What this research is intended to show is that an enriched environment leads to neuronal and cognitive growth in humans; that the high technology information society we presently find ourselves in is such an enriched environment; that we can understand the linkages involved and apply this knowledge in guiding both human development and human healing. These linkages are explicated by a Peer-to-peer Network Model (PNM) that leads to a nonlinear, nonhierarchical model of consciousness and behavior. This paradigm draws heavily from Evolutionary Epistemology (EE) and chaos theory as applied to psychology.
The overall view I take in the philosophy of science is that scientific research and the theories and hypotheses that scientific research consists of, concern phenomena, events, entities. That these entities exist independently of our knowledge of them and that they obey natural laws, make it science's goal to describe and explain both the observable and the unobservable facets of this independently existing world. This view is a melding of scientific realism with naturalism.
I will now give some definitions of the terms and concepts I'll be using. The first of these are linkages and the PNM. Linkage describes the interactions and the paths between and within entities such as a Conscious Mental Process (CMP), information, genetics, and the environment. Linkage takes into account the equal importance that all of the above entities have in a PNM, instead of the strict hierarchical relationship that so many other theories, like all reductionistic ones, put forth.
The PNM often exhibits a first among equals relationship in these linkages. Individual entities will make their own informational, analytical, or materialistic contributions in whichever proportion is appropriate in the circumstances. Take, for example, the complex, chaotic milieu of one's experience and education, one's present mood, the individual degree of cognitive processing possible, an awareness of the physical environment and changes therein which lead to or cause a sudden understanding in the solution of a problem. Which of these could be said to occupy the top spot in a hierarchy?
Compansion is a term I've coined that is a contraction of Complex Expansion. Compansion implies both growth and change and is the outcome of the various energy fluctuations that lead to bifurcation. Most compansions are not smooth or equally continuous in any direction, i.e. they are not necessarily concentric or symmetric in their linkages. Compansions can be fairly linear though, such as in the slow dawning of understanding or learning, or they can be saltatory as in the "Aha" experience.
The last term that needs defining in advance is EE. This concept derives from Darwinian evolution and posits that the biological underpinnings of intelligence account for our possession of intelligence in the first place, and the evolutionary aspects account for its growth. What makes humans truly distinct are the twin concepts of intelligence and knowledge that underlie rationality, and the recursive manner in which these two concepts build upon each other.
The most important point in accounting for the rapid spread of knowledge and the growth of intelligence is the human desire of and need for knowledge. The quest for knowledge is as important as the drives for sex and food, and humans cannot survive without knowledge of their surroundings. Humans can barely function without the information processing capabilities of the mind and its advanced intelligence. As Nicholas Rescher so aptly points out, "cognitive vacuity, dissonance, or disorientation can be as distressing to us as physical pain." We therefore have cognitive Darwinism as a survival tool. We have the ability to pass on values and beliefs to succeeding generations by purposeful variation as opposed to biological randomness, and we can select these values and beliefs by rational means in contrast to biological natural selection.
One view that seems to be accepted by all philosophers of science is that there is no theory without an underlying world view, framework, or as Thomas Kuhn says, paradigm. EE then gives us a paradigm to show how the theory of operant conditioning, as held onto by the behaviorists, has come up short. By focusing on the mechanistic-deterministic brain-body linkage, it has missed what is really going on in human behavior and the evolution, cultural and individual, of thought processes, learning, and growth in general. Once intelligence, which is the basic method of turning environmental information into knowledge, arose in homo sapiens, (although probably earlier, but for the sake of argument, I'll just deal with homo sapiens,) "...it sets up pressures towards the enlargement of its own scope, powerfully conditioning any and all future cultural evolution through the rational selection of processes and procedures on the basis of purposive efficacy" (Rescher 1990, p. 40). Conditioning and response reinforcement should also be looking at the way humans can purposely condition and reinforce the mind, and the minds's conscious and subconscious effects on purposive and learned behavior, and not only on the blindly mechanistic linkage between the body and the physical environment.
