An Introduction
by Jeff Bloom
THEMATIC CLUSTER
Making Sense of Making Sense
CATEGORIES: complex living systems, complexity, ecology, learning-cognition, relationships, schooling, teaching
This Thematic Cluster in its present form is probably in its fifteenth version, which originally started out as a book. Over the years, I’ve developed one outline after another and one chapter after another, but have never been satisfied with any of these earlier versions. One of the major difficulties I kept running into was dealing with the slipperiness of concepts like “learning,” “epistemology” or “knowledge,” “meaning,” “understanding,” “memory” or “remembering,” and so forth. As much as we (i.e., everyone) use these words, they are difficult to pin down to one specific definition or description.
• Merriam-Webster Dictionary -- Essential Meaning of Learning
1 : the activity or process of gaining knowledge or skill by studying, practicing, being taught, or experiencing something : the activity of someone who learns a computer program that makes learning fun different methods of foreign language learning The first year of college was a learning experience.
2 : knowledge or skill gained from learning They were people of good education and considerable/great learning. book learning [=knowledge gained from reading books]
The dictionary definitions, above, do little to clarify what learning actually is, what it actually looks like, or how it happens. From a cognitive psychology perspective the clarity of a definition for “learning” is not that much better and even may be more confusing than clarifying:
Cognitive science and related fields typically use the term “learning” for the process of gaining information through observation— hence the name “learning theory”. To most cognitive scientists, the term “learning theory” suggests the empirical study of human and animal learning stemming from the behaviourist paradigm in psychology. The epithet “formal” distinguishes the subject of this entry from behaviourist learning theory. Philosophical terms for learning-theoretic epistemology include “logical reliability” (Kelly [1996], Glymour [1991]) and “means-ends epistemology” (Schulte [1999]).Excerpt from: “Formal Learning Theory” in The Stanford Encyclopedia of Philosophy (2002/2017) Available at: https://plato.stanford.edu/entries/learning-formal/
If we continue to explore the explanations of learning, the clarity of explanations or does not improve. In fact, we are mostly left in a muddle about the nature and dynamics of learning no matter where we look. However, there are a few exceptions, which will be addressed throughout this book. In addition, I will draw on some ideas from the current muddle of psychological and philosophical literature that are useful, but usually get lost in quagmires of contradictions, paradoxes, relatively disconnected details, and unhelpful jargon.
One of the problems we have had as humans is, in fact, how we think about how we think and learn, and then we exacerbate the problems when we think about what we need to do in order to understand something. I hope that last sentence was sufficiently confusing, because that is pretty much the type bound up muddle we have created for ourselves. We have tied ourselves up in knots, but we only see what is right in front of us, which looks like a straight piece of rope and not a tangled knot. This muddle seems to have it’s deepest roots in the development of Western thinking that has arisen from ancient Sumerian across to Greek civilizations, which seemed to be particularly focused on hierarchical social developments and linear cause and effect thinking. At the same time that these sorts of patterns of thinking developed in the Middle East and Mediterranean areas, different patterns of thinking developed in South and East Asia, Oceania, Australia, Africa, South American, and North America. These patterns of thinking were much more circular or complex. This circular sort of thinking was not concerned with hierarchical societies or linear cause and effect. These ancient peoples tended to think about the complex sets of interdependencies that were evident in their natural and social environments. The “Western” view was much more concerned with objectivity and its subsequent disconnect among and between people and everything else in the world. Meanwhile, the rest of the world was concerned with the relationships between one another and between people and the living and physical world around them.
By the 17th Century CE, René Descartes cemented the seal on this objective and disconnected view of the world, which was further enhanced by Isaac Newton and his contemporaries. We now refer to the establishment of this worldview as three intertwined companion paradigms: Positivism, Reductionism, and Mechanism. These three paradigms worked their way into the worldviews of people and societies throughout Europe and then throughout the rest of the world.
