-larity measures to the uncertainty of models. Initially, parame-ter values are not known, and uncertainty of models is high; so


is the fuzziness of the similarity measures. In the process oflearning, as models become more accurate, and the similaritymeasure more crisp, the value of the similarity increases. Pro-gressing from vague and uncertain to concrete and exact is themechanism of dynamic logic.

Mathematically, it is described as follows: first, assign anyvalues to unknown parameters, {Sh}. Then, compute associa-tion variables f (h|n),

f (h|n) = r(h) l(X(n)|h)/∑

h ′ ∈ H

r(h′) l(X(n)|h′). (2)

Equation (2) looks like the Bayes formula for a posterioriprobabilities; if l(n|h), in the result of learning, become condi-tional likelihoods, f (h|n) become Bayesian probabilities forsignal n originating from object h. The dynamic logic of NMFis defined by

dSh/d t =∑

n ∈ N

f (h|n)[∂ ln l(n|h)/∂Mh]∂Mh/∂Sh . (3)

When solving this equation iteratively, at every step f (h|n) iscomputed according to (2), using parameter values from the pre-vious iteration step. Parameter t is the time of the internaldynamics of the MF system (like a number of internal iterations).

The dynamic evolution of fuzziness from large to small isthe reason for the name “dynamic logic.” This helps avoidlocal maxima during convergence [9], [12], and psychological-ly it explains many properties of the mind, as discussed later.Functional shapes used for conditional partial similarities oughtto allow for this process of matched convergence in parametervalues and similarity crispness.

Dynamic logic was proved to be a convergent process [9].It converges to the maximum of similarity, and therefore satis-fies the knowledge instinct. Several aspects of NMF conver-gence, in particular global vs. local convergence, werediscussed in [2]. In particular, the similarity measure increasesat every iteration. The psychological interpretation is that theknowledge instinct is satisfied at each step; an NMF systemwith dynamic logic enjoys learning.

5.2 ExampleOperations of NMF are illustrated in Figure 1 using an exam-ple of detection and tracking moving objects in clutter [15].Tracking is a classical problem, which becomes combinatorial-ly complex when target signals are below the clutter level.Solving this problem is usually approached by using multiplehypotheses tracking algorithm [16], which evaluates multiplehypotheses about which signals came from which of the mov-ing objects, and which from clutter. It is known to encounterCC [9], because large numbers of combinations of signals andmodels have to be considered. Figure 1 illustrates NMF whilesolving this problem.

Figure 1(a) shows true track positions, and (b) shows theactual data available for detection and tracking. It contains sixsensor scans on top of each other (time axis is not shown). Thedata set consists of 3000 data points, 18 of which belong tothree moving objects. In this data, the target signals are weakerthan clutter (by factor of 2). Figures 1(c)–(h) illustrate evolu-tion of the NMF models as they adapt to the data during itera-tions; (c) shows the initial vague-fuzzy model, while (h) showsthe model upon convergence at 20 iterations. Between (c) and(h), the NMF neural network automatically decides how manymodels are needed to fit the data, and simultaneously adapts

FIGURE 1 Detecting and tracking objects below clutter using NMF: (a) true track positions; (b) actual data available for detection and tracking. Evo-lution of the NMF neural network driven by the knowledge instinct is shown in (c)—(h), where (c) shows the initial, uncertain, model and (h) showsthe model upon convergence after 20 iterations. Converged model (h) are in close agreement with the truth (a). Performance improvement ofabout 100 in terms of signal-to-clutter ratio is achieved due to dynamic logic evolution from vague and uncertain models to crisp and certain.

0 Cross-Range1 km(b) (c) (d)(a)

(f) (g) (h)(e)


the model parameters. There are two types of models: one uni-form model describing clutter (it is not shown), and lineartrack models with large uncertainty. In (c) and (d), the NMFneural network fits the data with one model, and uncertainty issomewhat reduced. Between (d) and (e), NMF decides that itneeds two models to ‘understand’ the content of the data. Fit-ting with two tracks continues until (f). Between (f) and (g) athird track is added. Iterations stop at (h), when similarity stopsincreasing. Detected tracks closely correspond to the truth (a).In this case, NMF successfully detected and tracked all threeobjects and required only 106 operations, whereas a straight-forward application of multiple hypotheses tracking wouldrequire HN ∼ 101500 operations. This number, larger than thesize of the universe and too large for computation, preventspast algorithms from solving this problem. NMF, overcomingthis difficulty, achieved about 100 times improvement in termsof signal-to-clutter ratio. This improvement is achieved byusing dynamic evolution from vague and uncertain models tocrisp and certain (instead of sorting through combinations).

5.3 Integrating Language and CognitionNMF can be extended to language as described in [17], [18],and can integrate language and cognition as in [1]. Let meemphasize that we do not know neural mechanisms combin-ing language with thinking, nor their exact locations in thebrain. Psychological ideas still refer to associationism of J.Locke [19]. Mathematical mechanisms discussed for unifyingcognition and language (Elman, 1996; Jackendoff, 2002;Christiansen and Kirby, 2003; Brighton et al, 2005) face CCfor the same mathematical reasons discussed earlier. NMF uni-fies cognition and language, while avoiding CC.

How cognition interacts with language? Do we think withwords, or do we use words only after thinking is accomplished?Integration of language and cognition in NMF is attained byintegrating cognitive and language models (Perlovsky, 2002;2004), so that a concept-model Mh is given by

Mh = {MC

h ,MLh

}. (4)

Here, MCh denotes a cognitive part of the model of an object

or situation in the world, and MLh is a linguistic part of the

model. Consider operation of this integrated model. A datastream constantly comes into the mind from all sensory per-ceptions; every part of this data stream is constantly evaluatedand associated with models (4), according to the mechanismsof dynamic logic. This process begins with fuzzy models; cog-nitive models vaguely correspond to uncertain undifferentiated

sensory perceptions. Language modelsvaguely correspond to sounds. This isapproximately the state of mind of a new-born baby. After one year of age, lan-guage models are differentiated muchfaster than cognitive models. By five orseven, a child can talk about an entireculture. Every child acquires language

models ready-made, differentiated, without much effort or lifeexperience. Such are inborn language mechanisms, whichStephen Pinker called the Language Instinct [20]. But it willtake the rest of his or her life to connect this knowledge oflanguage to cognition. Language models in (4) provide indirectreferences for vague-fuzzy cognitive models. Driven by theknowledge instinct, a child will differentiate these cognitivemodels so that they correspond to life experience. Most of ourlife language drives our cognition.

