“Intelligence is what you use when you don’t know what to do.” This descriptive paraphrase by theoretical neurophysiologist, William Calvin, summarizes Jean Piaget’s emphasis on intelligence, as “the coping and groping ability needed when there is no right answer” (Calvin., 1996, p. 13; Piaget, 1952, pp. 395-407).
Differences in coping and groping abilities in the face of novelty (especially when there is no single or correct answer) provide a good description of the essence of Relational Thinking Styles (RTS). RTS is an operational theory about the innate processes underlying ordinary decision-making, identified by observing how someone deals with novelty and adapts to changing conditions. In other words, someone’s habitual RTS style is the sort of natural intelligence that the person uses when he or she doesn’t know what to do.
The RTS model (Davis, 1972) describes a fundamental type of natural intelligence--that of habitual inferencing patterns consistently applied to (1) making decisions of value (Discerning), (2) determining priorities (Goal-Setting), and (3) engaging methods for accomplishing goals (Problem-Solving). In essence, RTS is a pragmatic theory about how people habitually do things: how they decide what matters; how they decide what to do about what matters; and how they solve (or fail to solve) problems in the course of doing. Taken together, the thinking style that someone applies for these three functions of decision-making (Discerning, Goal-Setting, and Problem-Solving) comprises a habitual inferencing pattern. These patterns are engines of thought that drive purposeful activity.
Each of the five Relational Thinking Styles (Transient, Direct, Analytical, Relational, and Meta-relational) are content-free operational descriptors of how people think (especially in the face of novelty)—not what they think about. The first four of these styles resemble Justus Buchler’s descriptors of methods: 1) “hacking a path through the forest in a semiconscious daze” (Transient); 2) “applying an order or pattern” (Direct); 3) “devising an order” (Analytical); and 4) “creation or invention” (Relational) (Buchler, 1961, p.2). The fifth style, Meta-relational, deliberately applies, and/or combines each sort of operation as appropriate to developing or achieving a goal.
The Davis Non-Verbal Assessment (DNV), which is an analog model of RTS in the same sense as that described by Max Black (1962, p. 222)1, can accurately assess inferencing patterns without recourse to language. This non-linguistic approach is an advantage, not only because of inherent ambiguity problems with language-based assessments, but also because of the tendency of many individuals to manipulate verbal instruments or interpret meaning based upon how they feel in a given moment. The DNV controls for, and virtually eliminates this problem.
This book is an attempt to explain the theory of RTS and its analogue model the DNV, their significance, and their many applications. Since the inception of RTS over 40 years ago, a small cadre of collaborators comprised of teachers, counselors, philosophers, researchers, vocational rehabilitation therapists, risk managers, executives, and small business owners have been involved in exploring and testing its premises within their own lines of work. It is our hope that further researchers will be enticed to mount thoroughgoing validity studies of the DNV and to re-verify the excellent interrater and retest findings of the reliability and validity studies of the DNV at the University of Oregon (Chiasson, Malle, & Simmons, 2003).
Throughout this book, we variously refer to RTS as inferencing styles, thinking styles, decision styles, and reasoning styles. RTS styles are all of these. How they are referred to in a given instance depends upon the context being addressed. The term thinking styles is the most inclusive of the terms, since it refers to all types of thought, including thought that does not provide an outcome, such as aesthetic appreciation, daydreaming, listening to music, and so on. The most significant aspect of the RTS theoretical model, however, is that it can capture the formulation and execution of all sorts of purposeful thought, such as creating art, solving problems, making inferences, making decisions, producing miniature airplane models, fixing a car, reasoning, researching, and so on. The term, reasoning style specifically refers to the application of a particular set of inferencing patterns (which are purposeful applications of thinking styles) to solve a particular sort of problem. In the sense of RTS, a reasoning habit is the application of one’s thinking style to form a conclusion or judgment; the same holds true for inferencing and decision-making2. For, regardless of the purposes for which they are applied, the styles remain the same; except for a few exceptions3, a person makes decisions, creates art, fixes cars, cleans house, and reasons by means of a particular thinking pattern that seems to remain relatively constant over life.