Positivism is basically a view that maintains that there is one objective truth that can be understood through empirical or scientific observation, which includes experimentation and mathematical proofs. Reductionism maintains that we can understand any particular organism, object, or event by dissecting it into parts. If we understand all of the parts, we will gain an understanding of the whole entity. And, Mechanism maintains that all living things work just like mechanical devices, so we can understand how living things work by applying the principles of mechanical physics. These three paradigms had an amazing impact on societies. They gave rise to the modern Western sciences, including biology, chemistry, physics, geology, psychology, anthropology, medicine, and so on. They changed the world. Some of these changes were no doubt beneficial from the human perspective. However, we are finding now, that these same paradigms have created huge problems, as well. Pollution, global warming, ecosystem collapse, increasing frequency of environmental disasters, climate change, increasing frequencies in certain types of diseases, increasing populations beyond the point of sustainability, and so forth, can all be attributed, to one degree or another, to advances in science and technology. At the same time, here I am sitting in my home with heat and air conditioning, living in an environment (Arizona desert) that is not naturally conducive to human life, typing on my computer, while sitting in a reasonably comfortable swivel chair surrounded by books. As much as we try to limit our consumption of goods, water, and energy, we still have much too large of an ecological footprint. This sort of situation is where most of us find ourselves — caught in situations where we want to maintain some sort of lifestyle while pushing the limits of what can be sustained by the biosphere.
If we can briefly return to the initial quandary over a definition or explanation of learning, you may be able to pick up on the insidious incorporation of the three paradigms we just discussed. The dictionary definition centers upon “gaining knowledge,” which really doesn’t say much. What is the process of “gaining?” How does that work? In fact, it almost seems magical. And, “knowledge” is another word without any specific meaning. Is it just an accumulation of information of some kind? What kind of information? What does actual knowledge look like? Where is it “stored?” In the psychological definition “gaining” is used again, as well as “empirical” and “behaviorist.” Guess which paradigms undergird empirical and behaviorist or behaviorism. Behaviorism is still a dominant force, even among psychologists who criticize the older paradigms. Behaviorism is a love-child of these three paradigms and avoids all consideration of mental and emotional activity, which has been behind most of psychology, including psychotherapy. These paradigms are so embedded into our everyday lives that we fall into these patterns of thinking without realizing it. Even when we make a conscious effort to think about a something in a field that is the antithesis of positivism, reductionism, and mechanism (PRM), such as the complexity sciences, we start to weave PRM ways of thinking into making sense of this other field, like complexity. These paradigmatic ways of thinking are insidious. They weave their way into almost all of our thinking, even thinking that we regard as religious, spiritual, and social. It requires a concerted and more or less continuous effort to avoid such patterns of thinking, or at least to know when and where the use of PRM patterns of thinking is appropriate.
If you are wondering about alternative paradigms or ways of thinking, there is a significant paradigm that has its roots in thinking that emerged partly from quantum physics[1] (especially with insights into the unpredictability and uncertainty evident in subatomic behavior) and mostly from the early days of cybernetics[2] in the middle of the 20th Century. Curiously, this new way of thinking shares many of the characteristics of the thinking that developed outside of the Middle East and Mediterranean regions prior to the 4th Century BCE. This new and still emerging paradigm is called complexity or complexity sciences, complexity theories, or chaos and complexity theories. Fundamentally, complexity describes the nature and dynamics of living things and living systems. Such complex systems include everything from individual bacteria to primates; from small social systems, such as families, to the large socio-political systems among nations; and from a local garden plot to ecosystems and to the biosphere. Such living systems can be characterized by their abilities to maintain, organize, and even transcend themselves, which is known as autopoiesis or autopoietic systems. They also are unpredictable and do not operate in linear processes, but rather are comprised of multiple, interdependent, nonlinear, and recursive processes. We as individual human beings are complex systems. Our bodies maintain and repair themselves. We transcend ourselves and are not just the sum of all of our parts, as reductionism suggests. We are more than that. Our lives touch the lives of others. We express ourselves through all sorts of external entities, just as some of whoever I am is being expressed through my writing, photography, my decorations, etc. We also are unpredictable. We can see that in how each person may take the same medication, but everyone reacts differently. We also can change our minds and improvise in ways that are totally unexpected. And, each of our component systems — cardiovascular, pulmonary, nervous, digestive, immunological, skeletal, integumentary systems — are intertwined and interdependent. Take away one system and all the rest collapse. But, it goes further than this. We are interdependently intertwined with plants that produce oxygen and take in carbon dioxide, while also producing nutrients for our survival. And, plants are in interdependent relationships with bacteria and fungi, as well as with insects, birds, squirrels, and so on. Our whole world is one gigantic cluster of interdependencies, which is known as the biosphere.
