The following short discussions are explored in papers that can be copied from Publications section.
Is a physical theory of the mind possible? What kind of physics would this be? The mind and brain refer to the same physical system at different levels of description. This situation is not new to physics, for example thermodynamics and statistical physics are also related to each other in this manner. Although a physicist would prefer the most fundamental description level (elementary particles, strings...), intermediate levels are sometimes appropriate. Einstein once mentioned that he liked thermodynamics as physics defined at an intermediate phenomenological level. The world is amenable to understanding at various levels. Understanding searched by physicists is specific in certain ways: physics is a search for basic laws, a few universal “first principles” describing a wealth of observed phenomena.
Many physicists are uncomfortable with the phrase “physics of the mind,” and I will attempt to overcome this initial reaction. Some of the reasons for discomfort are obvious: the mind is perceived as deeply personal, something that no equation will ever be able to describe, no computer will ever be able to simulate. Another reason for skepticism is that the mind is both diverse and unpredictable, therefore how can it be reduced to few basic laws? Newton saw nothing wrong with developing physics of the mind, which he called spiritual substance. However Newton failed and since then few physicists have dared to approach the subject. Recently, new data, new intuitions, and new mathematical tools have emerged, and today we make a new attempt. I seek to identify a few basic principles of the mind operation, formulate these principles mathematically, use them to explain a wealth of known data, and make predictions that can be tested in the lab.
How the mind works has been the subject of discussions for millennia, from the Ancient Greek philosophers to mathematicians and cognitive scientists of today. Words like mind, thought, imagination, emotion, concept present a challenge: people use these words in many ways colloquially, but in cognitive science and in mathematics of intelligence they have not been uniquely defined and their meaning is a subject of active research and ongoing debates. Standardized definitions come at the end of the development of a theory (e.g., “force” was defined by Newton’s laws, following centuries of less precise usage). Specific neural mechanisms in the brain “implementing” various mind functions constitute the relationship between the mind and brain; this is a contemporary formulation of the millennia old mind-body problem.
A broad range of opinions exists about the mathematical methods suitable for the description of the mind. Founders of artificial intelligence, including Allan Newell and Marvin Minsky, thought that formal logic was sufficient and that no specific mathematical techniques would be needed to describe the mind. An opposite view was advocated by Brian Josephson and Roger Penrose, suggesting that the mind cannot be understood within the current knowledge of physics; new unknown yet physical phenomena will have to be accounted for explaining the working of the mind. Quantum computational processes were considered, which might take place in the brain (I wrote few papers on possible quantum algorithms operating in the brain). Some authors are developing a “classical physics” point of view in which there are few specific mathematical constructs, “the first principles” of the mind. Among researchers taking this view are Grossberg, who suggested that the first principles include a resonant matching between bottom-up signals and top-down representations, and emotional evaluation of conceptual contents; Zadeh develops theory of granularity; Meystel develops hierarchical multi-scale organization; Edelman suggests neuronal group selection. I suggested the knowledge instinct, aesthetic emotions, and dynamic logic among the first principles of the mind.
An inevitable conclusion from a mathematical analysis: humans and higher animals have a special instinct responsible for cognition. Let me emphasize, this is not an abstract mathematical theorem, but a conclusion from the basic knowledge of the mind operations as described in thousands of publications. Harry Harlow discovered that monkeys as well as humans have the drive for positive stimulation, regardless of satisfaction of drives such as hunger; David Berlyne discussed curiosity in this regard; Leon Festinger, introduced the notion of cognitive dissonance and described many experiments on the drive of humans to reduce dissonance.John Cacioppo studied psychological properties of the need for knowledge. Until recently, however, it was not mentioned among ‘basic instincts’ on a par with instincts for food and procreation. The reasons were that it was difficult to define, and that its fundamental nature was not obvious. The fundamental nature of this mechanism is related to the fact that our knowledge always has to be modified to fit the current situations. One rarely sees exactly the same object: illumination, angles, surrounding objects are usually different; therefore, adaptation-learning is required. A mathematical formulation of the mind mechanisms makes obvious the fundamental nature of our desire for knowledge. In fact virtually all learning and adaptive algorithms (tens of thousands of publications) maximize correspondence between the algorithm internal structure (knowledge in a wide sense) and objects of recognition. Concept-models that our mind uses for understanding the world are in a constant need of adaptation. Knowledge is not just a static state; it is in a constant process of adaptation and learning. Without adaptation of concept-models we will not be able to understand the ever-changing surrounding world. We will not be able to orient ourselves or satisfy any of the bodily needs. Therefore, we have an inborn need, a drive, an instinct to improve our knowledge. I call it the knowledge instinct. Mathematically it is described as a maximization of a similarity measure between concept-models and the world (as it is sensed by sensory organs; also the very sensing is usually adapted and shaped during perception).
Neural modeling fields (NMF) is a model-based neural architecture that mathematically implements many mechanisms of the mind discussed throughout this site. It is a multi-level, hetero-hierarchical system. The mind is not a strict hierarchy; there are multiple feedback connections among adjacent levels, hence the term hetero-hierarchy. At each level in NMF there are concept-models encapsulating the mind’s knowledge; they generate so-called top-down neural signals, interacting with input, bottom-up signals. These interactions are governed by the knowledge instinct, which drives concept-model learning, adaptation, and formation of new concept-models for better correspondence to the input signals. In NMF, cognition is inseparable from emotions.