Due to structure (4), association between language and cogni-tive models occurs before any of the models attain a high degreeof specificity characteristic of the grown-up conscious concepts.Language and cognition are integrated at a pre-conscious level.Knowledge of language forces the human mind to acquire cog-nition. I suggest that this is a mechanism of interaction betweenlanguage and cognition. Both abilities enhance each other. Struc-ture (4) provides for a cognitive placeholder fuzzy model for eachlanguage model, and vice versa. In this way, both types of modelsare gradually learned and remain associated. NMF resolves thefundamental difficulty of associationism: there are not enoughtraining examples in life to learn associations between words andcognitive concepts; associations are inborn as “potentialities”, stillflexible enough to learn real associations from surrounding lan-guage and culture and to develop new ones. In this way, knowl-edge is accumulated through generations.

5.4 The Knowledge Instinct vs. Language InstinctThroughout life, speed of learning and adaptation of cognitiveand language models differs. Mechanisms of the knowledgeinstinct driving adaptation and acquisition of language andcognitive models are significantly independent. This schism isso strong that Chomsky insisted on language being separateand independent from cognition. Pinker considered the Lan-guage Instinct [20]. Whereas the language instinct drives lan-guage models to correspond to a specific part of the world, thesurrounding language signals, the knowledge instinct drivescognitive models to correspond to the world around us. Usu-ally, language signals comprise speech. However, deaf lan-guages indicate that language models are not necessarily speechmodels, but more generally referential models. The languageinstinct operates in such a way that normal babies separatespeech from other sounds before the second year of life. Thisseparation makes possible significantly independent operationsof the two instincts. It seems the language instinct operatesuntil five or seven.

Aesthetic emotional mechanisms relate language to poet-ry and songs. Instrumental music perception evolved from

The knowledge instinct is an inborn mechanism in ourminds, an instinctual drive for cognition, which compelsus to constantly improve our knowledge of the world.


these mechanisms [21], [22], [23]. They are not yet wellstudied. This entire area of science is just emerging (wecontinue this discussion near the end of the paper.) Herewe summarize a few of the current conclusions necessaryfor this paper. Language evolved for creating and exchang-ing conceptual information. Animals can not separate con-cepts from emotions. Nevertheless, significant emotionalityis involved in language. Emotionality of languages is relatedto sound of speech, which appeal to ancient emotional lim-bic brain centers. Connections between conceptual andemotional contents significantly differ among languages.Since the 15th century, English lost its inflections. As aresult, its sounds became “fluid.” Within a few generations,it departed significantly from other Germanic languages (theGreat Vowel Shift). Millennia old connections betweensounds and meanings in English weakened. Today, Englishis the least inflected among European languages, and itsmeanings are least connected to emotions of English speak-ers. More inflected languages, like German and Russian,maintained this old connection of emotions and meanings.Even more inflected and more emotional are Semitic lan-guages, like Arabic. We discuss later that “too much emo-tion” might interfere with cognition. Still, emotionalconnections between language sounds and meanings areimportant for connecting cognitive and language models.

6. Hierarchy of Language and CognitionThe previous section described a single processing level in NMFsystem integrating language and cognition. This mechanism ofintegrated models can integrate cognitive and language hierar-chies as illustrated in Figure 2 (Perlovsky 2002; 2004; 2006).

Deacon (1998) suggested that the ability for two hierar-chies sets the human mind apart from the rest of the animalworld. For example, a dog can learn to bring shoes to ahuman master on a verbal command. A dog, it seems, canjointly learn language and cognition (a word “shoes” and anobject shoes). This is only true, however, at the lower levelsof the mind hierarchy, at the level ofobjects. The dog can do it because itperceives objects, shoes, in the world.Note that such a direct grounding insensory signals exists only at the verybottom of the mind hierarchy. Athigher levels, cognitive concepts aregrounded in lower level concepts.These higher levels exist only in thehuman mind. Try to teach a dog tounderstand the word “rational,” or anyabstract concept, which meaning isbased on several hierarchical levels; thisis not possible. It is known that thesmartest chimps after long training canbarely understand few concepts at thesecond level (Savage-Rumbaugh &Lewine, 1994).

Ability for learning higher levels of cognitive hierarchy,learning of abstract models, is closely related to the ability forlanguage. Otherwise, learning of cognitive models is ground-less. Abstract concepts cannot be directly perceived in theworld. The only ground for learning abstract cognitive con-cepts is language concepts, which are learned from surround-ing language and culture at many hierarchical levels. Due tointegration of language and cognition, language providesgrounding for abstract higher cognitive models.

Cognitive models that proved useful in life and evolutioncannot be directly transferred to the minds of the next genera-tion. Cognitive models are transferred to next generationsthrough language. Cultural evolution gradually selects usefulmodels. Language accumulates cultural knowledge at all levelsin the hierarchy of the mind.

Mechanisms of integration of cognition and languagegiven by dual models, equation (4), and dual hierarchies,Figure 2, are as if a bridge between nativist and cognitivistlinguistic approaches. Many of the mechanisms for languageand cognition discussed in literature (such as Chomsky,1995; Elman et al, 1996; Pinker, 2000; Lieberman, 2000;Jackendoff, 2002; Tomasello, 2003) can be integrated withNMF structure and take advantage of the dynamic logicovercoming CC.

Cultural evolution of language and cognition, as well asontological learning by a child, could be supported by sim-ilar mechanisms. For example, Brighton et al (2005)demonstrated that combinatorial compositionality of lan-guage emerges from a single simple mechanism: an abilityto guess-predict sounds for new situations from previousexperiences (so that new sounds are understandable by therest of the community). This accurate guessing is alsoinherent to the NMF because NMF models predict expect-ed signals. Implementing the Brighton et al approach withNMF will overcome current CC of that work, and explainjoint evolution of language and cognition (Perlovsky &Fontanari, 2006).