The view that the strict behaviorists take is that operant conditioning is the only paradigm needed in associative learning or behavior analysis. Although some of the behaviorists admit that cognitive processes exist, these cognitive processes have no effect on the outcome of the stimulus-reinforcement linkage, act only as passthrough variables, and add nothing to the explanatory power or act to falsify the operant conditioning paradigm. This is a law-like, deterministic paradigm which must have mechanical linkages with the environment only (Lieberman 1990, p. 349).
The problem with this paradigm is that it leaves out CMPs like expectation, rationality, and willful modification of events and responses depending on beliefs. However, there is more to learning than what is in the environment, such as the quest for self-knowledge or the effects which the human created social structure have on behavior and learning. The human quest for understanding concerns more than the survival skills necessary to obtain food or escape from predators. The cognitive behaviors of exploration and scientific inquiry also work by reinforcement of methods that have purposive efficacy.
So it would seem that not only are CMPs necessary in fully explaining behavior, but are a major input to the behavior of conscious rational agents. This is a more naturalistic and plausible paradigm of operant conditioning, as nature has provided the foundation for minds development, therefore mind is a naturally founded phenomena, and we see the order and periodicity of nature because we are natural creatures. As Rescher says, "it is no more a miracle that the human mind can understand the world through its conceptual resources than that the human eye can see it through its physiological resources" (1990, p. 61).
Humans, as rational agents, develop methods of scientific inquiry that prove historically successful, and we rationally abandon or refine those methods that fail. It should come as no surprise that our inquiry into nature has been improving. We are positively reinforced in a natural way for our successes. To slightly paraphrase Rescher: A world where the operant conditioning of evolutionary processes allows intelligent creatures to emerge must be an intelligible world (p. 65).
When success comes from a theory, the probability of the further use of that theory increases. An association is formed between the epistemic assumptions of the theory and the furtherance of our understanding; our knowledge and intelligence further evolves; and our understanding of nature, and ourselves as part of nature, increases. Nature must be, and has been, cooperative in this evolution of intelligence and the learning process. The regularities and periodicities in the linkages between nature and the organism, while often complex, must be stable and structured enough for the appropriate responses to natural events to be learned, and culturally passed on to succeeding generations. The studies that have been done in the field of associative learning through the various models of behaviorism (neo, radical, and cognitive,) all generally show that the more time spent with a stimulus, and the amount of similarity that can be generalized to other stimuli and environments, help in increasing both the strength of the association and the amount of learning.
This then becomes a more naturalistic outlook than what is normally associated with the schools of behaviorism. The behaviorists have been unwilling to expand the paradigm past the association of the environment working on the organism. This is a one-way linkage and the proponents of operant conditioning have let themselves be content with this underdetermined theory.
Naturalism, however, should include more than only what nature allows, such as what an organism like a human being can do as a natural, rational agent, or as an agent of change. Cultural evolution and EE can then be seen as also being natural in the compansion of the mind <-> culture linkages. Natural evolution and the biological brain have given way to the emergent qualities of cultural evolution and EE. We are now involved in a regenerative feedback loop with nature. What we can learn of nature is reinforced by nature itself, and nature and culture by evolutionary means conditions what we can further learn of nature by the growth of our natural and cultural intelligence.
This view of operant conditioning then melds into the systems view of science, and helps point out how the reductionism entailed in empiricism lead to many of the explanatory failures in behaviorism and the behaviorist's view of associative learning.
The basics of the systems view is that systems theory is the theory of organization. Rather than breaking things down into their constituent parts and isolable mechanistic causes, integrated relations that are not reducible are looked into. Specialization is all right since it gives depth, but without integration there is no breadth. The quantization of empiricism has shown us that experimentation and testability are indeed powerful tools, and we need to keep this power in mind while investigating the linkages between the theories that guide the various scientific specializations and philosophical traditions. The gaps in our knowledge cannot be filled by dogma or by pretending they don't exist. The patchwork knowledge of empiricist specialization almost by definition can not give us a unified understanding of ourselves and/or nature.