At each hierarchical level there are interactions of bottom-up and top-down signals (fields of neural activation). At each level, output signals are concepts recognized in (or formed from) input signals. Input signals are associated with (or recognized, or grouped into) concepts according to the models and the knowledge instinct at this level. This general structure of NMF corresponds to our knowledge of neural structures in the brain. The knowledge instinct is described mathematically as maximization of a similarity measure. In the process of learning and understanding input signals, models are adapted for better representation of the input signals so that similarity between the models and signals increases. This increase in similarity satisfies the knowledge instinct and is felt as aesthetic emotions.
Combinatorial Complexity and Logic
Developing intelligent algorithms faced difficulty of combinatorial complexity (CC). CC refers to multiple combinations of various elements in a complex system; for example, recognition of a scene often requires concurrent recognition of its multiple elements that could be encountered in various combinations. CC is prohibitive because the number of combinations is very large: for example, consider 100 elements (not too large a number); the number of combinations of 100 elements is 100100, exceeding the number of all elementary particle events in life of the Universe; no computer would ever be able to compute that many combinations. The problem was first identified in pattern recognition and classification research in the 1960s and was named “the curse of dimensionality.” Self-learning algorithms and neural networks encountered CC of learning requirements. Rule-based systems
encountered CC of rules. Model-based systems (since the 1980’s) use models with unknown parameters. Fitting models to data faced CC of computations.
CC was related to formal logic, underlying various algorithms and neural networks (their computations, architecture, or training). It is related to Gödel theory: it is a manifestation of the inconsistency of logic in finite systems. Learning and adaptation on NMF overcomes CC by using dynamic logic.
Dynamic logic is a dynamic mechanism, which evolves vague and uncertain models into crisp and specific models. In this way DL avoids evaluating multiple combinations and overcomes CC. In NMF, DL maximizes similarity between the knowledge and the world (between models and input signals). DL combines the advantages of logical structure and connectionist dynamics . DL explains how classical logic emerges in the mind from illogical neural operations. Neuroimaging experiments (Bar et al. 2006) demonstrated the DL mechanism "from vague-to crisp" in visual perception.
Neural Modeling Fields and dynamic logic give mathematical mechanisms that describe higher cognitive functions, including concepts, emotions, instincts, understanding, cognition, imagination, intuition, symbols, consciousness, unconscious, language, aesthetic emotions of beautiful and sublime, and explain the role of music in cognition. These are not abstract mathematical postulates, but notions corresponding to everyday usage as well as to thousands of years of philosophical debates, and to the current data in psychology, cognitive science, aesthetics, and psychology. Mathematics helps clarifying contentious issues in philosophy and aesthetics. For example it explains what is the role of emotions in cognition, the role of the beautiful in cognition, why beauty is both objective and subjective, the role of emotions of sublimity in cognition and in evolution of the mind and culture, what is the role of emotions in languages, why languages are different in their emotional contents more than in their conceptual contents, , and how cultures co-evolved with languages.
Many debates in philosophy beginning 2500 years ago and continuing today in philosophy, cognitive science, and aesthetics, can be related to mechanisms of the mind considered on this web page. Aristotle did not assume that the mind works according to logic, instead he described workings of the mind similar to dynamic logic. The role of emotions of the beautiful and sublime remains misunderstood since Aristotle. Why do we need these emotions and what is their role in cognition? Kant came close to answering these questions, but he could not give a complete consistent explanation (which was clear to him, and much worried him). As we discuss in several papers, such an answer requires understanding of the knowledge instinct. Emotions of the beautiful and sublime emerge with necessity at the top of the mind hierarchy due to mechanisms of the knowledge instinct. At this "top" we feel these emotions, mostly unconsciously, as related to the purpose of human life and existence. Is there such a thing as purpose of human life? The question is not simple: a rational explanation does not seem possible, but we cannot live without meanings. Scientific answer can be found in corresponding papers in the Publication section. Scientific understanding of the beautiful is crucial for contemporary aesthetics and art (despite some artists claiming the opposite). The explanation given in several papers on this web page already starts influencing artists, art without meanings gradually yields again to meaningful art, but of course it is a slow process.
Scientific understanding of the sublime emotions is a crucial step to bridge science and religion. Many “anti-religion” scientists suspect religion since times of Galilee and Bruno; others are inspired by evil perpetrated in names of religions. It is an honorable position, but it is no better than to be against law because sometimes law is misapplied resulting in evil. Still, statistics around the globe established that societies with law and order (including police to maintain them) enjoy much less casualties than societies without effective mechanisms maintaining order (Pinker 2002).
Many religious (and not so religious) people suspect science for the reason that philosophers call “reductionism”. This means that if the highest principles making life meaningful could be scientifically explained somehow, eventually, the explanation will be reduced to biology, then to chemistry, to physics, and it would turn out that a human is no different than a piece of rock at a curbside.