FIGURE 2 Hierarchical integrated language-cognition MF system. At each level in a hierarchythere are integrated language and cognition models. Similarities are integrated as products oflanguage and cognition similarities. Initial models are fuzzy placeholders, so integration of lan-guage and cognition is sub-conscious. Association variables depend on both language and cogni-tive models and signals. Therefore, language model learning helps cognitive model learning, andvice versa. Abstract cognitive concepts are grounded in abstract language concepts.

Similarity Action

ActionSimilarity

Similarity Action

ActionSimilarity

Cognition Language

M

M M

M


7. Symbols in Computational Intelligence and Linguistics“Symbol is the most misused word in our culture,”(Terrence Deacon, 1998). We use this word for trivialobjects, like traffic signs or mathematical notations, and alsoto denote objects affecting entire cultures over millennia, likeMagen David, Swastika, Cross, or Crescent. In the develop-ment of scientific understanding of symbols and semiotics,the two functions, understanding language and understandingworld, have often been perceived as identical. This tendencywas strengthened by considering logic to be the mechanismof both language and cognition. According to Bertrand Rus-sell (1919), language is equivalent to axiomatic logic, “[aword-name] merely to indicate what we are speaking about;[it] is no part of the fact asserted … it is merely part of thesymbolism by which we express our thought.” David Hilbert(1928) was sure that his logical theory also describes mecha-nisms of the mind, “The fundamental idea of my proof theo-ry is none other than to describe the activity of ourunderstanding, to make a protocol of the rules according towhich our thinking actually proceeds.”

This belief in logic has deep psychological roots related tothe functioning of the human mind. A major part of any per-ception and cognition process is not accessible to consciousnessdirectly. We are conscious about the final states of theseprocesses, which are perceived by our minds as conceptsobeying formal logic. Possibly for this reason, logical positivismcentered on “the elimination of metaphysics through the logi-cal analysis of language.” According to Rudolf Carnap (1928),logic was sufficient for the analysis of language. Similar under-standing of relationships among symbol, language, logic, andthe mind can be traced in semiotics of Ferdinand Saussure(1916) and in structuralism.

Compare opinions of two founders of contemporary semi-otics, Charles Peirce (Peirce, 19–20th century) and FerdinandDe Saussure (1916). Peirce classified signs into symbols, index-es, and icons. Icons have meanings due to resemblance to thesignified objects, situations, etc.; indexes have meanings bydirect connection to the signified; and symbols have meaningdue to arbitrary conventional agreements. Saussure chose dif-ferent terminology, emphasizing that the sign receives meaningdue to arbitrary conventions. Saussure used “sign” for arbitrarynotations because “symbol” implies motivation, relationship toemotions and instincts. It was important for him that motiva-tion contradicted arbitrariness.

Both Peirce and Saussure wanted to understand the processin which signs acquire meanings. Both of them failed; work-

ings of the mind were not known at thetime. Peircian assumption that iconsthemselves resemble situations in theworld is too simplistic. Algorithms basedon this assumption led to combinatorialcomplexity. Similarly, arbitrarinessemphasized by Peirce and Saussure didnot lead to algorithms of meaning cre-

ation. Because arbitrary conventions are also expressed throughsigns, all signs get their meanings in relation to other signs.Arbitrary signs have no grounding in the real world. Meaningscannot be created by unmotivated choices on the interconnec-tions of arbitrary signs. These lead to combinatorial complexi-ty. In infinite systems, they lead to Gödelian contradictions.Similarly, mechanisms of meaning creation were not found byfounders of “symbolic AI,” when they used the motivationallyloaded word “symbol” for arbitrary mathematical notations.Mathematical notations, just because they are called symbols,do not hold a key to the mystery of cultural and psychologicalsymbols. Multiple meanings of the word “symbol” misguidedtheir intuition. This is an example of what Wittgenstein called“bewitchment by language.”

The NMF and dynamic logic emphasize that meanings arecreated in symbol processes connecting language with instinctand emotions, conscious with unconscious.

8. Conscious, Unconscious, and DifferentiationConsciousness is an internal “space” in the mind where anorganism makes plans and evaluates alternatives. This ability isneeded when the mind operations reach a certain level ofcomplexity, when it perceives alternatives and needs a “space”to make choices or resolve contradictions.

Primitive perception abilities (in primitive animals) are lim-ited to a few types of concept-objects (light-dark, warm-cold,edible-nonedible, dangerous-attractive… ), and are directly‘wired’ to proper actions. Consciousness is not needed; instinc-tual mechanisms are sufficient. Further development of thepsyche beyond immediate actions requires complex internalmodel-concepts. If one model contradicts another, instinctualmechanisms are insufficient to overcome the impasse. Con-sciousness has to evolve to resolve contradictions. Evolutionproceeded through differentiation of psychic functions intoconcepts, emotions, behavior, and into concept-models. Dif-ferentiation of consciousness began millions of years ago accel-erating tremendously in our recent past and today [24]–[25].

How do we experience differentiated consciousness? Mostof the mind’s operations are unconscious. Neural firings andconnections cannot be perceived consciously. In the founda-tions of the mind, there are material processes in the braininaccessible to consciousness. Jung suggested that consciousconcepts are based on genetically inherited structures, orarchetypes, which are inaccessible to consciousness [26], [27].Grossberg [6] suggested that only signals and models attaining aresonant state where signals match models can reach con-sciousness. It was further detailed by Taylor [28] when he

Try to teach a dog to understand the word “rational,”or any abstract concept, which meaning is based onseveral hierarchical levels; this is not possible.


related consciousness to the mind con-trolling the body. To summarize, themind mechanisms, described in NMF bydynamic logic and fuzzy models, are notaccessible to consciousness. Final results ofdynamic logic, resonant states character-ized by crisp models, are accessible toconsciousness. Increase in knowledge and improved cognitionresults in better, more diverse, more differentiated conscious-ness. The knowledge instinct drives NMF to improve knowl-edge by evolving vague, uncertain, less-conscious modelstoward crisp models that are better accessible to consciousness.This process of knowledge accumulation constitutes an essen-tial aspect of cultural evolution. Vague and uncertain modelsare less accessible to consciousness, whereas crisp and concretemodels are more conscious [29].