One of the things I find myself worrying about is that with the over reliance on mathematical models and empiricist methods of explanation, we've lost the humanity that should be explicit in psychology. As humans, as conscious rational agents, we have the power to shape and guide our behavior, beliefs, and socio-cultural evolution. As mammals in the world of nature, we have biological limits and innate linkages. This means that we cannot be totally reduced to Descartes' reflex arc, but neither is there a total absence of pattern or organization, and it is the organization that takes center stage in most instances.
The behaviorists want our experimental efforts to be exact and point to physics as a paradigm example to follow in the quest for rigid mathematical proofs and strength of prediction and explanation. However, without a guiding paradigm for the science of psychology, there is not too much exactness or agreement. How many of us have read a definition in one of our $50-$75 texts, and then had our professor say, "But I would define it as..."
Explanatory failures are evident in cases like the Rescorla-Wagner Model. This model is used to explain why conditioning, blocking, and extinction occur. This is done by choosing arbitrary constants after the experimental data is gathered to make the resultant graphs from the model match the data. However, this model fails in experiments of configural learning, where compound stimuli do not show the associative strength of the sum of the components the model predicts, and in latent inhibition where the model predicts that no conditioning would occur.
Another example is Capaldi's Sequential Model, which doesn't fail so much as it becomes overly convoluted in assigning even more variables to overcome problems that result from failing or refusing to consider cognitive or biological factors. As Breland and Breland (1961) point out when they attempted to take standard operant conditioning techniques out of the laboratory, they ran ". . . afoul of a persistent pattern of discomforting failures . . . [that] all represent breakdowns of conditioned operant behavior" (p. 681). They then go on to say "that there are definite weaknesses in the philosophy underlying these techniques. . . . When behaviorism tossed out instinct, it is our feeling that some of its power of prediction and control were lost with it" (p. 684). Breland and Breland then conclude ". . . that the behavior of any species cannot be adequately understood, predicted, or controlled without knowledge of its instinctive patterns, evolutionary history and ecological niche" (ibid).
When we try to reduce behavior to the strict one-to-one functions of the stimulus-response paradigm, we forget the warning of the cognitive scientists ". . . that any theory of mind that fails to talk about the intervening mental processes that link these stimuli and responses will be unacceptably incomplete" (Flanagan 1991, p. 177). As E. C. Tolman pointed out, incoming stimuli are ". . . worked over and elaborated . . . into a tentative cognitivelike map of the environment. And it is this tentative map, indicating routes and paths and environmental relationships, which finally determines what responses, if any, the animal will finally release" (1948).
One of the other reasons the Rescorla-Wagner model fails is due to trying to squeeze behavior into a linear model. As I said at the beginning of this paper, there are laws of nature, and some of these laws are self-regeneration, fractal self-similarity, and especially sensitive dependence on initial conditions, which are all part of nonlinear dynamics, or chaos theory. One of the shortcomings of empiricist methods in general, and of modeling in psychology in particular, is the failure to realize that biological systems are nonlinear dynamic systems. The linkages don't always show symmetry; doubling one magnitude doesn't double another; the output is not proportional to the input or inputs, (as most anyone who has had to study for a test in "Conditioning and Learning" or "Memory and Cognition" will attest.)
The ubiquity of the possibilities of chaos theory modeling in the natural sciences led mathematician Stanislaw Ulam to remark that calling chaos theory "nonlinear science" is like calling zoology "the study of non-elephant animals" (Paulos 1991, p. 37). As tools are developed for studying nonlinearity, it can be seen that it is not necessary to ignore or screen out noise, random contingency, arrhythmia. These facets of our world "like fractals, are everywhere, and it is their linear cousins that are, like elephants, rare" (ibid).
I really feel like the empiricists had their hearts in the right place, unfortunately, they weren't pumping enough blood to their brains. Once many of the empiricist psychologists came to the conclusion that behavior could not be reduced to mechanistic Newtonian physics, they tried to reduce human nature to neurology. Ironically enough, one phenomena that shows the empiricist doctrine of reductionism not to hold, is the empiricist science of computer science. That psychological phenomena can be reduced to neurology in all cases is shown not to be true by "nomologically possible systems other than organisms (vis. automata) which satisfy the kind predicates of psychology but which satisfy no neurological predicates at all" (Fodor 1974).