This view has led some scientist to take a dualist position: consciousness and mental phenomena are of difference substance than material phenomena. But this conclusion is unsatisfactory from neither scientific nor religious point of view for the following reasons. The essence of science is to explain mental phenomena as emerging from material processes. The highest religious and philosophical thoughts rejected dualism for four millennia on spiritual grounds: this is the essence of the four largest religions (Judaism, Christianity, Buddhism, and Islam). Monotheistic religions are accepted by majority of people because surviving in the difficult world and achieving the highest human potential requires concentration of all human spiritual efforts, and dualism splits human soul.
Choosing between two alternatives: living without belief in science, or without belief in meaning and purpose of one’s life, many choose the former. Therefore, reductive science is not universally accepted over religion. But this choice is wrong. There is a scientific explanation for faith and religion, for their role in cognition and cultural evolution; there is a scientific non-reductive explanation, which is nor reducible to chemistry or physics (see Publication section).
“Symbol is the most misused word in our culture” wrote T. Deacon. We use this word in trivial cases referring to traffic signs, and in the most profound cases to cultural and religious symbols. To understand symbols we have to separate mechanisms of language and cognition. Why is the word ‘symbol’ used in such opposite ways: to denote trivial objects, like traffic signs or mathematical notations, and also to denote objects affecting entire cultures over millennia, like Magen David, Cross, or Crescent?
Let us compare in this regard opinions of two founders of contemporary semiotics, Charles Peirce and Ferdinand De Saussure. Peirce classified signs into symbols, indexes, and icons. Icons have meanings due to resemblance to the signified (objects, situations, etc.), indexes have meanings by direct connection to the signified, and symbols have meaning due to arbitrary conventional agreements. Saussure used different terminology, he emphasized that the sign receives meaning due to arbitrary conventions, whereas symbol implies motivation.
Both Peirce and Saussure wanted to understand the process in which signs acquire meanings. Both of them failed: workings of the mind were not known at the time. Consider Peircian icons; they resemble objects or situations because of specific mechanisms of perception and recognition in our mind. These mechanisms should be analyzed and understood as an essential part of meaning creation. Peircian assumption that icons in themselves resemble situations in the world is too simplistic. Algorithms based on this assumption led to irresolvable difficulties related to combinatorial complexity. Similarly, arbitrariness emphasized by Peirce and Saussure did not lead to algorithms of meaning creation. Arbitrary signs have no grounding in real world. Meanings cannot be created by unmotivated choices on the interconnections of arbitrary signs, this type of choices lead to combinatorial complexity. In infinite systems, they lead to Gödelian contradictions. Similarly, mechanisms of meaning creation were not found by founders of “symbolic artificial intelligence,” when they used the motivationally loaded 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 psychological symbols. Multiple meanings of the word “symbol” misguided their intuition. This is an example of what Wittgenstein called “bewitchment by language.”
The NMF theory emphasizes that meaning creation consists in bringing unconscious into consciousness in the process of model adaptation. This process is “motivated” by the instinct for knowledge. The motivated meaning creation, connecting conscious and unconscious, is consistent with Jungian explanations of the nature of symbols. This motivates to use the word symbol for the processes of meaning creation, and to use the word sign for conventional or nonadaptive entities. This corresponds to Pribram’s interpretation of signs, as non-adaptive neural signals with fixed meanings.
Meanings are created by symbol-processes in the mind. Language plays special role in these processes. Language accumulates cultural knowledge of the world. Through communication among people, language provides grounding for abstract model-concepts at higher levels in the mind hierarchy. The mechanism of this relationship between language and cognition is joint language-cognitive models. These joint models are organized in parallel hierarchies of language models (words, texts) and cognitive models (world representations in the mind). Near the bottom of these hierarchies words refer to objects. Higher up, complex texts refer to complex situations. An amazing result of the described mechanism is that words within texts refer to objects within situations, and this reference at higher levels corresponds to the words-objects relationships at lower levels. Because of this multi-level hierarchical structure, maintaining meaningful relationships throughout the hierarchy, language is a coherent structure and not a set of arbitrary notations for arbitrary relationships. This meaning-maintaining hierarchy makes possible “the infinite use of finite means.” We do not know to which extent the hierarchies are inborn or created by mechanisms, which construct higher levels from lower ones. Possibly higher levels are predicted from lower ones.
A symbol-process involves conscious and unconscious, concepts and emotions, inborn models-archetypes and models learned from culture, language, and cognition. Symbols are psychic processes connecting conscious and unconscious; they might take milliseconds of millennia. These processes are the essence of interaction between language and cognition. Symbol processes continue up and up the hierarchy of models and mind toward the most general models. Due to language, they continue in culture through many generations. In semiotics this process is called semiosis, a continuous process of creating and interpreting the world outside (and inside our mind). Symbols are processes creating meanings.
Traditional cognitive theories use representational schemes in the mind that are inherently nonperceptual and rely on amodal symbols based in logic, such as feature lists, frames, semantic nets, etc. These symbols are amodal because their abstract structures do not correspond to the perceptual states that produced them (similar to words of language vs. objects in the world). Barsalou (1999) reviewed the strengths and weaknesses of amodal symbol systems, and noted their inadequacies including insufficient empirical evidence, computational difficulties, and lack of grounding (amodal symbols have no mechanisms connecting them to perceptions); he proposed the Perceptual Symbol System (PSS), grounded in perception and sensory-motor representations, as the basis of cognition. PSS mechanism of simulators relate symbols to perception and cognition, and implement combinatoriality, recursivity, productivity, etc. PSS computational foundations are based on dynamic logic (DL). As discussed, DL explains how logic and amodal symbols emerge in the mind from illogical PSS and neural operations.