Our internal perceptions of consciousness is due to theEgo-model, which ‘perceives’ crisp conscious parts of othermodels similar to models of perception ‘perceive’ objects inthe world. The properties of consciousness, such as unity, con-tinuity, and identity motivate us to assume existence of theEgo-model with these properties. Since Freud, a certain com-plex of psychological functions was called Ego. Jung consid-ered Ego a part of the archetype of Self. Archetypes are psychicstructures (models) of a primordial origin, mostly unconscious,but determining the structure of our psyche [30]. Archetypesare similar to other models, e.g., receptive fields of the retinaare not consciously perceived, but determine the structure ofvisual perception. The Self archetype determines our phenom-enological subjective perception of ourselves. An importantphenomenological property of Self is the perception ofuniqueness and indivisibility (hence, the word individual).

We equate the mechanism of concepts with internal repre-sentations of objects, their relationships, situations, etc. Vagueand unconscious model-concepts are evolved into more crispand conscious ones. Another origin of conscious model-con-cepts is from language [17]. Consciousness and cultures evolveby creating diverse conscious models of the mind representingthe world. This was called by Carl Jung differentiation of psychiccontent [30].

9. Hierarchy and SynthesisDynamic logic activates models or concepts recognized inthe input signals. These concepts may generate behavior atthe same level. The activated models also initiate actions athigher levels. They send signals up the hierarchy, wheremore general concept-models are recognized and created.Each concept-model finds its mental meaning and purpose ata higher level. For example, consider a cognitive concept-model “chair.” It has a “behavioral” purpose of initiatingsitting behavior; this is the “bodily” purpose at the samehierarchical level. In addition, “chair” has a “purely men-tal” purpose at a higher level in the hierarchy, a purpose ofhelping to recognize a more general concept, say of a “con-cert hall,” which model contains rows of chairs.

Models at higher levels are more general than at the lowerones. At the very bottom of the hierarchy, if we consider visionsystem, models correspond to retinal ganglion cells; they detectsimple features in the visual field. At higher levels, at V1 andhigher up in the visual cortex, models detect more complexfeatures [6], [31]. At still higher cognitive levels, models corre-spond to objects, to relationships among objects, to situations,etc. [8]. Still higher up are even more general models of com-plex cultural notions and relationships, like family, love, friend-ship, and abstract concepts, like law, rationality, etc. Contentsof these models correspond to cultural wealth of knowledge,including writings of Confucius, Shakespeare, and Tolstoy. Atthe top of the hierarchy of the mind, according to Kantiananalysis [32], there are models of the meaning and purpose ofour existence, unifying our knowledge, and the correspondingbehavioral models aimed at achieving this meaning.

In pre-scientific literature about the mind, there was a pop-ular idea of homunculus, a little mind inside our mind thatperceived our perceptions and made them available to ourmind. This naive view is amazingly close to actual scientificexplanation. The fundamental difference is that the scientificexplanation does not need an infinite chain of homunculiinside homunculi. Instead, mind models higher in the hierar-chy are less conscious, less differentiated; they are more uncer-tain and fuzzy. At the top of the hierarchy there are the mostgeneral and important models of the meaning of our existence;they are mostly unconscious.

Models at lower levels correspond to objects directly per-ceived in the world. Perception mechanisms, to a significantextent, are determined by sensor organs evolved over billionsof years. Models at this level were produced by evolutionmore than by cultural constructions. These models are“grounded” in “real” objects existing in the surroundingworld. For example, “food” objects are perceived not only bythe human mind, but also by pre-human animals.

This is not true for concept-models at higher levels.Abstract and general models are cultural constructs. They can-not be perceived directly in the world (e.g., “rationality,” or“meaning and purpose of life”). They cannot emerge in themind as useful combinations of simpler concepts; here are somany combinations of simpler concepts that an individual lifeis too short to verify their usefulness. An individual mind isassured about usefulness of high-level concept-models due tolanguage, in that we can talk about them with other people(with a degree of mutual understanding). As discussed, lan-guage concepts are not automatically related to events in theworld. For example, every five-year-old knows about “goodguys” and “bad guys.” Yet, still at 40, or 90, nobody could

Saussure used “sign” for arbitrary notations because“symbol” implies motivation, relationship to emotionsand instincts.


claim that he or she can perfectly use these models to under-stand the surrounding world. Philosophers and theologianshave argued about the meaning of good and evil for thousandsof years, and these arguments are likely to continue forever.

Neural and mathematical mechanisms connecting the hier-archy of the mind and the knowledge instinct are just beingstudied [1] ,[2], [29]. Here we outline some basic principles ofthe knowledge instinct operation in the mind hierarchy. Eachmodel has a “purpose” of satisfying the knowledge instinct bycreating a more general and unifying representation frommodels recognized at lower levels. The hierarchy, therefore, isa knowledge instinct mechanism of the purposefulness. Pur-posefulness of our knowledge was so important throughout theevolution that a special mechanism of consciousness evolved tointerpret everything that happen to us as a part of the mean-ingful story, even if it was not supported by sensory data; Gaz-zaniga called this mechanism the interpreter [33]. Thismechanism was first described by Kant [32]. He discoveredthat purposefulness is a priori principle of the faculty of judg-ment (similarity or the knowledge instinct in NMF). ThusKantian judgment and Gazzaniga's interpreter are mathemati-cally described in NMF as the knowledge instinct.