RELATIONAL THINKING STYLES AND NATURAL INTELLIGENCE
Although the subject of natural intelligence is comprised of myriad factors, here we address only one, albeit a fundamental one, which serves as the engine of purposeful thought. We contend that the inferencing patterns that comprise RTS are fundamental to natural intelligence, though many other factors are important as well4.
Early split-brain studies, for which Roger Sperry won the Nobel Prize in 1981, began a process of identifying subtleties in neural processing. “Scientists now know that the brain runs largely on autopilot; it acts first and asks questions later, often explaining behavior after the fact,” reports New York Times writer, Benedict Carey (2001) writing about the work of Michael Gazzaniga, once a student of Sperry, who has continued his research.
RTS deals with such automatic processes, with unintended inferences that relate to overarching teleological processes of natural intelligence. Teleology is the study of purposes and goals: both the apprehension of goals and the achieving of them as well. Regarding teleology and the need for a unifying doctrine of goal-directed methods, philosopher C. S. Peirce contends that there is a great need to develop “a general doctrine of the nature of teleological action in general” (Peirce, 1932, Vol. 2, para. 108). We believe this need can be answered by RTS, which is a practical expression of Peirce’s formal concept of a method for discovering methods for goal acquisition, which he terms “methodeutic” (Peirce, 1932, Vol. 1, para. 191). RTS and the DNV provide a practical method for examining and testing Peirce’s concept of methodeutic (Peirce, 1932, Vol. 2, para. 105) and some of his thoughts on what he describes as methods of “Fixation of Belief” (Peirce, 1935, Vol. 5, para. 377). Specifically, RTS delineates four different ways of apprehending and achieving goals (Transient, Direct, Analytical, and Relational), including one abductive-like process (Relational thinking).
In addition, an understanding of the characteristics of these different inferencing styles suggests insights into why some people unjustifiably think they are experts while others underestimate their own knowledge or abilities (Goode, 2000; Kruger & Dunning, 1999). RTS also provides insights into and potential solutions to the Peter Principle (Peter & Hull, 1969), the problem of people being promoted beyond their range of competence.
Thus, RTS is a verifiable model of the tacit inferencing habits that are engaged for all sorts of purposeful activities. These vary from person to person and, although each pattern can be objectively observed and clearly demonstrated, almost everyone is unable to control his or her particular inferencing style in media res; these are automatic and instinctive operations of thought. Different people habitually apply a particular combination (pattern) of these methods when engaged in a goal-directed or goal development process. Except for a very small number of people, these methodic patterns do not change when contexts and goals change.
THE STUDY OF NATURAL INTELLIGENCE
Although the subject of this book is the RTS model of habitual inferencing patterns, many other approaches to understanding natural intelligence are under way. The most general way of thinking about natural intelligence from a computational perspective is to place it in opposition to Artificial Intelligence (AI). For artificial intelligence products to become more natural, the complicated and complex workings of ordinary minds need to be explored and understood (Bath, 2008).
Specific areas of research drive a broadening understanding of the ways for defining natural intelligence. For example, psychotherapist Aposhyan (2007) defines natural intelligence as sensory states of body awareness; inspirational speaker and writer Markova (1996) defines it as six patterns for learning based upon sensory states (visual, auditory, kinesthetic, and so on). Social biologists, Benzon and Hays (1988) propose five sequential principles of natural intelligence: feeling, coherence, action, finitization, and analysis. Computer scientist, Myrna Estep (2006) proposes what seems to be a Peircean-like phenomenological take on natural intelligence, positing three aspects of natural intelligence that correspond to Peirce’s phenomenological categories: (1) non-verbal immediate awareness, (2) sensory and somatosensory-motor processes (knowing how), and (3) verbal skills.
Some AI researchers continue to inquire into the topic of counterfactuals, explored in depth by the late philosopher David Lewis (1973), which deal with aspects of modal and hypothetical reasoning having to do with matters as diverse as many worlds theory, creativity, and worry (Lewis, 1973; Sêaghdha, 2003). This area of study relates to elements of Relational Thinking Styles having to do with non-verbal methods of goal (or hypothesis) development.
