The so-called “hard sciences” have long utilized diagramatic and mathematical models. Whereas psychologists grandly refer to their theories as “models,” the kind of flow charts, circuit diagrams, and state matrices utilized by the “hard sciences” are never verbal theories – they carefully depict the functional processes of phenomena like weather and have proved immensely useful in advancing the understanding of highly complex phenomena. Towards gaining similar advantages, “hard science” type circuit diagrams of perception, cognition, and memory are offered here to nudge behavior scientists toward new techniques for dealing with the human mind’s vast diversity.
Although deferential citing continues of half century old classics like those of Tolman’s “A Cognition – Motivation Model (1952) and Schacter’s “The Interaction of Cognition and Physiologic Determinants of Emotional States” (1962), much fresh work has been done in the domain of human data processing. Important studies have been made of the interaction of affective systems with data processing systems. The effects of impulse, emotion, attitude, and mood as these affects interact with perception, cognition, and memory were advanced by Isen et al. (1978, 1984), Laird et al. (1982), Forgas et al. (1984), Buck (1985), Leventhal et al. (1987), Fiedler (1988), and Ortony (1988). These pioneering studies paved the way for recent contributions on the interaction of affect with perception, cognition, and memory by Dickman (1990), Forgas (1991), Valman (1991), Eysenck (1993), Martin et al. (1993), Lieberman et al. (2000), Mayer et al. (2000), Brauer et al. (2000), and Clore et al. (2000). And Mischel and Shoda’s paper entitled “A Cognitive-Affective System Theory of Personality” (1995) urged a more thorough exploration of the influence of affect on reasoning.
No one has yet attempted, though, to provide professionals working in the field with a set of “hard science” type analog models that could be used to simulate human data processing. In his paper, “Toward an Operable Simulation Model of Personality” (1977), Shirley suggested the existence (then as yet unpublished) of such “hard science” models. Then a few years later, the first analog model of the human cognitive systems appeared in his book, Mapping the Mind (1983). Little notice was paid this book since, for psychologists and other so-called “soft scientists,” the word model is used as a synonym for descriptive or analytic theories containing no diagrams or designs of the “hard science” type.
Throughout nearly the whole of the 20th century, research psychologists focused their efforts towards reducing the myriad states and variables of the mind down to a manageable few. Toward the end of 20th century the crowning achievement of this century of reductionism was achieved with the publication of the much praised Five Factor Model of Personality (Costa el al. 1992). In this “model,” the enormously complex domain of the human mind was reduced to ten opposing factors.
After a century, we as behavior scientists have just about exhausted our efforts to simplify the human mind. Many in our field are beginning to believe that instead of trying to oversimplify the mind, we should try to accommodate our studies and our theories to the vast array of variables, states, processes, and functions exhibited by the mind as it directs human behavior. Over the last few years a new tone is being heard. Daniel Goleman in his book Emotional Intelligence suggests “there are hundreds of emotions along with their blends, variations, mutations, and nuances” (1995, p.153). A new group of researchers are beginning to take central stage, behavior scientists ready to deal at last with the immense complexity of the human control system.
These behavior scientists are inclined to feel that we should follow the example of meteorologists. Having discovered long ago that they were never likely to isolate and identify all the many factors at play in shifting weather patterns, meteorologists construct highly complex models of weather patterns – then experiment with their designs in order to gain a better understanding of how and why weather changes occur. Why should psychologists not design highly complex analog models of perception, cognition, and memory – then manipulate them to learn more about the functioning characteristics of the human mind?
The answer: for most of last century the so-called “hard sciences” have irrefutably demonstrated that to replicate a complex system mathematically or by linear design offers scientists immense advantages in understanding how such a system works. If we could design such a model – no matter how arbitrarily we choose its components and their organization – and make it work like the original, then immense gains are made toward understanding that system.
The design models included in this paper are preliminary ones. Hopefully they will inspire (or provoke) behavior scientists with a more extensive background in cybernetics and systems design to produce more sophisticated replications of the mind’s data processing systems. But at least the brief introduction to system design provided here might stimulate behavior scientists to begin exploring the possibilities implicit in the utilization of “hard science” type models.
An overview type of flow chart of the mind first appeared in “Toward an Operable Model of Personality” (1977). This “hard science” type flow chart displays how the human data processing systems tend to interact with the three psychomotor drive system to produce volition and goal-directed behavior (see Figure 1).