Satisfaction of the knowledge instinct in a hierarchyinvolves different, even opposite, mechanisms from differentia-tion. Models at higher levels are more general and vague, lessconcrete, and less conscious. Still, they are more meaningful,purposeful, and more emotional. Whereas conceptual contentof high-level models is less conscious, their emotional contentis more conscious. Pure aesthetic feel of harmony between ourknowledge and surrounding world at lower levels is below thethreshold of conscious registration in our minds. We do notfeel much joy from understanding simple objects around us.But we do enjoy solving complex problems, which require alot of time and effort. This emotional feel of harmony fromimproving and creating high level concept-models is becausehigh level concepts unify many lower level concepts, andincrease the overall meaning and purpose of our diverseknowledge. Jung called this synthesis, and emphasized that it isessential for psychological well being.

Synthesis, the feel of overall meaning and purpose ofknowledge, is related to meaning and purpose of life, whichwe perceive at the highest levels of the mind hierarchy. Atthose high levels, conceptual and emotional are not quite dif-ferentiated. This inseparability, which we sometimes feel as ahigh emotional value of the highest concepts of meaning andpurpose of our existence, is important for evolution and sur-vival. It is necessary for concentrating will and resolving con-tradictions. If the hierarchy of knowledge does not support thisfeel, the entire hierarchy would crumble, which was possiblythe most important mechanism of destruction of old civiliza-

tions. The knowledge instinct demandssatisfaction at the lowest levels of under-standing concrete objects (differentiation)and also at the highest levels, understand-ing of the entire knowledge in its unity,

which we feel as the meaning and purpose of our existence.This is the other side of the knowledge instinct, a mechanismof synthesis [26].

10. Evolution of Consciousness and CulturesSynthesis and differentiation, the two aspects of the knowledgeinstinct, are at once symbiotic and antagonistic. Their interac-tion defines complex evolution of cultures. Language is animportant mechanism of both differentiation and synthesis, andits evolution is intertwined with evolution of cultures.

10.1 Neurodynamics of Differentiation and SynthesisIndividual experience is limited. Therefore, a finite number ofconcept-models is sufficient to satisfy the knowledge instinct. Itis well appreciated in many engineering applications that esti-mating a large number of models from limited data is difficultand unreliable; many different solutions are possible, one no bet-ter than the other. The importance of each model-concept inthe entire body of knowledge diminishes. Psychologically, anaverage emotional investment in each concept goes down withthe number of concepts increasing. A drive for differentiationand creating more knowledge subsides. Emotional investment ina concept is a measure of meaning and purpose of this conceptwithin the mind system, a measure of synthesis. Thus, drive fordifferentiation requires synthesis. More synthesis leads to fasterdifferentiation, whereas more differentiation decreases synthesis.

Another aspect of synthesis is related to language. Peopleare different in their ability to connect language and cognition.Some people are good at talking, without fully understandinghow their language concepts are related to real life. On anysubject, they can talk one way or another without much emo-tional investment. Synthesis of language and cognition involvessynthesis of emotional and conceptual contents of the psyche.

Synthesis of emotional and conceptual is related to hierar-chy. As discussed, higher level concepts are more general,vague, and less conscious. Their conceptual and emotionalcontents are less differentiated, and they are more emotionalthan low-level, mundane, everyday concepts. Therefore, syn-thesis connects language and cognition, concepts and emo-tions, conscious and unconscious. This is the opposite ofdifferentiation, in which we all have high-value concepts(related to family life, or to political causes, or to religion)thatare so important to us and so emotional that we cannot “coldlyanalyze,” cannot differentiate them. A “too high” level of syn-thesis invests concepts with “too much” emotional-value con-tents, so that differentiation is stifled.

To summarize, differentiation and synthesis are in complexrelationships, at once symbiotic and antagonistic. Synthesisleads to spiritual inspiration, to active creative behavior leadingto fast differentiation, to creation of knowledge, to science and

Consciousness is an internal “space” in the mind wherean organism makes plans and evaluates alternatives.


technology. At the same time, a “too high” level of synthesisstifles differentiation. Synthesis is related to the hierarchicalstructure of knowledge and values. At the same time, a highlevel of differentiation discounts psychological emotionalvalues of individual concepts, and destroys synthesis, which isnecessary for differentiation. This interaction between differen-tiation and synthesis leads to complicated evolution of lan-guages and cultures.

10.2 Mean Field Evolutionary ModelsHere we study evolutionary models similar to mean field theo-ries in physics, which describe neural mechanisms of differentia-tion, synthesis, and hierarchy using measures averaged overpopulation. We start with the simplest evolutionary equationscorresponding to the discussed interactions of differentiation andsynthesis. Differentiation and synthesis can be measured in psy-chological laboratories: differentiation by the number of oftenused words and phrases in language, and synthesis by strength ofemotions associated with various concepts [34]. Therefore,results of our analysis can be used in sociological cultural studiesto understand past, present, and future of cultures, emerging cul-tural phenomena, and to improve current and future models.

We characterize accumulated knowledge, or differentiationby a “mean field” averaged quantity, D, an average number ofconcept-models used in a population. When considered alone,separate from other mechanisms discussed above, the simplestdynamical equation accounts for the fact that differentiationinvolves developing new, more detailed models from the oldones, and therefore the speed of differentiation is proportionalto accumulated knowledge,

dD/d t = aD. (5)

Here a is a constant. Solution of this equation, describes expo-nential growth of knowledge,

D( t) = D0 exp(a t). (6)

R. Kurzweil uses this kind of equation to predict the comingsingularity of human evolution [35]. I do not think however,that this kind of simplified equation in principle can predictsingularities. Near singularities in other mechanisms have to betaken into account, namely, synthesis.

From time to time, growth in knowledge and conceptualdiversity in all societies was interrupted; cultures disintegratedor stagnated. This is true in all known cultures, e.g., westernculture disintegrated and stagnated during the Middle Ages.Whereas disintegration of Roman Empire was attributed tobarbarians or lead poisoning [36], here we would search forspiritual mechanisms related to the working of the minds. Wehave to account for the effect of synthesis.

According to the previous analysis, influence of synthe-sis on speed of differentiation is not linear. In a moderateamount, synthesis inspires creativity and stimulates differ-entiation. But, “too much” emotional value invested in

every concept stifles differentiation; it makes concepts “sta-ble” and difficult to modify or differentiate. We accountfor this by modifying (5) as follows: dD/d t = a D G (S),G (S) = (S − S0) exp(−(S − S0)/S1); function G first grows,then declines.