Developmental psychologist Howard Gardner offers a concept of multiple intelligences as a counter to the emphasis placed upon IQ testing as the sole measure of intelligence (1983). His theory allows abilities in art, music, social skills, and many other sorts of intelligences, to have equal (and sometimes greater) weight than intelligence factors from the limited range of abilities identified by IQ tests. Psychologist and writer Daniel Goleman claims that emotional intelligence matters more than IQ when it comes to success in life (1995)—though success in this case is defined only in social/emotional terms. (This idea does not explain the obvious successes of certain socially inept and emotionally unintelligent individuals—including at least one American billionaire and individuals with high-functioning Asperger syndrome.)
Those who adhere to the Jungian typology of temperaments—made popular by the flawed, but ubiquitous, Myers-Briggs Temperament Indicator (Briggs & Myers, 1962)—identify the combination and relative intensity of pairs of attitudes (i.e., introversion vs. extraversion) and personality functions (i.e., thinking vs. feeling, sensing vs. intuiting) as fundamental aspects of the thinking styles underlying natural intelligence. However, we contend that although the temperamental aspects of personality are important (and possibly hard-wired) traits that affect personality, life choices, and relationships within and among individuals, they do not affect the functions of thinking and reasoning per se5.
Other researchers contend that language is the mechanism by which higher order thinking occurs, a claim with which we heartily disagree. These researchers include Jennings’s claim in Language, Logic and the Brain (Jennings, 2008, p. 118) and Bermúdez in Thinking without words (Bermúdez, 2003, p. 111), both of whom propose that reasoning capabilities are necessarily centered in linguistic processes.
Physicists Ben-Jacob and Shapira (2005) distinguish between meaning-based natural intelligence and information-based artificial intelligence, contending that the natural intelligence functions of living organisms cannot be replicated by machine functions. These writers make a clear case for this assertion, one that may turn out to be true. However, although computational scientists may never entirely model every aspect of intelligence, we contend that there are so many aspects of natural intelligence yet to be explored and understood that we can only accept an assertion such as theirs as a research challenge for someone (or many someones) to prove otherwise. RTS may provide one way of challenging this assertion, because it is not a meaning-based model; rather, it enables the production of meaning, without resorting to semantics and linguistics.
Another, albeit language-based challenge to the information-only assertion for AI is IBM’s new natural language machine WATSON, which answers non-linear questions, even those posed as puns, as if it possessed a sort of natural intelligence (Jackson, 2011). Though the remarkable program is comprised of a vast array of information and myriad linguistic rules for processing that information, its usefulness seems thus far to be mostly limited to the sort of information-based AI described by Ben-Jacob and Shapira (2005). However, the appearance that a meaning-based intelligence is driving WATSON, leaves us with the impression that we are not too far away from a machine intelligence (perhaps even an improved version of WATSON) passing Turing’s test for an intelligent machine (Ben-Jacob & Shapira, 2005, p. 2).
AI researchers are also seeking ways of understanding and representing spatial, temporal or sensory processes related to intelligence (Dong, 2008, pp. 142). George Armitage Miller, the pioneering cognitive researcher and linguist who oversaw development of WorldNet (a precursor to modern internet search engines), contends that both temporal and spatial issues affect computer programs (plans) in pervasive ways (Miller, Galanter, & Pribram, 1960, p. 101). Klopf and Morgan, researchers in the field of modeling neural networks, also suggest that temporal issues are primary factors of natural intelligence, contending that “real-time considerations may be fundamental to natural intelligence” (Klopf & Morgan, 1989, p. 97). Some AI researchers focus upon a particular aspect of these temporal and spatial processes, such as Dong, who specializes in cognitive ergonomic systems and proposes a commonsense approach for representing knowledge of spatial environments (Dong, 2008), and López, Núnez, and Pelayo, who propose a method for formally specifying the process of memorization (López, Núnez, & Pelayo, 2008). Although none of the above seems to relate to RTS, the exploration of each of these contentions may have important contributions for computational sciences. Ultimately, many seemingly diverse and unrelated perspectives may come together, synthesize, diverge, and re-synthesize in the exploration of this relatively new science.
Finally, an emerging field of social psychology has recently identified the phenomena of unintended inferences and their consequences (Hassin, Uleman, & Bargh, 2005; Uleman & Bargh, 1989). Social science researchers are seeking to uncover the source and operation of ordinary, everyday decision-making (Hassin, Uleman, & Bargh, 2005, p. 392). These sorts of inferences relate to very short term, almost immediate, decisions—having to do, for example, with how somatosensory factors (such as first holding a hot or cold cup of liquid) influences how a person fills out a questionnaire, or decides to eat more or less of available food items, or selects one sort of food rather than another.