This simple overview of the mind demonstrates – as “hard science” models tend to do – important relationships. When data containing warnings of danger or need for swift action are processed by perception, an immediate coupling with the impulse system is effected in order to prompt an instant spontaneous response (see line from impulse to behavior output).
When people have time to consider carefully decisions to be made, plans to be formulated, considered action to be carried out, then cognition interacts with emotion and memory to implement these functions (see the “decision-making triangle” of lines intersecting these three systems).
When people repeat customary long-ago memorized behavior sequences, such as driving an automobile, riding a bicycle, washing dishes, cleaning house, bathing, shaving, putting on make-up, catching a bus, making one’s way around town – then all of these memorized behaviors are mediated by a coupling of memory and attitude (see line descending from attitude to behavior output entitled “disciplined” behavior).
In addition to modelling some typical interactions of the major subsystems of the human control system, this simple flow chart of the mind also starkly reveals pathological difficulties that impose themselves when anyone centers his locus of control too extensively in one or the other of these subsystems. Although we in clinical psychology do not like to utilize terms drawn from typology, many of modern clinical diagnostic labels in psychology and psychiatry are actually disguised typologies (see “The Enduring Charm of Diagnostic Typology for Clinicians,” Shirley, in press).
Though we have not encorporated all of them into our diagnostic manuals, labels identifying certain personality types are customarily applied to people who fixate for inordinate time periods in one or another of the eight subsystems. See Figure 2 overleaf for a list of the typological labels customarily utilized.
“Hard science” type display models of perception, cognition, and memory will presently be advanced. Yet many of you will be enormously curious about the precise interactions that are posited between cognition and emotion. For those who are, a very precise, completely specified detailing of such variable interactions between systems is provided in Mapping the Mind (Shirley, 1983).
In that detailed study, hard science models of cognition and emotion were depicted containing parallel circuits. It was demonstrated that emotional input enormously affects the resulting thought sequences initiated by cognition. The parallel emotional circuit for the cognitive circuit producing prediction (see Figure 4) contains three powerful compound emotions: hope, disappointment, and indignation. As Shirley indicated in his paper “Emotion As a Three-Valued Variable” (1976), every emotion provides the organism the choice of three directional possibilities – approach, retreat, attack. Human decision making is powerfully affected by which of the three values is being signaled by the emotional system. A detailed study of how emotion interacts with cognition entitled “Utilizing Hard Science Type Models to Study Interactions Between Emotion and Cognition” (Shirley, in press).
Putting aside for the moment then the importance of affective interaction with the human data processing systems, let’s explore a set of totally specified, completely articulated models of perception, cognition, and memory.
Perception, cognition, and memory constitute the mind’s three data processing systems. Affective signals customarily called sensations are generated within perception as its sensors monitor its outer environment (the world) and its inner environment (conditions within the organism). Clusters of these sensations, customarily referred to as images, are passed on to cognition where they are processed and become what is termed as thoughts. To pursue this process a step further, images and thoughts are evaluated in cognition as to their significance to the organism, then stored in memory. A century of research has confirmed that memories are then coded for retrieval so that the original affective tone attached to their experience is evoked within the organism when the memory is retrieved. Memories appear also to be coded according to their apparent significance to the organism and to be linked by association with other memories that contain common elements.
In considering the visual aspects of perception, the simplest approach toward constructing a circuit diagram is to divide into four categories the functional aspects of what vision needs to do. Why four categories? Creating hard science models has always been an art of utilizing what is feasible. Three categories provide too few interactional possibilities for the variables to replicate perceptual processes – and five categories would produce the chaos of too many interactional possibilities for us to deal with.
So, proceeding toward utilizing four subsystems, we must ask ourselves what is it we perceive each time we utilize our visual capabilities? First we see objects around us or in the distance. Next we note their locations in relation to each other. Then we note any movement or location shift. Then last of all we try over a time period to note any changes that occur in our visual field: new objects appearing, different objects moving, shift of size or location of objects.
Utilizing slightly more formal terms, we may describe the perceptical field of vision as follows:
FORM – pattern perception: perception of contiguity of shape, contours, textures, and colors that seem to coalesce to make a pattern or gestalt (an organization that seems to fit together).
ORDER – relationship perception: perception of proximity or distances between forms and between forms and space; perception of similarities between different forms and spaces.
SUCCESSION – movement perception: perception of sequentiality, of succession, of serial continuity as forms move (or as the eye moves across forms, as when scanning a row of houses).