Now we need to account for evolution of synthesis. Withgrowth of differentiation, emotional value of every individualconcept diminishes, therefore the simplest evolutionary equa-tion for synthesis is dS/d t = −bD (b is a constant). Thishowever would only lead to destruction of synthesis. Fromprevious analysis, we know that synthesis is created in hierar-chies. Diverse, differentiated knowledge at a particular level ina hierarchy, acquires meaning and purpose at the next level. Asimplest measure of hierarchy, H , is the number of hierarchi-cal levels on average, in the minds of the population.Accounting for hierarchical synthesis, we come withdS/d t = −bD + dH (d is a constant).

Expanding knowledge in long time leads to expandinghierarchical levels. Growth of hierarchy involves differentia-tion of models at the highest level, which involve concepts ofthe meaning and purpose of life. These concepts in manysocieties involve theological and religious concepts of theHighest. Changes in these concepts involve changes of reli-gion, such as from Catholicism to Reformation; they involvenational upheavals and wars, and they do not proceedsmoothly. Currently, we do not have a theory adequate todescribe these changes; therefore, we proceed within a singlefixed value of the hierarchy of knowledge. Alternatively, wecan consider slowly expanding hierarchy, which might char-acterize average evolution over the long term, like westernsociety evolution over thousands of years, H( t) = H0 + e∗ t.Combining these equations, we obtain

dD/d t = a D G (S),

G (S) = (S − S0) exp(−(S − S0)/S1) (7)

dS/d t = −b D + d H (8)

H( t) = H0, o r H( t) = H0 + e∗ t. (9)

Let us now consider solutions to this system of equations.At moderate levels of synthesis we obtain oscillating solutions(or oscillating-growing solutions, if hierarchy expands). This isillustrated in Figure 3. This and following figures should beconsidered as preliminary qualitative indications of the impor-tant effects. How much differentiation (knowledge) is lost andreacquired on every oscillation? How long are oscillations(decades or centuries)? These questions should be addressed infuture studies better grounded in experimental data.

Another type of solution possible here involves a highlevel of synthesis, with stagnating differentiation. IfdH > bD, according to (8), synthesis grows; differentiationlevels off, whereas synthesis continues growing. This leads toa more and more stable society with high synthesis, highemotional values attached to every concept, while knowl-edge accumulation stops, Figure 4.


Cultural psychologists and historians might find examples ofstagnating internally stable societies. Candidates are AncientEgypt and contemporary Arab Muslim societies. Differencesbetween Figures 3 and 4 are related to our previous discussionof different emotionalities in English and Arabic. Presently,these are only suggestions for future studies. Levels of differen-tiation, synthesis, and hierarchy can be measured by scientificmeans. These data should be compared to the model. Thiswould lead to model improvement, to developing moredetailed models, including simulations of large societies ofinteracting agents involving the mind subsystems of cognitionand language [37]. This will help in understanding existingcultural differences in the world, and lead to less confrontationsand more harmonious coexistence.

10.3 Interacting Cultures Let us study interacting cultures with different levels of dif-ferentiation and synthesis. Both are characterized by NMF-minds and evolutionary equations (7, 8, 9). Culture k = 1 isof Figure 3 type (“dynamic” culture). Culture k = 2 is ofFigure 3 type (“traditional” culture). In addition, there is aslow exchange of differentiation and synthesis among thesetwo cultures (examples: the U.S. and Mexico, or in general,immigrants to the U.S. from more traditional societies; oracademic-media culture within the U.S. and “the rest” of thepopulation). Evolutionary equations modified for inflow andoutflow of differentiation and synthesis:

dDk/d t = ak Dk G (Sk) + X kDk (10)

dSk/d t = −bk Dk + dk Hk + ykSk (11)

Hk = H0k + e∗k t. (12)

Here index k denotes the opposite culture, for k = 1,k = 2, and vice versa. Figure 5 illustrates a solution ofthese equations.

In Figure 5, the evolution started with two inter-acting cultures, one traditional and another dynamic.Due to exchange of differentiation and synthesisamong cultures, traditional culture acquires differenti-ation, loses much of its synthesis, and becomes adynamic culture (by about t = 2.5). Let us emphasizethat we tried to find parameter values, leading to lessoscillations in differentiation and more stability. Wedid not find such solutions. Although parametersdetermining exchange of differentiation and synthesisare symmetrical in two directions among cultures, it isinteresting to note that the traditional culture does not“stabilize” the dynamic one. The effect is mainly one-directional; traditional culture acquires differentiatedknowledge and dynamics. It would be up to cultural

historians and social psychologists to judge if the beginning ofthis plot ( t < 2.5) represents contemporary influence ofAmerican culture on the traditional societies (let me empha-size, this is a scientific question; levels of differentiation andsynthesis can be measured in societies and modeled mathe-matically). And possibly, this figure explains why the influ-ence of differentiation-knowledge and not highly-emotionalstability-synthesis dominates cultural exchanges (unless “emo-tional-traditionalists” physically eliminate “knowledge-acquiring ones” during one of their periods of weakness).Wild swings of differentiation and synthesis subside only aftert < 5 when both cultures acquire a similar level of differenti-ated knowledge.

In long run ( t < 5), cultures stabilize each other andswings of differentiation, and synthesis subsides while knowl-edge accumulation continues. Note that in this example, hier-archies were maintained at different levels. Is thisrepresentative of Catholic and Protestant communities coex-isting with approximately equal levels of differentiation and

FIGURE 4 Highly stable, stagnating society with growing synthesis.High emotional values are attached to every concept, while knowledge accumulation stops; parameter values: D(t = 0) = 3,H0 = 10, S(t = 0) = 50, S0 = 1, S1 = 10, a = 10, b = 1, d = 10, e = 1.