None of these interpretations of natural intelligence is necessarily incorrect; most do not exclude one another—yet none is complete. Nor does RTS provide the complete answer to the question of what comprises natural intelligence. However, it does suggest that the inferencing patterns described and demonstrated by the RTS theoretical model may provide a platform from which to examine and integrate various aspects of natural intelligence, and may provide ways in which these aspects might be expressed more effectively in computational models of human systems within various contexts. Unlike any of the previously mentioned approaches to the study of natural intelligence, RTS identifies non-verbal inferencing patterns that resemble formal inferences but which (in contrast to the deliberate and measured application of formal inferencing) are automatic and engine-like functions for placing thoughts and feelings into action for a purpose.
HOW THIS BOOK IS ORGANIZED
Chapter 1: Definitions of Terms
This chapter provides information about the philosophical ideas and associated terminology that underlie RTS and briefly explains how these relate to computational modeling and to the DNV, all of which comprise much more than a list of definitions. This chapter also provides a rationale for the importance of understanding terms in their context. The objectives of this chapter are to introduce readers to the philosophical underpinnings of RTS and provide them with the background and vocabulary to understand the rest of the book.
Chapter 2: Why Thinking Style Matters
This chapter introduces the ongoing paradox of the Peter Principle, which states, “In a hierarchy every employee tends to rise to his level of incompetence” (Peter & Hull, 1969). It proposes RTS as a solution to this problem.
RTS is a human systems theory, a model of innate human operational processes that interact with one another, with materials and within contexts to affect the functioning of whole systems, regardless of their size or importance. RTS describes the logica utens of thought—the untrained, instinctive methods by which different individuals habitually make decisions about matters of various degrees of importance. These differing tacit methods—described by Peirce (1935, Vol. 5, para. 377), Buchler (1961), and by RTS (Davis, 1972)—are of the nature of automatic, instinct-like habits based upon template-like beliefs and operational habits.
No particular method of instinctive thinking is inherently superior to another; no method has much meaning until the issue of context is considered. Thinking habit-patterns are more or less effective depending on the context within which they are used.
Here lies the crux of (and the solution for) the Peter Principle: Which inferencing pattern is most appropriate for a given situation is dictated by the requirements of that context. The DNV can identify the nearly two-thirds of individuals who lack the capability for effectively adapting to changing conditions (Chiasson, et al., 2003). These individuals, when promoted to positions requiring that they be able to recognize upcoming dangers or opportunities, adapt to change, or deal with novelty, continue to apply what they already know how to do. At this point, they have reached their level of incompetence. Thus, the DNV, by determining a person’s thinking style based upon the RTS model, provides a practical method for impeding the relentless march of the Peter Principle in any organization.
Chapter 3: Structure of the RTS Model
Though inference patterns are a nearly invisible aspect of natural intelligence, each of these RTS inferencing patterns can be observed by watching patterns of behavior in standardized testing situations. Identifying a typology of habitual inference styles starts from the assumption of non-identity—thinking habits are not identical and suggests further that one style—at the level style is identified—may not simply learn another. Rather the style is a character that both limits and expands possibilities.
This chapter lays out the initial RTS model designed by Dorothy Davis to describe her theory of thinking styles (Davis, 1972). Included here is a brief discussion of the Peircean-like phenomenological model that Davis used as the framework from which to develop the theoretical model of RTS and the DNV, an analog model of RTS. (Peirce’s phenomenology is the subject of Chapter 9) The main body of the chapter introduces the underlying structure of the RTS model (and, by association, the DNV), laying out the basic criteria for analysis and identification of inference patterns.
Chapter 4: Assessing Inference Patterns
Though inference patterns are a nearly invisible aspect of natural intelligence, each of these RTS inferencing patterns can be observed by watching patterns of behavior in standardized testing situations. Our studies (Chiasson, et al., 2002) and thirty-five years of field-testing suggest that these habitual inference patterns constrain how individuals deal with issues of value, goals, and the production of outcomes.