Integration of language and cognition is attained by integrating cognitive and language models-representations in the mind. Data streams constantly come into the mind from all sensory perceptions. Every part of this data stream is constantly evaluated and associated with models. At the beginning, the models are fuzzy; cognitive models vaguely correspond to uncertain undifferentiated sensory perceptions. Language models vaguely correspond to sounds. This is approximately a state of the mind of a newborn baby. First, models of simple perceptions differentiate; objects are distinguished in visual perception. Language sounds are differentiated from other sounds. Until about one year of age, perception models corresponding to simple objects become crisper at a faster rate than language models.
Gradually, models are adapted, their correspondence to specific signals improve, selectivity to language signals and non-language sounds is enhanced. Language models are associated with words (sentences, etc.), and cognitive models are associated with objects and situations of perception and cognition. Between the first and second year of life the speed of adaptation of language models tremendously accelerates and overtakes learning of cognitive models. By the age of 5 or 7, a child knows tremendous number of language models (words, rules of grammar), which attained differentiated, crisp status. But it will take the rest of his life to associate them with real life situations and acquire highly differentiated crisp cognitive models.
Association between language and cognitive models occurs before any of the models attain a high degree of specificity characteristic of the grown-up conscious concepts. Language and cognition are integrated at a pre-conscious level. Certain language models evolve faster than their corresponding cognitive models and vice versa. Correspondingly, uncertainty and fuzziness of the two aspects of integrated models may significantly differ. Still, existence of crisp language models helps to identify relevant objects and situations in the world, and therefore, speeds up learning and adaptation of the corresponding cognitive models and v.v. Language and cognition abilities enhance each other.
Language and cognitive models form interacting hierarchies. Only at the bottom of the cognitive hierarchy model learning is directly grounded in sensory perception. Learning abstract cognitive models is only grounded in language. Abstract models, say, situations in the world corresponding to word “rationality” cannot be directly observed in the surrounding world. We can only learn abstract models by observing other people understanding us. This learning is not perfect. While meanings of words seemed to us crisp and clear, using these meanings in real life (cognition) is difficult and unclear. Say, “good guy” is understood by every three-year old, still who uses this notion in real life without errors! Notions of good and evil are discussed by philosophers for millennia. The higher up in the hierarchy of cognition, the vaguer are the concept-models.
Language is considered first of all a mechanism for communicating conceptual information. Of all animals, humans alone possess languages and high level conceptual thinking. Nevertheless, everyday conversations most of the time contain little novel informative conceptual contents. People talk to establish emotional contacts. From New Guinea villages to Wall Street Board Rooms, people listen to how the other guy sounds before they decide to deal or not with a particular person. When somebody tells you: “Let’s make a deal, I am an honest guy; you can rely on my word,” it is not likely that you will trust him, just based on the conceptual content of this talk. Emotions in the voice sound are no less important. How language affects emotions?
Animals’ vocal tract is controlled from the brain limbic system, which is also a seat of emotions. When monkey sees a leopard its understanding of danger, emotion of fear, action of jumping on a tree, and voice-call “leopard” (in monkey’s language) are undifferentiated and uncontrollable: the monkey’s psychic state is undifferentiated and uncontrollable concept-emotion-behavior-call. A monkey cannot say “leopard” if it is not scared by a leopard. Because of their brain’s neural “wiring,” animals do not voluntarily control their voice tract, cannot have our kind of language, and cannot think separately from feelings.
Human brain is different: our vocal tract is controlled from two emotional centers, one in limbic system, which we share with animals; another in cortex, which is partially under conscious voluntary control. This ability evolved jointly with language over millions of years. Language is the main mechanism of differentiation of concepts. Language also differentiates concepts from emotions. Language enables us to maintain deliberate conversations. We can disagree, still maintain a dialog, without getting at each other’s throat. Notwithstanding this progress, language is perceived emotionally as well as conceptually.
Emotionality of language is in it sound (rhythm, accent, prosody or melody of speech). These emotional “colorings” of speech connect language to our instinctual side directly, in addition to cognitive content (and to some extent independently from it). Language, therefore, is not a set of arbitrary signs denoting conceptual contents; language contains emotional motivations linking conceptual and emotional sides of our psyche. While language and cognition evolves, how stable is this link over time? In primates this link is essentially hardwired; relations between conceptual and emotional meanings of primate “words” likely remain unchanged for hundreds of thousands or even millions of years. This is not so in human languages. Languages evolve. Their conceptual and emotional contents change over time.
If sounds of a language change “too fast,” the link between emotion-sounds and conceptual contents might be severed. One mechanism maintaining this connection involves grammatical inflections. Modern English is weekly inflected language; examples of our inflections are “-ed” affix for the past tense and “-s” affix for plurals. Mid-English and Old English were much more inflected. When inflections disappeared, sounds of English started changing from generation to generation. Russian is more inflected than English. Arabic is even much more inflected, connections between sounds and meanings are stronger, which have two different, even opposing, consequences, considered below.