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FIGURE 3 Moderate values of synthesis lead to oscillating and growing differ-entiation and synthesis (eqs. 7, 8, 9, with parameter values a = 10, b = 1,d = 10, S0 = 2, S1 = 10 H0 = 1, and initial values D(t = 0) = 10, S(t = 0) = 3).A unit of time here could be a century or longer. Periods of growth and knowl-edge accumulation are followed by collapse and destruction. In long timeknowledge is slowly accumulated, corresponding to slowly growing hierarchy,e = 0.1.

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synthesis, but different hierarchies? This is aquestion for social psychologists. We would liketo emphasize that co-existence of different cul-tures is beneficial in long run; both communitiesevolve with more stability. Possibly, stabilizationand expanding knowledge beyond t < 5 repre-sent the effect of multiculturalism, and explainthe vigor of contemporary American society.

11. Future Directions

11.1 Music and Synthesisof Differentiated Psyche High levels of differentiation, as we saw, maythreaten stability. By destroying synthesis, differ-entiation undermines the very basis for knowl-edge accumulation. This may lead to wildoscillations of differentiation and synthesis, alter-nating periods of flourishing and collapse. Herewe analyze an important mechanism of preserv-ing synthesis along with high level of differentia-tion, which will have to be accounted for infuture models.

Synthesis, a feel of the meaning and purpose, let us repeat,is a necessary condition of human existence. Synthesis isthreatened by differentiation of knowledge. It is difficult tomaintain synthesis along with high differentiation and a lot ofabstract knowledge, like in contemporary western societies;this leads to uncertainty about values and to crises-like events.In traditional societies, there is less knowledge, but it is closelyrelated to the immediate needs of life. Synthesis is easier tomaintain, but at the expense of cultural stagnation. Since timeimmemorial, art and religion connected conceptual knowledgewith emotions and values; these were cultural means maintain-ing synthesis along with differentiation. A particularly impor-tant role in this process belongs to music. The reason is thatmusic directly appeals to emotions [21], [38].

Music appeared from voice sounds, from singing. Prosodyor melody of voice sounds, rhythm, accent, tone and pitch aregoverned by neural mechanisms in the brain. Images of neuralactivity (obtained by magnetic resonance imaging) show thatthe human brain has two centers controlling melody of speech, anancient center located in the limbic system, and a recent one inthe cerebral cortex. The ancient center is connected to directuncontrollable emotions; the recent is connected to conceptsand consciously controlled emotions. This is known frommedical cases when patients with a damaged cortex lose abilityfor speaking and understanding complex phrases, but still com-prehend sharply emotional speech [39].

Prosody of speech in primates is governed from a singleancient emotional center in the limbic system. Conceptual andemotional systems in animals are less differentiated than inhumans. Sounds of animal cries engage the entire psyche,rather than concepts and emotions separately. An ape or birdseeing danger does not think about what to say to its fellows.

A cry of danger is inseparably fused with recognition of a dan-gerous situation, and with a command to oneself and to theentire flock: “Fly!” An evaluation (emotion of fear), under-standing (concept of danger), and behavior (cry and wingsweep)—are not differentiated. Conscious and unconscious arenot separated: Recognizing danger, crying, and flying away is afused concept-emotion-behavioral synthetic form of thought-action. Birds and apes can not control their larynx musclesvoluntarily [40], [41].

In humans, emotions-evaluations have separated from con-cepts-representations and from behavior. (For example, whensitting around the table and discussing snakes, we do not jumpon the table uncontrollably in fear, every time “snakes” arementioned). This differentiation of concepts and emotions isdriven by conceptual power of language. On the opposite,prosody or melody of speech is related to cognition and emo-tions through the knowledge instinct and through ancientemotional mechanisms. This connection of concepts withemotions, conscious models with unconscious archetypes, issynthesis. The human voice engages concepts and emotions.Melody of voice is perceived by ancient neural centersinvolved with archetypes, whereas conceptual contents of lan-guage involve conscious concepts. Human voice, therefore,involves both concepts and emotions; its melody is perceivedby both conscious and unconscious; it maintains synthesis andcreates wholeness in the psyche [22], [42].

Over thousands of years of cultural evolution, music per-fected this inborn ability. Musical sound engages the humanbeing as a whole—such is the nature of archetypes, ancient,vague, and undifferentiated emotions-concepts of the mind.Archetypes are non-differentiated, their emotional and con-ceptual contents, their high and low are fused and exist only

FIGURE 5 Effects of cultural exchange (k = 1, solid lines:D(t = 0) = 30, H0 = 12, S(t = 0) = 2, S0 = 1, S1 = 10, a = 2, b = 1, d = 10, e = 1,x = 0.5, y = 0.5; k = 2, dotted lines: D(t = 0) = 3, H0 = 10, S (t = 0) = 50,

S0 = 1, S1 = 10,a = 2, b = 1, d = 10, e = 1, x = 0.5, y = 0.5). Transfer of differentiated knowledge to less-differentiated culture dominates exchange duringt < 2 (dashed blue curve). In long run (t > 5), cultures stabilize each other, andswings of differentiation and synthesis subside while knowledge accumulation contin-ues. Note, in this example hierarchies were maintained at different levels (exchange ofhierarchical structure would lead to the two cultures becoming identical).

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as possibilities. By engaging archetypes, music gets to themost ancient unconscious depths as well as to the loftiestideas of the meaning of existence. This is why folk songs,popular songs, or opera airs might affect more strongly thanwords or music separately. Synthetic impact of a song, con-necting conscious and unconscious, explains the fact thatsometimes mediocre lyrics combined with second-rate musicimpact listeners. And, when music and poetry truly corre-spond with each other and reach high art, a powerful psy-chological effect occurs. This uncovers mechanisms of themysterious co-belonging of music and poetry. High forms ofart effect synthesis of the most important models touchingthe meaning of human existence; and popular songs, throughinteraction of words and sounds, connect usual words ofeveryday life with the depths of unconscious. This is why incontemporary culture, with its tremendous number of dif-ferentiated concepts and lack of meaning, such an importantrole is taken by popular songs. [9], [25], [23].