In this chapter, we address the underlying form and structure of the assessment task, the purpose for each aspect of the assessment, as well as specific data and explanations regarding the DNV process. Included in this chapter are rationales for each factor of the assessment process, a diagram of the table set-up for testing, and several items that are observed during testing. This chapter also addresses some of the relationships between observed items and the performance implications of these. The assessment itself is explained in terms of Davis’s category items of sequence, intensity, and duration, as well as the action components (varying and repeating) and the patterns of actions that these take on for each style.
Chapter 5: Basics of Inferencing Algorithms
This chapter addresses some of the specific patterns of RTS relevant to the construction of the algorithms for analyzing inferencing patterns. These will include the relationship between the items on the DNV observation sheet and the categories of sequence, intensity, and duration; subroutines for determining frequency/significance of these items to award numerical values (points) to particular inferencing patterns; and a sampling of formulae that identify action patterns derived from the expression of particular items.
The algorithms used to identify thinking style patterns are derived from Davis’s theoretical construct of RTS; as such, we can demonstrate that they are capturing predicable patterns of human behavior. These patterns are based upon the priority and combination of ways in which individuals of a particular style habitually engage the order and direction of thoughts (sequence), confront options (intensity), and use time (duration). These categories are engaged by means of action patterns comprised of simple or complex repeating, and random or deliberate varying.
Theoretical and formal logic does not (and probably ought not) consider the human systems that underlie the making of everyday decisions of value, purpose, and method. However, understanding how differing individuals make these decisions is vital for understanding overarching inferencing habits—which are much more powerful than inferences described by purely theoretical logic, because they are the habitual ways people make decisions that affect the operations of human systems for good or not.
Chapter 6: Variables for Computational Modeling of RTS
This chapter discusses the interrelationships between the three main variables that may provide extra insights into the ways and purposes for which thinking styles might be addressed in computational modeling.
Successful modeling of inference patterns often addresses these three primary variables: (1) the inferencing styles for each of the three stages (Discerning, Goal-Setting, and Problem-Solving), (2) delineation of each of the three functions according to the components of each, and (3) generic contextual analyses. Although inferencing styles (1) can be run independently of delineation of function and contextual analyses (2 and 3), they cannot have applicable meaning without knowing to which stage they apply (2). Although the styles and function analyses (1 and 2) can be run separately from contextual analyses (3), the algorithms and the stages to which they apply cannot be meaningfully modeled without generic contextual analyses. Other variables (such as temperament, skills, intelligence, and so on.) may be required, depending upon the purpose of a particular application.
Chapter 7: Computationally Modeling Inference Patterns
The inferencing styles identified by RTS are standardized and capable of being computationally modeled. The past thirty-five years of field-testing the DNV instrument indicate that styles and the effects of their interactions within contexts, with certain types of goals, and with other styles can not only be observed and codified, but also used to predict outcomes over the long term.
This chapter proposes two different perspectives from which to computationally model RTS, although there are many other modeling possibilities besides these that may be developed in the future.
The first and relatively straightforward perspective addresses modeling thinking styles in light of implications for future performance. This interactive model could address any number of variables, though the three variables we include here correspond to those discussed in Chapter 6. Applications for a computational model such as this one includes enabling better hiring and promotion decisions, as well as determining in advance, how a particular task group or management team is most likely to operate6. This type of computational model could also provide tools for cultural anthropologists, such as a method for identifying and analyzing the individual thinking style patterns of all members of a given group, in order to study the relationships between thinking styles as they relate to the hierarchy and interactions within that group.
The second perspective, which would be much more complex than the first, involves the shaping of algorithms that could computationally define RTS operational processes, including that of the abductive-like Relational thinking style. This second approach could potentially provide a means for enhancing various game theories, as well as contribute to the eventual development of an abductive inference engine.
By creating and improving computational tools that model these processes, such as Davis’ Non-Verbal Assessment, we might be eventually able to identify and head off the consequences of a series of poor choices that ultimately result in an individual or organizational failure.
