Claudio Gutiérrez
Thanks to an initiative of the Sloan Foundation, the cognitive sciences were reborn in the seventies as an interscience, i.e., an interdisciplinary effort or researchers from several disciplines, namely:
Linguistics, which studies how human beings store and communicate knowledge by means of language.
Cognitive psychology, which tries to clarify the phenomena of perception, attention, memory, and the like, that define knowledge.
Brain science, which analyzes the biological processes that sustain all knowledge phenomena.
Philosophy, that is: philosophy of the mind, which aims to explain the architecture and operation of knowledge phenomena; epistemology, which tries, among other things, to decide the validity criteria of knowledge, especially the scientific one; and logic, which studies the structures of knowledge and the processes of reasoning in which they intervene.
Computer science, which studies the representation, storage, transmission and transformation of knowledge, especially in digital computation machines. In particular, the subespecialty of artificial intelligence, which studies the possibility of endowing machines with intellectual capacities similar to those of human beings.
Although cognitive science was in fact created thanks to the intervention of a financing agency, the theoretical and practical foundations for common work among its practitioners were given long beforehand by two events of utmost importance in this century: an intellectual advance, the work of Alan Turing in metamathematics; and a technological one, the invention of the digital computer. The intellectual advance consisted of the abstract definition in the thirties of a universal machine, able to reproduce the operation of any another machine. The technological advance consisted of the actual construction of that machine during the fifties, within realizable conditions of memory and practical processing speed.
This double phenomenon created a center of irresistible attraction for the, until then separate, disciplines of knowledge. It has proved to have enough magnitude and transcendence to be equated to those exceptional feats in the history of science that we identify with scientific paradigms. Thus, we can consider the Turing universal machine and the digital computer as the joint paradigm of the new contemporary cognitive science.
We should remind ourselves the situation of the cognitive sciences, for example psychology, before the arrival of the computational paradigm. As it is well known, the reigning paradigm was behaviorism, inspired by experiences with animals (for instance, Pavlov's experiments on conditioned reflexes). Its fundamental categories were those of stimulus and response.
The important contribution of that paradigm was give an empirically solid foundation to the discipline, in substitution of a previous paradigm, based on the introspective method, with its load of subjectivity and lack or public observability. German psychologists had postulated, in the nineteenth century, introspection as the characteristic method of psychology. It was supposed that the researcher was able to "observe" psychological phenomena inside himself. Based on that "observation" he could elaborate universal psychological laws. That paradigm persisted down to the forties of this century, at least within so-called philosophical anthropology, under the robe of the phenomenological method. Of course, the introspective "observations" could not be equated with the observations in the physical or biological sciences. In the empirical sciences, different observers could repeat an experiment to check the results on their own, thus controlling, by direct exercise of their senses, the conclusions obtained by other scientists.
Although the behaviorist paradigm overcame the introspectionist difficulty, it incurred in some of its own. It was the price that had to be paid for the achievement of empiric objectivity, and a heavy one at that: the proscription of the vocabulary that we normally use to express our mental life, so-called intentional terms –like "believe," "try," "fear," "hope," etc. That was too much to get rid of: it amounted to throw away the baby with the bath water. Also, within the behaviorist method, there was simply no way of distinguishing, for example, whether amputation of a leg was a surgical or a sadistic act, since we could not look into the internal and subjective motivations of social actors.
Functionalist philosophers have shown, within the computational paradigm, how the use of those terms is as perfectly justifiable in the social sciences as it is the use of powerful commands in a high-level computer language. This is an example of the advantages the new paradigm offers. If instead of taking animals as our model for understanding the human mind, we choose digital computers, their inputs and outputs will be as observable as the stimuli and responses of Pavlov dogs; in addition we will have at our disposal the internal program that determines the behavior. That program will be observable, in the sense that we can analyze it with all the tools of computer science. If Francis Bacon has directed us to discover in the simplest the laws of the complex, animal behavior or the behavior of a machine constitute equally valid strategies for modeling the mind. However, the model of the machine turns out to be more fruitful, since we can inspect their innards directly, and manipulate them to our whim, hardly what we could do with the entrails of an animal. Hence the superiority of the computational paradigm over behaviorism as a great unifier and fertilizer of cognitive science.