Now, let’s return again to the issue of “learning.” Most of our contemporary views of learning, and especially those that determine our approaches to schooling have a long and deep history in the positivistic, reductionistic, and mechanistic paradigms. And, as mentioned, these paradigms are insidious and tenacious. A part of this insidiousness and tenacity is similar to what happens with conspiracy theories, where the rationale has a certain resonance with our personal experiences, preferences, assumptions, beliefs, and emotions. We have all grown up and lived in a world that has been pretty much defined by the big three paradigms. Even most religions, especially those of the Judeo-Christian-Islamic group, align comfortably with a sort of reworked version of positivism, reductionism, and mechanism. From the religious perspective, the “one truth” of positivism is not based on scientific observation, but on what is espoused by the religion. It is the same pattern, just modified to fit a new context. This particular group of religions all arose out of the same Middle East linear thinking cultures discussed above, so there is some historical alignment.
Behaviorism is a particularly interesting sub-paradigm of the big three. Ivan Pavlov established this approach to understanding non-human animals in the late 19th Century. His early studies with animals expanded to include humans in the early 20th Century. John Watson, who worked with animals, began to make inferential connections to human behavior. Then, B. F. Skinner, who also worked with non-human animals, started to make the leap to work in human psychology, especially after his book, Walden Two, which described a society based on behaviorism. By 1960, Behaviorism had assumed a core position of most psychological studies and therapeutic practices.[3] And, the entirety of this position has its roots in positivism, reductionism, and mechanism, where one’s internal emotional and mental state is more or less irrelevant. Learning and thinking can only be assessed by observable indicators in the behavior of an organism. For humans, that observable indicator translated quickly into the use of tests for the assessment or “measurement” of personality, psychopathology, learning, skills, and so forth.
From this point, behaviorism pretty much took over the entire approach to schooling, from classroom management to Bloom’s (no relation to me) Taxonomy of Educational Objectives and the fundamental view of children’s learning and how to teach to that type of learning. Much of this take-over of education is reflected in the terminology that is commonly used today in the contexts of schooling:
- Direct Teaching — just what it sounds like — spew out information
- Lesson Plans — a sequential plan for delivering instruction
- Learning Objectives — what students are supposed to learn… exactly
- Worksheets — a decontextualized and repetitive process of completing some task
- Closure — all instructional sequences need to reach a point of closure, where all of the information presented is completed in a nice neat package
- Accountability — an “objective” assessment of a teacher’s practice based on student achievement on tests
- Anticipatory Set — the first step in a sequence of instruction
- Scaffolding — providing a step-by-step sequence of “learning”
- Efficiency — a mechanistic approach to covering the material, rather than paying attention to what students are actually learning
- Prescriptive Learning or Teaching — an approach to teaching to a predetermined deficiency as assessed by testing
- Teacher-Proof Curriculum — a curriculum that is meant to be followed exactly — word for word — at a predetermined speed, which cannot be modified by the teacher
- Information Processing Psychology — based on the ways computers work, which is just an extension from the logical approach to mechanistic systems
- Educational Standards
- Testing and High-Stakes Testing
- Stages of Development
- Students Being “On Task”
All of this terminology and much more has roots in Behaviorism and the Big Three Paradigms (PRM). As such, they pretty much ignore the inner worlds of our children and adult learners. Students move through each grade and each level of higher education like some piece of equipment being built in a factory. Knowledge is poured out, then regurgitated back, or not, on a test, and then mostly forgotten over a short period of time. And, any “knowledge” that is retained is mostly disconnected from meaningful contexts or contexts that are relevant to the learner. If the knowledge is embedded in meaningful and relevant contexts, it generally not the “fault” of the schooling, but is due to the learner, who has developed relationships to meaningful contexts.
Most past and present research on cognition and learning is still highly focused on the details, “the parts,” without placing them in the larger contexts of both the individual and the contexts in which that individual lives. In addition, all of these parts are fundamentally seen as linear and mechanistic processes. Even the more recent emergence of information processing psychology is stuck in a similarly reductionistic and mechanistic approach based on how computers “process” information, which is based on multiple series of binary-based, on/off, information.
What Does Complexity Have To Do With It?
As complex systems, we and all living things, as well as all social and ecological systems, think, learn, and communicate. From bacteria and maybe even viruses through primates, biological systems engage in these “cognitive” processes, even in
the absence of a nervous system. Every day, new research is published that describes such learning, thinking, and communicating in a wide range of organisms, even viruses.[4] These cognitive processes are essential aspects of complex living systems, and are most certainly early adaptations for survival.