VARIATION – change perception: the perception of how forms and spaces vary from each other as their positions shift as we move or as they move or as they change in form; discrimination of differences between forms and space.
In attempting to design a circuit diagram of how these four components of the perceptual field might interact with each other, one set of possible interactions is represented in Figure 3. The lines between the subsystems list the putative interactional results as the subsystems couple with each other.
Of the three data processing systems of the human mind, the cognitive system is the most complex and most difficult to understand. Cognition has long been treated as synonymous with thought. Yet neither people nor animals have too much leisure to think purposeless thoughts. The cognitive system was evolved as the problem solving system of the mind.
To repeat a litany we all agree to, all animals organisms – man included – must deal with dangers, avoid injury, find food and shelter, build relationships, and cooperate with others of the same species in order to ensure species survival.
To solve a problem, the cognitive system must consider most of the factors involved in a problem and juggle them around so that a favourable outcome can be envisaged. But the human mind is more sophisticated than are the minds of most other animals. The human mind can do more than just juggle the factors in a problem, seeking to organize the factors so that a favourable solution to the problem is achieved. The human mind is capable of conjuring up other factors not involved in the problem which, when taken into account, may provide unexpectedly new and fresh solutions to any problem being considered, indeed might even solve a whole series of related problems.
The human cognitive system, faced with a problem, sinks down into abstraction, juggles and puts together all sorts of images that offer means to solve the problem. This juggling of images while in abstraction has been termed a destructuring of concepts. Images and ideas are often torn apart and reunited in bizarre and different ways until a new approach to solving the problem is discovered. Once new ideas or solutions are conceptualized, thought processes emerge from abstraction and return to concrete thinking, bringing up to the level of consciousness the new elements of thought. This process has been called a restructuring of concepts.
In designing a circuit diagram of cognition, it would again be advisable to utilize four categories as our four essential subsystems. Again the imperative applies that neural systems that evolved in parallel be modeled to replicate this evolutionary overlap. A clear and unmistakable relationship between perception, cognition, and memory should be imbedded in our model.
Pursuing the above design guidelines, we may then speculate, perhaps not ineptly, that as multicelled creatures evolve greater complexity they begin (without utilizing labels of course) to identify various forms in their environments. As they begin to perceive similarities and differences, organisms then begin to conceptualize them in classes. One group they class as predators, another group as food, another class as potential shelter. Then even fairly low on the phylogenetic scale perhaps, creatures with primitive cognitive systems begin to gain the capability to manipulate images, imagining a larger fish or beast dashing ferociously toward them. Nor is it unlikely, since environments around the world continue to vary and change, that a cognitive capability of discrimination develops fairly early in the evolutionary scale.
Identification, classification, imagination, and discrimination shall provide us our four essential cognitive processes. These then will be treated as subsystems and their potential coupling effects will be labeled on the lines between each. Labels characterizing some of the possible levels of abstraction each subsystem might perform are also found in the simplified circuit design of the cognitive system in Figure 4.
Each subsystem of the cognitive system performs this destructuring and restructuring of concepts. The ladders under each of the four subsystems of cognition are the depth levels of abstraction into which one can sink while attempting to solve problems. This process of sinking down into abstraction to find new approaches to solving a problem is usually referred to (by laypersons) as intuition. A list of possible results as destructuring and restructuring of concepts is carried forward are listed below the comprehensive circuit diagram of cognition in Figure 5.
After pulling out of the unconscious mind the potential solution to a problem, cognition then takes these elements and reasons through the problem to see if an adequate solution has been found. This is usually referred to as the use of logic in problem solving. The kinds of reasoning available to cognition are displayed on the lines linking the subsystems. The kinds of reasoning involving all four cognitive subsystems are summarized below the diagram of cognition.
Finally, the solution to a problem is tried out in real life to confirm or disconfirm the logical conclusions that were reached. This is called reality testing. A simple representation of how people reason – relating it to the present paradigm – appeared on pp. 323 – 4 of Mapping the Mind (Shirley, 1983) and is included here for your convenience.
The challenge implicit in attempting to design hard science type circuit diagrams of such highly complex systems as those we are attempting here involves the need to shape a replication of functions as they appear to work within the systems themselves. The critical choices in designing such models does not center on choosing the identical sets of processes and functions as actually are operating in these electrochemical systems within the mind. Critical choices focus rather on selecting variables, states, processes, and functions capable of adequately replicating the activities these systems perform as closely as our knowledge permits.