Whereas language evolved as a main mechanism of differ-entiation of concepts, music evolved as a main mechanism ofdifferentiation of emotions (conscious emotions in the cor-tex). This differentiation of emotions is necessary for unifyingdifferentiated consciousness. Synthesis of differentiated knowl-edge entails emotional interactions among concepts [22]. Thismechanism may remedy a disturbing aspect of oscillating solu-tions considered in the previous section: wild oscillations ofdifferentiation and synthesis. During every period of culturalslowdown, about 85% of knowledge collapsed. In previoussections we defined the knowledge instinct as maximization ofsimilarity and aesthetic emotions as changes in similarity.Future research will have to take the next step: define themechanism by which differentiated aesthetic emotions unifycontradictory aspects of knowledge. We will model neuralprocesses, in which diverse emotions created by music unifycontradictory concepts in their manifold relations to our cog-nition as a whole. We will have to understand processes inwhich the knowledge instinct differentiates itself, and synthe-sis of differentiated knowledge is achieved.

This hypothesis about the role of music in evolution ofthe mind, consciousness, and cultures proposes an answer to along standing mystery. David Huron summarized dozens ofproposed explanations for importance of music, from inspir-ing social cohesion and relaxing psychic tensions to sex andcourtship [43]; he emphasized that all these explanations areat best, proximate causes, and do not address the primarycause, which was searched for by many great philosopherssince Aristotle. Almost 2,500 years ago, Aristotle asked howmusic, being just sounds, affects states of our soul [44]. Near

the end of the 18th century, Kant came closeto understanding the role of beauty in mecha-nisms of the mind, but still could not see a rolefor music: “(as for) the expansion of the facul-ties which must concur in the judgment forcognition, music will have the lowest placeamong (the beautiful arts) … because it merely

plays with senses” [32]. Contemporary experts in evolution-ary psychology follow Kant (for example, Steven Pinkerwrites that music is “auditory cheesecake,” a byproduct ofnatural selection that just happened to “tickle the sensitivespots” [45]). Today, we are close to resolving this millennialmystery (see also [22], [42]).

11.2 Future StudiesStudies of neural mechanisms of interacting language and cog-nition already began [2], [17], [46], [47]. Future experimentalresearch will need to examine, in detail, the nature of hierar-chical interactions, including mechanisms of learning hierar-chy, to what extent the hierarchy is inborn vs. adaptivelylearned. Differentiated nature of the knowledge instinct shouldbe examined theoretically and experimentally. Unsolved prob-lems include: neural mechanisms of emerging hierarchy, inter-actions between cognitive hierarchy and language hierarchy[1], [17]; differentiated forms of the knowledge instinctaccounting for emotional interactions among concepts inprocesses of cognition, the infinite variety of aesthetic emo-tions perceived in music, their relationships to mechanisms ofsynthesis [25], [42]; neural mechanisms of interactions of dif-ferentiation and synthesis, and evolution of these mechanismsin the development of the mind during cultural evolution.

Mechanisms of conceptual differentiation at a single level ina hierarchy described in sections 4 and 5 correspond to psy-chological and neurophysiological experimental evidence.These include interaction between bottom-up and top-downsignals, and resonant matching between them as a foundationfor perception [8], [48]. Immediate experimental evidence fordynamic logic comes from the fact that imaginations (concept-models voluntarily recollected from memory with closed eyes)are vague and fuzzy relative to actual perceptions with openeyes. Experimental evidence is less certain for these mecha-nisms at each hierarchical level. Dynamic logic make a specificsuggestion that top-down (model) signals form a vague-fuzzyimage, gradually becoming more specific until they match theperceived object. This prediction might be amenable to directverification in psychological experiments. Detailed mathemati-cal models of perception and cognition can be compared topsychological measurements.

Further evidence for dynamic logic comes from N. Weinberger’s studies of perception of acoustic tones whilean electrode measured the response from the cellular receptivefields for acoustic frequencies in the brain [49]. Dynamic logicpredicts that during learning, the neural response will graduallybecome more specific, more “tuned.” This was actually exper-imentally observed when the frequency receptive field in the

Vague and uncertain models are less accessible toconsciousness, whereas crisp and concrete models are more conscious.


auditory cortex became more “tuned.” However, the auditorythalamus, an evolutionarily older brain region, did not exhibitdynamic-logic learning. This mechanism should be confirmedor disproved higher in the hierarchy.

The knowledge instinct is clearly a part of the mind opera-tions [3]–[5]. Can we prove its ubiquitous nature and connec-tion to emotional satisfaction or dissatisfaction? Can wemeasure aesthetic emotions during perception (when it is usu-ally subliminal)? Can we measure aesthetic emotions duringmore complex cognition (when it is more conscious)? Doesthe brain compute similarity measures, and if so, how is itdone? Does it relate to aesthetic emotions as predicted by theknowledge instinct theory? Does it operate in a similar way athigher levels in the hierarchy of the mind? Operations of thedifferentiated knowledge instinct, emotional influence of con-cepts on cognition of other concepts, is a virtually obvious fact;but neural, experimental and quantitative studies of this phe-nomenon are missing. For example, can we prove that emo-tionally sophisticated people can better tolerate cognitivedissonances (that is, conceptual contradictions) than people lesssophisticated emotionally (it would be important to controlother variables, say IQ)?

Daniel Levine studies emotional effects on learning [50]. Inhis experiments, normal subjects gradually accumulated cogni-tive knowledge, whereas emotionally impaired patients couldnot properly accumulate cognitive knowledge. Emotions in hisexperiments were not related to any bodily need, these wereaesthetic emotions. Are these aesthetic emotions limited tocortex, or are ancient emotional mechanisms also involved?

What are the time scales in Figures 3 through 6, decades ormillennia? Answering this question will require psychologicaland psycholinguistic experimental measurements of differentia-tion, synthesis, and hierarchical structures in various societies,and development of more precise models.

Acknowledgments I am thankful to D. Levine, R. Deming, R. Kozma, and B.Weijers for discussions, help and advice, and to AFOSR forsupporting part of this research under the Lab. Task05SN02COR, PM Dr. Jon Sjogren.

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leonid i. perlovsky ci culture - 02mci03-perlovsky.qxd.pdfaddress some of the limitations of the...

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