Aspects of experimental science
Ironically, the very same development of computer science and technology during the last–say– twenty years have come to blur the validity of the computational paradigm in its original purity. Although computational modeling continues to be useful, the most important recent advances in cognitive science are being obtained by a type of work very similar to that which takes place in the biological sciences. A great number of electronic tools permit nowadays to observe cerebral processes in living beings, humans included, what we hardly imagined possible even a quarter of a century ago. PET (Positron Emission Tomography) technology can serve as a conspicuous example, since we could say with little hyper that it has made possible reading the thoughts of another person in laboratory conditions. Consider also the techniques of open-brain surgery in a waking patient which allow to dialogue with him while electric signals of different neurons in his cerebral cortex are being monitored or excited electrically. Take into account also the many experiments with drugs that have allowed to identify numerous neurotransmitters and their varied functions in mental life with much precision.
Consider also the immense contribution of molecular biology in the identification of the genes that affect mental life and their activation processes. Also, the sequencing of the human genome, and of the genomes of other living or extinct species, that projects a panorama of the unity of all biological beings. Such investigations have made easier to generalize the results obtained from experiments with diverse species to the structures and processes of the human mind. We could quote as an example the fact that the human brain does not have a single building block (be it neurotransmitter or other particular neural structure) that is not common to all other mammals. Or that we share more than 98% of our own genes with the genome of our nearer relative, the Chimpanzee. Such discoveries cannot fail to affect deeply the concepts and methods of the work that currently goes on in cognitive science.
Among these achievements, which increasingly place cognitive science within the general paradigm of empirical science, the emergence and consolidation of topobiology deserves special mention. This is the science of organic development, i.e., the circumstances that mold the growing organism, including of course the brain, which neither depend solely on genetic endowment, nor yet largely on physical or social conditioning. Between inheritance and learning one has come to identify development as an important tertium, with their own categories, processes and constraints. Think for example in the development of the brain, where the neurons begin to grow throwing axons and dendrites in all directions. Pay also attention to how physical obstacles, including the interference of appendixes of all other neurons (remember the dense packing of neurons in the cortex), are bound to determine greatly what ends up connected with what. Topobiology offers us a kind of geometrization of biology, which, taking apart some technical differences, we could parallel to the geometrization of physics established by Einstein: the presence of matter determines possible space, so much in astronomy as in biology. I think that, as the work of researchers like Gerald Edelman and Jean Pierre Changeux have contributed to show, topobiology offers an elegant explanatory resource, typically mechanistic in kind, of the complexity of neural connections in brains, especially the human one.
Aspects of speculative science
Apart from the surge of experimental methods which are now applicable to cognitive science, thanks to the creation of computer-science tools, I distinguish a group of very important theoretical movements that also affect it in profound ways within the contemporary circumstances. Some have originated in the last years; others have been with us longer, but their widespread acceptance is only recent. All these movements have two characteristics in common. The first one is a greater concentration in the very phenomenon of knowledge, and less in the knowing object. NOTE 1 I seem to perceive a growing interest in knowledge by itself, regardless of who has it and even of its medium, which permits a greater applicability to generalization. Knowledge, it seems, has come to be more and more the center of attention of cognitive science. The second characteristic of the current situation is the growing appreciation of evolutionism as an integrative factor of the different aspects of cognitive theories. It seems as if, at this moment, the "Darwin universal machine" is taking the upper hand over the "Turing universal machine," as the main explanatory and integrative instrument for the different cognitive disciplines. A comparative outline between these two machines may be helpful.
I want to present some concrete examples of theoretical innovations that justify the importance of these two tendencies. I will begin with one that seems to span a bridge between the Turing and Darwin machines.
Holland's genetic algorithm (HOLLAND 75) introduced evolutionism to artificial intelligence. It fructified there to the point of generating an independent discipline: "artificial life." The works of Thomas Ray and others make it clear that the best (perhaps the only) path to a digital mind is the one that evolution has followed to produce the organic mind. The emergence of this new discipline seems to confirm that evolution is an algorithm of totally general character, independent of carbon chemistry and capable of operating with indefinitely many materials and in very diverse universes (for example, the close and digital universe of a computer memory).
Gerald Edelman starts from his experience with the immunological system, where he was able to verify the evolution for natural selection of antibodies, and goes on to postulate a similar system of intra-organic evolution for the cerebral cortex (EDELMAN 92). This intra-cortical evolution would explain the problem-solving ability of the brain.Daniel Dennett supplements in the same line with his editorial pandemonium model of the linguistic activity of consciousness (DENNETT 91) . It is the entrance of evolutionism to the innermost quarters of the mind. The subjects of mutation and selection are here very peculiar: patterns drawn by neural interconnection in the immense population of cortical neurons.