Chapter 8: Implications and Applications of RTS
This chapter reviews ongoing and possible practical applications for RTS and the DNV. Over the past thirty-five years, the DNV standardized assessment has been applied in an attempt to benefit many different fields and business applications including education, social and counseling services, criminal justice, risk management, hiring and succession planning. Other applications are possible such as the identification of specific characters of creative processes and the consequences of recognizing them as such. In addition, RTS provides a platform for further study of the relationships between inference styles and temperament, and the correspondence of these patterns to Peirce’s methodeutic, which resemble elements identified by the RTS model. Action patterns from the DNV can be observed in real-time standardized testing situations and can then be used to make predictions based on the effects of the interactions of elements that indicate a particular style and the degree of success an individual with that style will have in a given context.
Although other researchers have investigated the topic of thinking styles (Briggs & Myers, 1962; de Bono, 1985; Sternberg, et al., 2000; Cokely & Kelley, 2009) none seems to be addressing thinking as comprised of inferencing patterns, or conceiving of thinking styles in this Peircean sense as having a mirror-like relationship to formal inferences as does the RTS model and its analog, the DNV. Certainly, no other research has thus far addressed the inherent non-linguistic nature of inferencing. In fact, influential authors in many fields tend to deny the possibility of non-linguistic inference.
Because the DNV assessment functions as a standard by which observations are made objective during test situations (in the same way an observation of temperature is made possible by the thermometer) the DNV has predictive capabilities (Tristan, 2002). Davis’s theory thus provides a testable model of instinctive reasoning/inferencing processes. Formal reliability and discriminate validity studies designed to test the DNV performed at the University of Oregon in 2002-2003 demonstrated high inter-rater reliability and good retest reliability, as well as a strong relationship between DNV Discerning and Goal-setting style and the Need for Cognition Scale (Cacioppo & Petty, 1982; Chiasson, et al., 2003).
It is important to recognize, however, that traditional validation methods of psychology and social psychology do not seem appropriate for operational models of cognition that have to do with non-verbal inferential processes, such as RTS. On the other hand, analysis and computer modeling techniques—such as those used for the ecological sciences (Ford, 2000)—offer promising possibilities for developing methods for testing the predictive usefulness of RTS. Neuro-imaging techniques are also promising, which may be able concurrently capture the mental activity by which the signatures of different styles of thinking may be eventually linked as one goes about making decisions of value, purpose, and method.
The 2002-2003 study at the University of Oregon suggests that distinct capabilities such as the capability to learn imaginatively from experience appear to comprise only a third of the population (Chiasson, et al., 2003). The most controversial claim, then, here suggested is that we do not come equipped with identical thinking styles. The different thinking styles can be discerned, and tests designed to identify individual thinking styles, within specific standardized contexts.
It is our contention that each thinking style has a different set of needs for learning, for successful job performance, and for effective parenting. The view advanced here suggests that a fruitful area of study may include how to teach children whose styles differ from one’s own. We hope to have made clear how fundamental and how various are the styles in which persons habitually make inferences.
This chapter discusses implications as well as previous and future applications of RTS and the DNV, particularly in reference to Direct thinking, the most pervasive instinctive inferencing pattern of RTS. In addition to the social impact of having a majority of Direct thinkers, we discuss actual and potential applications of RTS and the DNV, including the first applications of the DNV in education, social services, and business settings.
Chapter 9: The Qualitative Core of Reasoning
This chapter describes Peirce’s phenomenology and its connection to abduction, Peirce’s logic of discovery. RTS is derived from the phenomenological categories of Peirce. Abduction also clearly flows from phenomenology (Peirce, 1935, Vol. 5, para. 181) and a process similar to Peirce’s descriptions of this phenomenological sort of proto-abduction can be demonstrated and observed by means of the DNV. Noticing, or failing to notice, similarities and differences among things resides at the core of reasoning; all similarities and differences are discerned based upon the qualities of things, for there is no possibility of discernment without qualities to discern among. A mind cannot think about what it does not notice or has not previously noticed. Individuals become aware of similarities between things and ideas based upon the qualities, or properties, that distinguish one thing from another.
Each of three types of qualities represents a category in Peirce’s phenomenology: (1) Quality (or Value), (2) Reaction, and (3) Mediation (or Relationship) (Peirce, Vol. 1, para. 378). Thus, from a Peircean perspective, qualities are of three sorts: (1) the immediate feeling-based qualities of affect (such as impulses, emotions and attitudes—including those of the lowest and highest order feelings, such as rage and awe), (2) the reactive qualities of sensation (having to do with the five-plus senses), and (3) logical qualities (those that enable the making of rational connections).