Creativity is an ability to improve and develop new model-concepts. In a small degree it is present in everyday perception and cognition. Usually the words “creativity,” “creative,” or “discovery” are applied to improving or creating new model-concepts at higher cognitive levels, concepts that are important for the entire society or culture. A crisp and specific model could only match a specific content; therefore it cannot lead to creation of new contents. Creativity and discovery, involve vague, fuzzy models, which are made more crisp and clear. It occurs, therefore, at the border between consciousness and unconscious. A similar nature of creative process, involving consciousness and unconscious, was discussed by Jung. Creativity usually involves intuition: fuzzy undifferentiated feelings-concepts.
Creativity is driven by the knowledge instinct. Two main mechanisms of creativity, the components of the knowledge instinct, are differentiation and synthesis. Differentiation is a process of creating new, more specific and more detailed concept-models from simpler, less differentiated and less conscious models. Many learning algorithms use some mathematical techniques of differentiation. Language is the most important mechanism of differentiation; this research is in its infancy and a subject of future research.
Synthesis is a process of connecting detailed crisp concept-models to the unconscious, instincts, and emotions. The need for synthesis comes from the fact that most of our concept-models are acquired from language. The entire conceptual content of the culture is transmitted from generation to generation through language; cognitive concept-models cannot be transmitted directly from brain to brain. Therefore, concepts acquired from language have to be used by individual minds to create cognitive concepts. The mechanism of integrating cognition and language, discussed in section 6, explains that language concepts could be detailed and conscious; but not necessarily connected to equally detailed cognitive concepts, to emotions, and to the knowledge instinct. Connecting language and cognition involves differentiating cognitive models, developing cognitive models, which differentiation and conscious approaches that of language models. Every child acquires language between one and seven, but it takes the rest of life to connect abstract language models to cognitive concept-models, to emotions, instincts, and to the life’s needs. This is the process of synthesis; it integrates language and cognition, concepts and emotions, conscious and unconscious, instinctual and learned. Current research directions discussed in section 6 are just touching on these mechanisms of synthesis. It is largely an area for future research.
Another aspect of synthesis, essential for creativity, is developing a unified whole within psyche, a feel and intuition of purpose and meaning of existence. It is necessary for concentrating will, for survival, for achieving individual goals, and in particular for satisfying the knowledge instinct by differentiating knowledge. Concept-models of purpose and meaning are near the top of the mind hierarchy; they are mostly unconscious and related to feelings of beautiful and sublime. A condition of synthesis is correspondence among a large number of concept-models. A knowledge instinct is not a single measure of correspondence between all the concept-models and all the experiences-data about the world. This would be a simplification. Certain concept-models have high value for psyche (e.g., family, success, certain political causes) and they affect recognition and understanding of other concepts. This is a mechanism of differentiation of the knowledge instinct. Satisfaction of the knowledge instinct therefore is not measured by a single aesthetic emotion, but by a large number of aesthetic emotions. The entire wealth of our knowledge should be brought into correspondence with itself; this requires a manifold of aesthetic emotions. Differentiation of emotions is performed by music; this is a new research direction.
There is an opposition between differentiation and synthesis in individual minds as well as in the collective psyche. This opposition leads to complex evolution of cultures. Differentiated concepts acquire meaning in connections with instinctual and unconscious, in synthesis. In evolution of the mind, differentiation is the essence of the development of the mind and consciousness, but it may bring about a split between conscious and unconscious, between emotional and conceptual, between language and cognition. Differentiated and refined models existing in language may loose connection with cognitive models, with people’s instinctual needs. If the split affects collective psyche, it leads to a loss of the creative potential of a community or nation. This was the mechanism of death of great ancient civilizations. The development of culture, the very interest of life requires combining differentiation and synthesis. Evolution of the mind and cultures is determined by this complex non-linear interaction: One factor prevails, then another. This is an area for future research.
Human vocal tract is controlled from two emotional centers, one in limbic system, which we share with animals; another in cortex, which is partially under conscious voluntary control. This ability for language leads to ability for deliberate conversations. We can disagree, still maintain a dialog, without killing each other. But, in this ability people are different, and languages are more different than people. In some languages it is easier to maintain “civil” dialog, than in other languages. If it is difficult to imagine for a native English reader, I would suggest: observe people speaking other languages: Arabic, Italian, or Russian. “Civility,” or “calmness” of conversations depends on cultural customs, but also on how much emotionality is differentiated from conceptual content, how close are concepts and emotions. Let me emphasize, it is not that Americans are less emotional than Russians, no; the difference is in connections between emotions and concepts in languages; differentiation of conceptual and emotional contents are different in different cultures.
All languages evolved toward reduced emotionality and increased conceptual contents. English is among the least inflected languages. Therefore conceptual contents evolve faster in English, it has more words than any other language. Science originated in English language. But this reduced emotionality implies the price. English language does not guide its speakers toward emotional values of its conceptual contents. English speakers are “freer” to select values of moral and spiritual concepts. Therefore, English speakers are prone to internal crises; many people need psychological and psychiatric help.
Arabic language, by its very sound, guides speakers to very strong emotional values. It is difficult to create new concepts in Arabic language. Airplanes are not built in Arabic. But Arabic people have a strong feel of their identity. They are ready to sacrifice life for their beliefs without much indoctrination.