In the feud between the behaviorists and cognitivists, each school relates certain phenomena into organized simplifications, and then either denies or ignores the other. But the organizational schemes should be looked at as nothing more than a way of fitting phenomena into context dependent classes of similarity. Looked at this way, perhaps we should look to physics for an example of scientific methodology, especially in the tolerance of different views. Physicists have no problem with talking about electrons as either a wave or a particle, since they understand the validity of different explanations depending on the context. Psychologists should also exhibit no reluctance to discuss human nature as either behavioristic or cognitive. In the effort of constructing a more complete domain of discourse to pull theories of explanation and prediction from, it should be understood that in some cases the difference depends on the context of the question we are seeking answers to.
Another example from the physical sciences concerns the use of constants, which I can't help but call fudge factors. In cosmology, a factor called the Hubble constant is used in determining the age of the expanding universe. While highly regarded in its purposive efficacy, when first put forth by Edwin Hubble in 1929, it calculated an age for the universe that was approximately 2 billion years, which was younger than the geological evidence then known gave for the age of the earth. Recent refinements to this constant (does it strike anyone else as odd that constants seldom are?) put the age of the universe between 11 and 18 billion years.
But hey, plus or minus 7 billion years for the age of the universe, that's pretty close, so maybe I shouldn't be quibbling so much with the use of arbitrary constants in psychological models. I do, however, question the degree of dogma attached to the acceptance of these models. The uncertainty and explanatory failures should caution us that traditional methods of viewing behavior and learning are still very much underdetermined.
This is not meant to totally denigrate the schools of behaviorism or even classical conditioning. The application of the successes of their research is especially apparent in the field of animal training, (well, at least for most species of animal besides humans,) but they do give us quite a bit of information and predictive power in the understanding of what our innate biological resources will allow. However, any theory that does give us a closer view of the complex relationships in the Brain-Mind-Body compansions will draw freely from several different schools of thought that have been held to be distinct and autonomous.
Even when discussing the different fields of science, autonomous they may be, but they are not independent. Ervin Laszlo (1972), one of the pioneers in systems science, uses the wall of nature analogy. As scientists, in any field, we have historically tended to stand straight in front of this wall of nature and drilled exploratory holes straight into it. We do tend to probe to different depths on occasion, and get a bit off-center and toward the edges of the holes, but rarely do we connect the probes or try to see what's in between. When we do, it seems to be only because of the relative closeness of the probes, such as between psychology, philosophy, and sociology, or between physics, chemistry, and cosmology. There are quite a few internal linkages, such as in physics with photons, optics, and lasers, but relatively few external.
When we take the systems view however, and look at the wall of nature from the side, and start drawing the internal and external linkages, the model starts looking like a neuronal column through the cortical laminae. This organizational complexity is nonhierarchical in both vertical and horizontal phase space, that is, at any compansion level. Edward White (1991), author of "Cortical Circuitry," says in terms of "cortical circuitry, we must think not only . . . of chains of interconnected neurons, one synapsing with the next, but rather, in a broader sense, of more complex interactions between networks of interconnected neurons" (p. 51).
Recent research from the neuro sciences has shown that the connections between thalamic nuclei and neurons of all types in the primary sensory area of the neo cortex "provides a direct and important challenge to the concept that thalamic input to the cortex is processed by hierarchically organized chains of neurons" (p. 67).
This then, is the paradigm of the science of psychology that I intend to follow, and the epistemic qualities inherent in this paradigm. It shows there are conditioning facets in cognition, and cognitive facets in conditioning; that beliefs and desires, in the proper circumstances, can overcome or inhibit innate or conditioned behaviors. The rate at which these capabilities are growing coincides with the growth of cultural evolution over natural evolution, however, we are not going to come to understand ourselves if we consistently ignore an entire class of phenomena.
Totally mechanistic systems have only two states; they either function, or they're broke. There is no allowable mechanism for change, for the plasticity exhibited in dynamic open systems, such as the brain and mind. However, in light of the above fuzzy examples from the hard sciences, I think psychology has as much right to be called a science as any other, and probabilistically speaking, we're probably just as correct.
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