In addition to these three kinds of qualities, Peirce’s concept of phenomenology as a whole comprises the qualitative core of reasoning. Since these three “universal categories” (Peirce, 1932, Vol. 1, para. 280) underlie the structure of Peirce’s philosophy as a whole, they underlie his logic as well. In particular, these phenomenological categories are essential for the justification for his concept of abduction and, therefore, Peirce’s full logical construct (Peirce, 1998, para. 375).
Chapter 10: Peirce’s Normative Sciences
This chapter is about Peirce’s second classification of philosophy, following phenomenology, a group of philosophical disciplines that he terms normative science (Peirce, 1932, Vol. 1, para. 281). By normative, Peirce means that each philosophical branch and sub-branch within this group is subject to particular norms, or standards, for performance.
There are three disciplines within the normative sciences: aesthetics, ethics, and logic. The discussion of logic will include a brief treatment of its three sub-groups: (1) semiotic or speculative grammar (the study of signs and their meanings), (2) logic proper (traditional logic), and (3) methodeutic “which studies the methods that ought to be pursued in the investigation, in the exposition, and in the application of truth” (Peirce, 1932, Vol. 1, para. 191). Each classification, category, and sub-category acquires fundamental principles from each and all of those that precede it.
The purpose of this chapter is to establish the basis for identifying the etiology of the abductive reasoning process by first addressing the relationship between Peirce’s concept of phenomenology and aesthetics, the first of his normative sciences. The connection between aesthetics and abductive inference will be drawn out, leading, consequently, to a discussion of the concept of methodeutic, the third sub-branch of logic from which the testing and proof of abduction needs to be undergone, and suggesting that the RTS model may provide a means for this testing.
Chapter 11: The Dilemma of Defining Abduction
This chapter and the next are devoted to Peirce’s concept of abduction. Although Peirce states that abduction is the “only logical operation which introduces any new idea” (Peirce, 1935, Vol. 5, para. 171), many, if not most, explanatory hypotheses offer nothing new at all. They do not seem to be, in the sense Peirce means, abductively derived. In various writings, Peirce provides at least four different descriptions of abduction. Italian computational philosopher Lorenzo Magnani (2001) proposes three types of abduction: theoretical, model-based, and creative (the final chapter discusses this third type). Chiasson (2005) points out that Peirce uses two distinct and contradictory terms to signify these processes (abduction and retroduction). Peirce uses the terms abduction and retroduction interchangeably as names for a distinct form of logical inference, as well as for the method by which hypotheses are engendered (Chiasson, 2005). In the background portion of this chapter, we discuss an alternate way of addressing these two processes.
The purpose of this chapter is to clarify the various ways abduction has been defined. In addition to defining abduction as an aspect of retroduction, we discuss induction, with which abduction is often confounded. This discussion of induction includes the concepts diagnosis and inference to the best explanation, both of which can be achieved inductively (and deductively as well, though deduction will not be addressed here), as well as abductively.
Chapter 12: Creative Abduction
In this chapter we discuss creative abduction—a particular generative process that incites and nourishes (rather than implements) an incipient idea and which can lead to the creation of a unique outcome. From the perspective of creative abduction, a unique relationship between means and ends holds the clue for explicating the mystery of abduction.
In terms of the RTS model, the abductive-like process of Relational thinking does not necessarily engage the retroductive-like (Chiasson, 2005) recursive process of Meta-relational thinking. The former moves in an exploratory way outward from means towards potential ends (yet without a specific end in view) and the latter begins with the abductive acquisition of a hypothesis or goal and proceeds to explicate and verify (or disprove) its validity (Chiasson, 2005). There are indeed Meta-relational (retroductive-like) thinkers and, though rare, such thinkers7 (who are capable of operating within any of the style patterns for a given purpose) may choose a habitual thinking pattern from any style, unless some thing or idea emerges to sufficiently capture their attention long enough to engage in full-scale retroduction (Chiasson, 2005).