This section is devoted to the problem that was studied in hundreds of books and thousands of papers from Plato and Aristotle to our time. How does music affect people? What are the mind mechanisms of music perception? How did ability for music perception evolved in the human mind? The founder of contemporary aesthetics, Kant in the 18th c. wrote: “(as for) the expansion of the faculties which must concur in the judgment for cognition, music will have the lowest place among (the beautiful arts)… because it merely plays with senses.” Contemporary experts in evolutionary psychology follow Kant (for example, S. Pinker, a Harvard professor, author of hundreds of papers and most popular books on psychology, mind, and their evolution, writes that music is “auditory cheesecake,” a byproduct of natural selection that just happened to “tickle the sensitive spots.” Prof. David Huron (Ohio University, Columbus) lists on his website dozens of attempts to explain the importance of music from unifying people for collective work to sex (attracting mates). But, explains Prof. Huron, all these are proximate causes. They do not answer the fundamental question posed already by Aristotle: “How come that music, being just sounds, affects our states of soul?” Why not other means are used in place of music, say language. The current section offers a different conception of the hierarchy of music among arts in terms of its principal importance for human existence, for evolution of our consciousness and culture. For the first time this section explains neural mechanisms of musical influence on human psyche as a whole, from unconscious archetypes to the highest concepts of the meaning of human existence. This section shows the reason why music participated in human evolution, and how music gave an advantage to our psyche in struggle for existence. Bases for our conclusions are closely related to theory of the mind described on this site, which on the basis of mathematics unified data from psychology, neurobiology, cognitive science, and evolution theory.
Synthesis in voice melody
Music appeared from voice sounds, from singing. Intonation, prosody, or melody of voice sounds, rhythm, accent, tone pitch are governed by neural mechanisms in the brain. Images of neural activity show that the human brain has two centers controlling melody of speech, ancient center located in the limbic system and recent one in the cerebral cortex. The ancient center is connected to direct uncontrollable emotions; the recent is connected to concepts and consciously controlled emotions.
Prosody of speech in primates is governed from a single ancient emotional center in the limbic system. In part, because of this, conceptual and emotional systems (understanding and evaluation) in animals are less differentiated than in humans. Sounds of animal cries engage the entire psyche, rather than concepts and emotions separately. An ape or bird seeing danger does not think about what to say to its fellows. A cry of danger is inseparably fused with recognition of a dangerous situation, and with a command to oneself and to the entire flock: “Fly!” An evaluation (emotion of fear), situation (concept of danger), and behavior (cry and wing sweep) – are not differentiated. Conscious and unconscious are not separated: Recognizing danger, crying, and flying away is a unified situational-behavioral fuzzy form of thought-action.
Emotions-evaluations in humans have separated from concepts-representations and from behavior (For example, when sitting around the table and discussing tigers, we do not jump on the table uncontrollably in fear, every time “tigers” are mentioned). Similarly, a professional musician learns to switch her attention from an ancient unconscious-emotional system to the recent conscious-emotional system. This enables a more accurate control of voice or bow, without experiencing at the same time uncontrollable strong emotions. (A professional singer on a stage may wipe sweat from his forehead not because he experiences strong emotions, but because he uses tremendous effort for exact control of the voice tract).
Prosody or melody of speech is related to thinking and emotions. To trace this connection, let us revisit the main mechanisms of intellect discussed previously. We have an inborn instinct to understand the world around us, the instinct for knowledge. It drives us to improve correspondence of model-concepts and signals perceived by sensory organs. Satisfaction of this instinct, when the surroundings correspond to our expectations, we perceive as an aesthetic emotion of harmony, and dissatisfaction as disharmony. Along with the everyday model-concepts, we are also endowed with ideas-models of the meaning and purpose of our existence; they are fuzzy, less conscious. When these most important models become crisper, more conscious, we perceive this highest harmony as beauty. Beauty reminds us of the purpose in our life (sometimes as if purpose existing in the world). The purpose in one’s life is realized through purposeful behavior.
The model-concepts of the meaning and purpose as well as models of behavior realizing this meaning are vague, fuzzy, inconcrete. Let’s dwell for a moment on this principled point. These models are vague and cannot be completely conscious because there is an irresolvable contradiction in the very foundation of human life. We know about our finiteness in the material world. Yet we fill limitless eternity of our spiritual existence. This feeling is sometimes fleeting (since life in the material world does not directly support this feeling) and it might be completely unconscious. But even if unconscious, the models of meaning perfectly perform their main function: Creation of synthesis as a condition for inspiring life and creativity. This is why these models are the highest, the most important ones. A feel-perception of meaning creates synthesis in the soul. This leads to creativity, which highest manifestations begin to analyze (differentiate) this feeling of meaning and models it is based upon. And when models of the meaning (and related models of behavior) become crispier, closer to consciousness, we feel the presence of beauty or spiritually-sublime. Models of meaning and of corresponding behavior are important not because of rules of morals; it is the other way around, morals directing us to meaning evolved because meaning is required for concentration of will and for survival.
Our perception and cognition is related to speech (language) through aesthetic emotions. This process of connecting concepts with emotions, conscious models with unconscious archetypes, we called synthesis. The human voice engages concepts and emotions. Melody of voice is perceived by ancient neural centers involved with archetypes, whereas conceptual contents of language involve conscious concepts. Human voice, therefore, involves both concepts and emotions; its melody is perceived by both conscious and unconscious; it maintains synthesis and creates wholeness in psyche.