By designing models whose functions resemble the functions that the mind performs, we gain the availability of means to manipulate variables, test hypotheses, and shape experiments so that we might gain fresh insights into the mind’s functions.
Memory in the human mind appears to work much like cognition. Let’s discus how memory appears to function and then attempt to construct a model which will to some degree replicate these functions. Like cognition, memory generates no feeling tones (affective tone). Just as cognition must draw affective tone from feelings experienced via impulse, emotion, attitude, mood, and appetite systems to get a “gut check” on whatever is under consideration, memory must also do so. Memory stores sensorial experiences, reasoning sequences, and the feeling tones that accompanied them. The memory stores not only the images of events, that happen to people, but it faithfully stores all of the feelings that were elicited by the experience. So when someone recalls a painful event, the memory often elicits in a person nearly as much pain as the original originally experienced. This maintenance of painful memories, as much as we curse them when we are haunted by such memories, is an important survival mechanism: it serves as a warning signal to avoid such painful experiences in the future. Moreover, it seems highly likely that memory evolved as a spin off of perception and cognition. Hence memory is very likely to be characterized by parallel functions, corollaries to those of perception and cognition. Some of these overlaps and parallels we detect at a glance at Figure 6.
It might be appropriate here to examine a few of the parallels between cognition and memory that are finding their way into this effort to produce “hard science” type design models of the mind’s three data processing systems. Because the memory records both cognitive and feeling elements of any experience, it tends to code experience in a way that utilizes both concepts and feelings at the same time. Thus comparisons that have been made by cognition are remembered according to their elegance, or appropriateness. Calculations that have been made are remembered by the measure they have provided, or usefulness they have served. Verification is remembered according to relevance to whatever was involved in the verification. Predictions are remembered as estimates of probability to be applied in the future. Adduction (evidence gathering) is remembered as causation of whatever the evidence was related to. And generalization is remembered as principles arrived at through thought.
When we combine the causes of things with principles underlying the causes, we feel that we have discovered the truth about something. When we remember the relevance something had for us and estimate the probability of it occurring again, we have arrived at the meaning involved in that matter. And when we remember the elegance and measure involved in past experiences or considerations, we feel we have arrived at an understanding of the beauty or of the ugliness of this set of events. Thus, quite unexpectedly, this hard science type model of memory provides us operable definitions of humankind’s three most revered concepts.
TRUTH, MEANING, BEAUTY, the three most sought after sets of significances for everyone. Every person alive has his own interpretation as to what is true, what is beautiful, and what is meaningful. In primitive societies, these convictions are often communal and static over a lifetime. In scientific societies, we often frequently modify our convictions of what is true, beautiful, or meaningful.
Yet as enriching as memory can be, it can also be traumatizing. Indeed, memory can become the haunted chambers of the mind. This is due to the way memory stores pain – so that when a painful event is remembered, it is as fresh and as painful as when originally experienced! Psychotherapists often must perform hypnotherapy to drain out the pus from festering memories. When such relief is not gained through therapy, people who have experienced too much pain in life (and therefore have too many painful memories) often suppress or repress memories in order to avoid re-experiencing the pain stored in these memories (see bottom of diagram of memory field). Some people even experience amnesia toward whole large sets of memories.
Yet notwithstanding problems that can occur, memory, like cognition and perception, is an elegant system vastly useful and marvelously enriching to those who use it effectively, building and storing happy memories of an effective life well-lived.
“Hard science” type diagramatic models are not designed as sets of words or symbols carved on stone, immutable forever after. Analog models of complex machines or of complex natural phenomena are diagramatically designed to bring order out of chaos – to lessen the confusion induced by trying to deal with a vast array of variables by reducing them to a manageable array.
The designs introduced here can be inserted in our computers and called up and manipulated when we want to evaluate data or explore data processing functions with which we are not familiar.
Behavioral scientists can substitute their own labels for states or functions they feel are misnamed. It is obvious from the design of cognition in Figure 5, for example, that people who spend too much time at one or another of the levels of abstraction tend to induce in themselves the kinds of psychopathology listed in the model. Psychiatrists and clinical psychologists might want to insert names of cognitive pathologies omitted there, such as derealization, autism, etc. Indeed, possibilities exist on all sides for behavioral scientists to modify these models to suit their orientation and past research findings.
As the behavioral sciences move away from reductionism and finally begin to grapple with the mind’s vast array of states, processes, and functions, it seems almost certain that “hard science” type analog models will play an important role in 21st century psychological research.
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