The focus of this chapter is the sort of abduction that is only achievable by means of the unique, generative process capable of producing original ideas. We discuss Kapitan’s (1997) theses as a framework for understanding Peirce’s theory of abduction. We then return to Peirce’s concepts of phenomenology and normative science to explore the relationships between these philosophical concepts (and that of mathematics) with the development of a verifiable model for Peirce’s concept of abduction as a logically verifiable inference form. We conclude with an in-depth description of Relational thinking, which includes algorithms (based upon Chiasson’s personal notational system) for three types of operations performed by Relational thinkers.
The RTS model and DNV assessment of thinking styles provide invaluable insights into the workings of the human mind—in particular the different ways that people habitually engage differing inferencing styles to make instinctive decisions of value and purpose, and apply methods (some effective, some grossly ineffective) to achieve those purposes. Although this model derives from Peirce’s theoretical construct, it is not a representation of his theory, as the latter demands deliberate and formal application of particular methods. RTS styles are automatically applied, sometimes effectively so, sometimes not. Some may even argue (perhaps, rightly) that these styles more closely approximate John Dewey’s concepts of aesthetic and anesthetic methods (Dewey, 1980, pp. 38-39) than they do Peirce’s logical forms. However, Peirce’s philosophical construct and its resulting interconnections with RTS are significant for understanding a variety of subjects—including Peirce’s general concept of phenomenology, his “Doctrine of Categories” (Peirce, 1932, Vol. 1, para. 280) and the particular “order of the march of suggestion…from experience…to an abductive inference” (Peirce, 1932, Vol. 2, para.755).
Peirce identifies scientific intelligence as “intelligence capable of learning by experience” (Peirce, 1932, Vol. 2, para. 227). Everyone learns by experience to some degree, if only by discovering that a hot iron will burn. Yet, in our thirty-five plus years of field studies and site-specific validity studies, only two instinctive thinking types identified by RTS, Analytical and Relational thinkers, have consistently demonstrated the innate capability of learning from mistakes (theirs or others), the remainder gain mastery from careful guidance and ongoing repetition of their successes.
Although there are many types of natural intelligence, this book is about identifying these invisible engines of decision-making. These RTS thinking styles identify conditions within which someone is most likely to succeed in the world (however one might define success), and the circumstances in which that person is nearly guaranteed failure. Making a correct match of a person’s style to context is vital; a style that is effective for one context may fail miserably in another. With this knowledge of thinking styles and performance contexts, individuals, parents, educators, managers, and those in the helping professions can make better choices for themselves and others.
The thinking/inferencing styles identified by RTS appear to be capable of being computationally modeled because of the way these thinking styles work (and the effects of their interactions with contexts, goals, and other styles). These styles can be observed, codified, and, in the field testing by the Davis-Nelson Company and others have accurately predicted performance over the long-term. However, a nearly invisible aspect of natural intelligence, each of the RTS inferencing patterns determines how individuals deal with issues of value, goals, and the production of outcomes. By creating computational tools that can model these processes, we may be able to identify and head off the consequences of a series of bad choices that ultimately result in an organization’s failure. We may be able to identify the potential implications of clueless ignorance of the meaning of signs relevant to future events. We may prevent blind dismissal of the sorts of minds that might be able to ameliorate or prevent problems stemming from the Peter Principle (Peter & Hull, 1969), in which less complex minds direct and manage the individuals who, in many—if not most—cases, ought to be in charge.
Relational Thinking Styles is a hypothesis with an analog method (the Davis Non-Verbal Assessment) for testing its validity. Although the assessment has been identified as reliable (Chiasson, et al., 2003) it has not yet undergone a satisfactory and thoroughgoing validity study8 by means of suitable methods for validating operational processes. (We have applied this assessment in a variety of fields over the past thirty-five years by using site-specific validations.) It is our hope that this book will inspire others to work with this information in a variety of ways, but also to develop computational modeling programs for identifying and assessing the ecology of human performance.
However, we will be content if this book does nothing more than make readers aware that each thinking style has a different set of needs for learning, for successful job performance, and for effectively parenting and teaching children whose styles differ from one’s own. We hope to have demonstrated the fundamental nature and impact of the way in which a person habitually 1) makes inferences about what matters for a given situation, 2) develops and plans for goals to place those values into action, and then 3) attempts to solve problems in the course of producing outcomes based upon those goals.
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