Works like that of Terrence W. Deacon seem to have dethroned the innate theories in linguistics associated with the work of Noam Chomsky. Such theories are being replaced by a causal doctrine in which language as an autonomous knowledge system evolves as much as the brain, adapting itself to be "friendly for the child" who will learn it. The emergence of this kind of theory underlines the tendency to focus on knowledge systems as much as the importance of a theory of generalized evolution (carbon independent and integrative of multiple evolutionary processes, including those made of unconventional replicators).
The French philosopher and computer scientist Pierre Lévy proposes a generalization of computer science as a technique of intelligence (which we could as well call a technique of knowledge). He offers examples of characteristic techniques of intelligence from previous cultural times: printing, writing, rhapsodic procedures in pre graphic cultures. In the latter, dramatization, with all its paraphernalia, is the pre graphic method of creating and maintaining the collective memory of societies. In that perspective, the oral literature represents the first form of virtual reality that humanity has known (LÉVY 90). With this theory a very firm bridge is spanned between the cognitive science and the humanities. At the same time, the objective character of knowledge is insisted upon, independent from the cognizer and from its implementation media.
Richard Dawkins proposes a generalization of evolutionism that makes it applicable to cultural constructs, like for example the wagon with spoked wheels or the game of football. These cultural replicators go under the name of memes, which are the genes of the cultural Kingdom. Their habitat is the mind, and they move from mind to mind, subject to the selective pressure of the acceptance by human beings (DAWKINS 76). This theory signifies a very important contribution to the universalization of evolutionism, which one can now apply to culture with equal right than to biology. It also represents a radical insistence on the autonomy of knowledge, integrated by a population of memes, independent of whatever mind happens to offer them transitory lodging.
Daniel Dennett generalizes the concept of research and development in order to embrace the entirety of human history and prehistory, and further on, until the very beginning of life. He bases this bold intellectual maneuver in a concept of design which does not need a designer. Each achievement of natural selection by itself can constrain the possibilities of ulterior mutation and selection, progressively reducing the leeway of randomness (DENNETT 95 ). He does, by this means, not only unifies engineering and biology (considered as "inverse engineering," because the biologist tries to discover the blueprint of organic design), but also consolidates the independence of knowledge with respect to minds. Within this generalized conception of design it becomes possible to say that (pre mental) life does know the design which it is bound to reproduce and mutate.
Economists have reconciled themselves with the idea that knowledge is not only a factor of production but even the most important one of all. NOTE 2 To that effect, they had to overcome the previous much-ingrained idea that the state of technology at a given moment should be considered as gratuitous as air for the purposes of economic analysis. NOTE 3 This revision of economic theory has special importance in a socio-political atmosphere which increasingly recognizes free competition (an essentially Darwinian idea) as the firmest base for social development.
We face the fact that cognitive science has staged a migration from the closely methodological concept where one insisted mainly on conditions of knowledge validity, toward a thematic concept of the science, with concentration on knowledge, its object of study. On the other hand, that object has spread out, so much outwards (toward the humanities, through the ideas of memes and techniques of intelligence) as inwards (to the very genetic bases of knowledge in molecular biology). The concept of knowledge is now generalized, as an omnipresent feature of life, conscious or not. Ironically, we encounter here what we could recognize as a redeemable legacy from vitalism and teleologism: organized matter needs knowledge in order to survive, and it does so in all stages of its evolution, starting with the construction of the very first cell. History and biology appear thus indistinctly based on knowledge, although of course, this conception of knowledge is bound to a "design without designer," in the purest mechanistic epistemological atmosphere.
Finally, I see a movement beyond interdisciplinarity, toward a true universality: We recognize in cognitive science contributions from genetics, paleontology, history, literature, but also economy, medicine, engineering. We are in the presence of a true "common market" of knowledge, widening out in concentric circles, from the first nucleus of five founding disciplines. That movement develops itself under the canopy of the ultimate paradigm, a generalized theory of evolution, the great super integrator of all the sciences . . . and the humanities.
Is this reductionism? In a sense it is, but more than anything it is universalism. Universalism in the sense in which the theories of Einstein are universal, for example, by unifying electricity and magnetism, or physics and geometry. Would we feel comfortable calling Einstein reductionist, for searching until his death, evermore encompassing theories? At bottom, cognitive science will not be able to extricate itself from the imperatives of its own object matter: To build an image of the universe and all there is within, evermore coherent and complete.
NOTE 2 See Jan Fagerberg and Alvin & Heidi Toffler.
NOTE 3 See for instance Robert M. Solow.