From the perspective of complex living systems, cognition does not operate like a computer, even though researchers have been able to fit the round data into square holes. I suppose if you reduce the round data to a small enough diameter you can make it appear to fit in the square hole. This “roundness” metaphor describes living cognition that is more than just on/off bits of information. Cognition includes a vast array of more complicated information, including emotions, values, beliefs, imagination, imagery, humor, paradox, empathy, and so forth. And, as we’ll see later, all of this type of qualitatively different “information” is associated with the semantic sort of information we encounter in school-type learning of specific conceptual and factual knowledge. Nora Bateson has described this qualitatively different information as “warm data.”[5] In other words, living systems do and life does more than just “process” cold, lifeless, decontextualized bits of information.
As opposed to mechanical and electronic systems, living things learn and think in ways that not only are analogic as well as digital, but also involve a great deal of concomitant “warm” information. Mechanical and electronic systems operate along strictly logical sets of relations. If you haven’t noticed, people are completely capable of illogical thinking, which can be beneficial as well as problematic. This sort of capability of illogical, emotional, and imaginative thinking provides for creativity, play, exploration, as well as crime, other ways of breaking the rules, and all sorts of unpredictable actions.
In this thematic cluster, we will be exploring the nature and dynamics of cognition with an emphasis on learning. However, this book will not address the biochemical or molecular level of learning, since that is still relatively unknown territory. No one is capable of finding in my head or body the memory of my mother and father at the molecular level, if that is indeed where and how information is “stored.” At a more generalized level, current thinking holds that learning and thinking are distributed throughout our bodies.[6] Gregory Bateson and others more recently have suggested that cognition, as the cybernetic flow of information, extends well beyond that of the individual to others and to various objects in our environment. [7] Bateson’s classic description of distributed cognition involved a man chopping down a tree with an axe. The flow of information goes from the man’s brain to his arms and hands — to swing the axe upwards then downwards — the contact between the axe and the tree sends information into the tree, which in return sends back information on resistance to the path of the axe. This information travels up the axe into the man’s hands and arms, then to his brain. The circuit of information flow extends out of the body and then back again. The same sort of circuitry can be seen in driving cars, writing on a piece of paper or computer keyboard, washing dishes, and just about every physical activity. Socially distributed cognition also occurs in social contexts ranging from informal social gatherings to classrooms and other settings where there is a focus on working and learning together, such as workplaces, team sports, religious venues, various in-person and virtual gaming contexts, and so forth.
This thematic cluster explores the nature and dynamics of learning at the level of how we experience various learning processes. In addition, we will explore learning as a major characteristic of complex living systems. As we may have noticed, there are lots of traps we can fall into whenever we start trying to layout some sort of “understanding” of our world, including ourselves or any other organism. In fact, people seem to be quite good about setting their own traps and falling into them.
Some of the traps we’ve seen previously in this chapter include those that are created when we try to establish some “truth,” some “way of seeing,” or some “way of thinking about and doing things.” These traps occur everywhere, from spiritual practices, such as those of Buddhism, Hinduism, or any of the western religions, to the whole array of human “disciplines” of creating knowledge, such as biology, physics, anthropology, psychology, the visual and dramatic arts, poetry, engineering, and so forth, as well as throughout our everyday lives. Such traps are problematic as well as a source of great potential for insight and creativity. The traps are most problematic when we end up in a cage of our own making and not realize we’re in a cage. These traps are most valuable, when we fall into them, then realize their repercussions and “trappiness.” So, as we go through this thematic cluster, we need to keep the array of possible traps in mind. Although I will try to avoid the traps of which I try to be aware, I very well may stumble into traps that are seemingly unbeknownst to me at the time.
Before bringing this article to an end, I have one caveat to mention. Our use of words can be problematic, which has been mentioned a couple of times earlier in this chapter. For the most part, people speaking in any particular language share a certain degree of meaning for the words they use. If we didn’t do that, it would be almost impossible to communicate. The challenge is that people not only have their own individual meanings and connotations for words, but also have their own idiosyncratic rules, patterns, and habits of language use. Even though people may be talking in the same language (e.g., English, German, Italian, Navajo, etc.), they may not always be in implicit agreement as to the meanings and connotations of any particular statement. The conventions of language use may not be entirely clear to the people engaged in a conversation.[8] However, to varying degrees, each person has highly personalized meanings for the words they use. And certain words, such as those listed in the discussion of how positivism, reductionism, and mechanism, influence the meanings of common words used in educational contexts. Our paradigms, worldviews, beliefs, personal experiences, social and cultural contexts, and emotions affect our personalized meanings. For example, my wife never seems to catch my personalized meanings and intentions when I say some particular object or event is “weird.” Although we may agree that weird may refer to something “strange,” my personal usage of “weird” invokes a wider sense of bizarre-ness as well as perplexity or something not making any sense. I suspect my wife’s meaning of “weird” overlaps, but differs significantly from my own.