Synthesis in differentiation of emotions
The correspondence among concepts and life is evaluated by aesthetic emotions – therefore a multiplicity of emotions is required, corresponding to multiplicity of concepts and their relations. The closer consciousness is to our times, the more there are differentiated concepts, the larger number of various emotions one needs to support synthesis.
In every type of art, direct emotional effects are important. Music, however, by differentiating sounds involves primordial emotional neural centers and possesses incomparably stronger means for differentiation of emotions than other arts. For example, mechanisms of visual perception are located in cortex; they use concept-models, which literally model surrounding world. Therefore, visual perceptions affect emotions only indirectly, through concepts. Direct impact of visual signals on emotions, circumventing visual cortex plays a minor role in emotionality of visual perceptions. Even more so literary prose perception is based on conceptual mechanisms. Texts affect emotions not directly, but only through its conceptual content. Therefore, literature (prose), visual arts (paintings, sculpture, graphics, architecture) are perceived and cognized conceptually; mainly through concepts they can influence emotions. Poetry, like songs, affects conceptual and emotional mechanisms directly (concepts through words, and emotions through sounds). Synthetic art forms (theater, film, TV) act similarly. Essentially, of all art forms, only music affects emotions directly.
By constructing new shades of emotions and by talking to archetypes of psyche (that is to primordial, vague, undifferentiated emotions-concepts, which contain the high and the low only as potentialities), music differentiates the aesthetic need, that is the instinct for knowledge. Music does it to a much larger extent than any other art. The multitude of differentiated emotions created by music we use to evaluate every concept in its multifaceted relationships to our knowledge as a whole.
Sounds can reach to the most ancient unconscious depths of human psyche as well as to most exalted ides of human existence. Music engages the human being as a whole, - such is the nature of archetypes, ancient, fuzzy, undifferentiated emotions-concepts of the mind. “Music moves foundations directly into hearts of listeners” [Nietzsche]. Isn’t this the reason why folk songs, popular songs, or opera airs might affect stronger than words or music separately? Such is synthetic impact of a song, connecting conscious and unconscious.
Coming to a completion of this part of the discussion, let us note the duality of the music nature. By differentiating emotions, music unifies contrarian concepts in their multifaceted relationships to our knowledge in its wholeness. By turning to undifferentiated unconscious structures of psyche, archetypes, music connects conscious and unconscious, conceptual and emotional and creates synthesis.
In language the world strives to split into pieces, and music makes it whole in manifold of emotions. This is why “Music is so deeply understood by our inmost being” [Schopenhauer]. Music differentiates emotions, invoking divisions, yearnings, dissatisfactions. And at the same time it is a mechanism of synthesis, creating harmony and wholeness in the human soul.
See papers online below: Perlovsky, L.I. (2005). Music – The First Principle. Role of music in evolution of consciousness. Also the web page of the journal Musical Theater, St. Petersburg,http://www.ceo.spb.ru/libretto/kon_lan/ogl.shtml
Teleology explains the Universe in terms of purposes. In many religious teachings, it is a basic argument for the existence of God: If there is purpose, an ultimate Designer must exist. Therefore, teleology is a hot point of debates between creationists and evolutionists: Is there a purpose in the world? Evolutionists assume that the only explanation is causal. Newton laws gave a perfect causal explanation for the motion of planets: A planet moves from moment to moment under the influence of a gravitational force. Similarly, today science explains motions of all particles and fields according to causal laws, and there are exact mathematical expressions for fields, forces and their motions. Causality explains what happens in the next moment as a result of forces acting in the previous moment. Scientists accept this causal explanation and oppose to teleological explanations in terms of purposes. The very basis of science, it seems, is on the side of causality, and religion is on the side of teleology.
However, at the level of the first physical principles this is wrong. The contradiction between causality and teleology does not exist at the very basic level of fundamental physics. The laws of physics, from classical Newtonian laws to quantum superstrings, can be formulated equally as causal or as teleological. An example of teleological principle in physics is energy minimization, particles move so that energy is minimized. As if particles in each moment know their purpose: to minimize the energy. The most general physical laws are formulated as minimization of action. Action is a more general physical entity than energy; it is an intuitive name for a mathematical expression called Lagrangian. Causal dynamics, motions of particles, quantum strings, and superstrings are determined by minimizing Lagrangian-action. A particle under force moves from point to point as if it knows its final purpose, to minimize Lagrangian-action. Causal dynamics and teleology are two sides of the same coin.
The knowledge instinct is similar to these most general physical laws: evolution of the mind is guided by maximization of knowledge. A mathematical structure of the knowledge instinct is similar to Lagrangian, and it plays a similar role; it bridges causal dynamic logic of cognition and teleological principle of maximum knowledge. Similarly to fundamental physics, dynamics and teleology are equivalent: Dynamic logic follows from maximization of knowledge and vice versa. Ideas, concept-models change under the ‘force’ of dynamic logic, as if they know the purpose: Maximum knowledge. One does not have to choose between scientific explanation and teleological purpose: Causal dynamics and teleology are equivalent.
From the work of the pioneering 18th century mathematicians Jakob Bernoulli and Thomas Bayes through the late 20th century, the dominant notion in the psychology of human decision making was based on rational optimization. This was similar to the knowledge instinct discussed above. But all that changed with the work, starting in the late 1960s, of Daniel Kahneman, winner of the 2003 Nobel Prize in economics, and Amos Tversky, who would have shared that prize had he been alive (Tversky and Kahneman 1974, 1981).
Tversky and Kahneman found that in many choices relating to gain and loss estimation, preferences run counter to rational optimization and lack self-consistency over different linguistic framings of the choice. For example, subjects asked to consider two programs to combat an Asian disease expected to kill 600 people tend to prefer the certain saving of 200 people to a 1/3 probability of saving all 600 with 2/3 probability of saving none. However, subjects also tend to prefer a 1/3 probability of nobody dying with a 2/3 probability of 600 dying to the certainty of 400 dying. The choices are identical in actual effect, but are perceived differently because of differences in frame of reference (comparing hypothetical states in one case with the state of all being alive, in the other case with the state of all dying). Tversky and Kahneman explain their data by noting that “choices involving gains are often risk averse while choices involving losses are often risk taking” (Tversky and Kahneman, 1981, 453). Tversky and Kahneman were led to descriptions of a large repertoire of simplifying heuristics that decision makers characteristically employ.
Most psychologists believe that heuristics have evolutionary value despite sometimes leading to errors and information losses. Heuristic simplification is particularly useful when a decision must be made rapidly on incomplete information, or when the stakes of the decision are not high enough to justify the effort of thorough deliberation. An example is buying a box of cereal in a supermarket (Levine 1997). The use of heuristics has been explained as cognitive effort minimization, EM (Montgomery and Svenson 1976).
The origin of the controversy between the KI and EM can be traced to the first pages of the Bible, to the story of Adam and Eve. In the 12th century Moses Maimonides, in his “Guide for the Perplexed” (Maimonides 1190/1956) analyzed the relationship between KI and heuristics. He was asked by his student: “Why did God, on one hand, give Adam the mind and free will, while on the other, forbid him to eat of the tree of knowledge? Did God not want Adam to use his mind?” Maimonides answered that God gave Adam the mind to think for himself what is good and what is bad (we associate this ability with the KI). But Adam succumbed to temptation and ate from the tree of knowledge. Adam thereby took a "shortcut” and acquired ready-made heuristics, that is, rule-of-thumb knowledge to guide him so his choices did not require hard thinking. In conclusion, Maimonides explained that Adam's story described our predicament. Whereas God’s ultimate commandment is to use the KI, it is difficult and we are not completely capable of doing it, especially when thinking about the highest values. Adam’s story described the workings of our mind: struggle between the KI and EM. EM provides the surety of millennial cultural support, but may not suit your individual circumstances. EM does not cause direction in evolution; it is a random force, averaged out over millennia. The KI may lead to doubts and uncertainties, but if successfully used, leads to the satisfaction of being more conscious about your choices. The KI is the mechanism of cultural evolution of humanity, equating dynamics with purposeful evolution.
Maimonides’ interpretation of the Biblical story adds another dimension to the previously discussed differences between the KI and EM. Mathematically, it is possible to formulate a minds’ utility function so that the KI and EM are brought close to each other. This utility function can account for the survival value of quick decisions and also for the limited amount of any individual experience, for uncertainty in observation of data, and for minimizing the worst-case losses (such as preventing death) versus maximizing average gain. The utility function even can account for the fact that future is unknown and therefore individual experience should be integrated with culturally accumulated knowledge. But Maimonides hints at something different, something more fundamental than correct formulation of a utility function. He suggests that “original sin” determining the basic imperfectness of humankind is related to how we do or do not use our ability for knowledge and for making conscious choices.
We will now relate this Biblical account to theoretical understanding and experimental data about the mind and brain, consider neural mechanisms of the KI and EM, relate them to conceptual and emotional intelligence, to consciousness and the unconscious, and will try to add scientific interpretation to this millennial old mystery. Cognitive neuroscience data suggest new interpretations about KI versus EM, which are important for understanding human evolution, which further increase our understanding of knowledge maximization versus effort minimization, between “high” and “low”.
The tendency that Tversky and Kahneman (1974, 1981) found for decision makers to be susceptible to linguistic framing is not universal: a significant minority of adults (along with most young children!) is not susceptible to the distortions of rational decision making caused by framing effects. A functional magnetic resonance imaging (fMRI) study (DeMartino et al. 2006) showed significant differences in brain region activation between individuals who were and were not susceptible to framing effects. The heuristics-violators (KI-type people) had more activation than the heuristics-followers (EM-type) in the frontal lobes and adjacent cortex. Conversely, those subjects whose choices were consistent with the framing heuristic had more activation in the amygdala, the area below the cortex that is most involved with primary emotional experience.
The higher up we go in the hierarchy, the closer we are to the beautiful and sublime, the easier, it seems, to succumb to the temptation to stop thinking and to use ready-made concepts acquired from the culture: language concepts connected not to individual thinking, concepts connected to “Mom and Dad prohibitions,” to amygdalar emotions triggered by previous failures, when we tried to think and got burned. Whereas God demands us to use our cortex, the uniquely human ability for thinking, the old Snake present pulls us to using amygdala, which we share with animals, pulls us back to animal kingdom.