As we explore a variety of aspects of cognition and learning, please keep in mind the issue of variations in meaning. I will attempt to clarify such words as they are introduced, but trying to address all such meanings would require far more space than is warranted.
End Notes
[1] Most notably from the work of Max Planck, Niels Bohr, Erwin Schrödinger, Richard Feynman, Werner Heisenberg (Uncertainty Principle), and John von Neumann, among many others.
[2] Cybernetics congealed during the first Macy Conference in 1946 and going through to 1953, with the involvement of such people as Gregory Bateson, Margaret Mead, Heinz von Foerster, Norbert Wiener, Barry Commoner, Warren McCulloch, G. Evelyn Hutchinson, and John von Neumann, among many others.
[3] See the Stanford Encyclopedia of Philosophy
[4] The extent of literature is so vast that to list all the references in the past twenty years will take an entire book. However, a small sampling is provided here. • Ben Jacob, E., Shapira, Y., & Tauber, A. I. (2006).• Güntürkün, O., & Bugnyar, T. (2016).• Morell, V. (2013). • Lieff, J. (2017, January 29). • Wohlleben, P. (2015).
[5] Bateson, N. (2016). • Bateson, N. (2017, May 18).
[6] Cárdenas-García, J. F., & Ireland, T. (2017). • Michaelian, K., & Sutton, J. (2013). • Zhang, J., & Patel, V. L. (2006).
[7] Bateson, G. (1979/2002). • Cheng, K. (2018). • Orman, J. (2016).
[8] Gregory Bateson delved into this issue in some depth in one of his talks at the Macy Conference in 1952: Bateson, G. (1952/2003/2016).
References
Bateson, G. (1979/2002). Mind and nature: A necessary unity. Cresskill, NJ: Hampton Press.
Bateson, G. (1952/2003/2016). The position of humor in human communication. In D. Pias (Ed.), Cybernetics: The Macy Conferences 1946—1953 — Transactions (pp. 541-574). Zurich-Berlin: Diaphanes.
Bateson, N. (2016). Small arcs of larger circles: Framing through other patterns. Axminster, UK: Triarchy Press.
Bateeson, N. (2017, May 18). Warm data: Contextual research and new forms of information. HackerNoon, 17.
Ben Jacob, E., Shapira, Y., & Tauber, A. I. (2006). Seeking the foundations of cognition in bacteria: From Schrödinger’s negative entropy to latent information. Physica A: Statistical Mechanics and Its Applications, 359, 495–524.
Cárdenas-García, J. F., & Ireland, T. (2017). Human distributed cognition from an organism-in-its-environment perspective. Biosemiotics, 10(2), 265–278.
Glymour, C., & Kelly, K. (1992). “Thoroughly modern Meno”, In J. Earman (ed), Inference, explanation and other frustrations. Berkeley, CA: University of California Press.
Cheng, K. (2018). Cognition beyond representation: Varieties of situated cognition in animals. Comparative Cognition & Behavior Reviews, 13, 1–20.
Güntürkün, O., & Bugnyar, T. (2016). Cognition without cortex. Trends in cognitive sciences, 20(4), 291–303.
Kelly, K. (1996). The logic of reliable inquiry, Oxford, UK: Oxford University Press.
Lieff, J. (2017, January 29). The first virus communication signals [Professional Blog]. Jon Lieff, MD.
Michaelian, K., & Sutton, J. (2013). Distributed cognition and memory research: History and current directions. Review of Philosophy and Psychology, 4(1), 1–24.
Morell, V. (2013). Animal wise: The thoughts and emotions of our fellow creatures. New York: Crown.
Orman, J. (2016). Distributing mind, cognition and language: Exploring the (un)common ground with integrational linguistics. Language and Cognition, 8(1), 142–166.
Schulte, O. (1999). Means-ends epistemology. The British Journal for the Philosophy of Science, 50: 1–31.
Wohlleben, P. (2015). The hidden life of trees: What they feel, How they communicate. Berkeley, CA: Greystone Books.
Zhang, J., & Patel, V. L. (2006). Distributed cognition, representation, and affordance. Pragmatics Cognition, 14(2), 333–341.
© 2024 by Jeffrey W. Bloom
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