Beyond Physical and Social Determinants: A theoretical model of human behavior

Bruce Nelson, Harold Koenig,

Mounting evidence is making it apparent that cortisol rhythmicity exhibits nonlinear properties. This is immediately relevant to brain-mapping projects and, more importantly, may profoundly affect our overall understanding of human cooperative behavior. The relative importance of the emerging insights builds on an understanding that disruptions to cortisol rhythmicity underlie individuals’ response to stress, where efforts to restore stability at the cellular molecular level may involve activity which is highly structured and coordinated on massively different levels of scale, including individual and community behavior that increasingly displays a global reach. If we are correct in understanding that this comprehensive set of behavioral relationships depends on nonlinear properties as an organizing principle, then we have a powerful set of tools with which to creatively approach many important challenges facing the emergence of a global community. Chief among these is the need to dramatically improve human cooperation, where large gaps exist in our collective understanding of community behavior. Up to this point the implications for human cooperation arising from the discovery that cortisol rhythmicity displays nonlinear behavior have been unknown, and the mechanisms whereby nonlinear properties of these relationships impact behavior across levels of scale have not been identified. The following introduction explores the implications of this discovery. Specifically, we will explore how understanding nonlinear properties in the relationship between cortisol rhythmicity, other neurological activity, and the larger community (on successive levels of scale) can enable us to lay out a theoretical framework as well as to conceptualize specific tools with which we can first model and then design human systems that significantly improve cooperation on all levels.

The brain’s stress center (HPA axis and amygdala), which modulates a wide variety of sensory input, is attuned to changes in the body’s homeostasis in a manner something like changes in the trajectory of a missile can be monitored and guided by the operation of a gyroscope. Importantly, however, it is known that the influences stabilizing cortisol rhythmicity are not limited to local neurological or physiological functions. In an effort to restore stability the brain’s stress center has developed ability to also drive activity in other parts of the brain, where perceived changes to homeostasis trigger the brain to make auto-corrections that stabilize cortisol rhythmicity. In humans this process also includes engaging the frontal lobe (cognition), which then further initiates stabilizing activity on progressively larger levels of scale. This activity can range from impulsive reactions to reasoned responses, which appear able to influence the collective behavior of communities of all sizes, including geopolitical systems. Observing that the stress-response system of individuals is part of a more comprehensive dynamic—one that displays non-linear characteristics, then, cues us to look for multi-scalar architecture and system parameters that become engaged in promoting social system stability and which defines tolerance for stress across multiple levels of scale. This also includes the point at which individual cortisol rhythmicity becomes unstable and may become a destabilizing influence for others. As this theoretical model unfolds, new opportunities for understanding and promoting human cooperation are being developed can be envisioned.

This also means that in the process of discovery we are gaining an improved understanding of the influences that constrain cortisol rhythmicity into a healthy pattern and enable it to return to that pattern following stressful experiences or traumatic events. Some of these constraining influences arise in the stress center of our brain where production of the stress hormone, cortisol, is triggered and where much of the constraining regulatory activity operates. Many such regulatory influences arise from the operation of well-known physiological mechanisms that function on the cellular /molecular level. However, some that are very important exert their influence from other levels of scale, including other regions of the brain, itself, such as the amygdala and the prefrontal cortex, which register and interpret experience as safe or threatening and then signal the stress center of our brain to respond accordingly. Nonetheless, their function as system parameters for modulating the nonlinear processes of stress response has not been generally recognized although the implications for brain mapping are substantial.

Other levels of scale with significant activity that is known to modulate stress response includes the levels at which cooperative social behavior occurs, i.e., in community, national, and geo-political systems. The implication relative to these multi-scalar influences is that that they are acting as system parameters to constrain the potential for divergence from one cycle to the next in the pattern of cortisol rhythmicity, i.e., they produce a net effect that determines the Lyapunov characteristic exponent. This means that constraining influences on multiple levels of scale are acting in concert to minimize instability in the cortisol cycle, where instability can result in chaotic behavior. The Lyapunov characteristic exponent is a variable that corresponds with the measure of chaotic divergence in a nonlinear system. The human stress response system employs a layered array of constraining influences to keep the value of this variable small, hence, minimizing divergence in cortisol rhythmicity and increasing our relative* tolerance for stress. When these constraining influences are successful, people are more likely to be healthy and to engage in healthy behavior. When they are not, people are more likely to feel anxious, act impulsively or aggressively, become depressed, and suffer from poor health.

It is also important to observe that processes modulating the non-linear characteristics of stress response are bi-directional, e.g., individual stress response is destabilized by traumatic experience and/or chronic exposure to stressors, but this instability in individual stress response can also trigger a wider shared instability mirrored by the individual’s community. Communities, in turn, are also attuned to destabilizing influences and, hence, are likely to engage in adaptations that restore stability, when overstressed, and/or to reinforce structures that are considered to maintain stability. The success of efforts to manage stress levels for individuals in a population, then, can be intuitively perceived as a feedback measure for the effectiveness of the collective and cooperative efforts to promote stability. The point is that the feedback process operates in both directions—the stability of individuals’ cortisol rhythmicity can feed back cyclically to modulate community behavior and community behavior can feed back cyclically to modulate the cortisol rhythmicity of individuals.

The cyclical pattern of feedback influencing system behavior is important to recognize because each cycle represents a potential escalation or de-escalation of divergence from stability (movement toward or away from chaotic behavior). This is a key feature of nonlinear systems, which reflects a Lyapunov exponent value for any given point in time that, when considered in series, may be useful in modeling and identifying the trajectory of system behavior. For the multi-scalar influences affecting or affected by cortisol rhythmicity, the cyclical pattern of stabilizing/destabilizing influences is also bidirectional. That is to say, escalating and de-escalating influences can be exerted in either direction or both directions, with mutually reinforcing behavior occurring back-and-forth across different levels of scale. That is particularly important to understand because this reciprocal relationship suggests that mechanisms with non-linear properties are operating at each pole, the micro and the macro (keeping in mind, there are more than two levels of scale that significantly modulate stress response and thus there are more than two poles that we will ultimately need to take into account, e.g., in humans the individual’s cognitive activity becomes an integral third pole that bi-directionally moderates the other two).

Next, it is important to recognize that bidirectional cyclical escalation and de-escalation can also be stated in the language of nonlinear systems as follows: feedforward and feedback cycles are occurring in both directions, where feedforward results in increasing divergence for successive iterations and feedback in decreasing divergence, displaying larger or smaller Lyapunov exponents respectively. With that being said, we are ready to observe that feedforward behavior has the potential to display a significantly greater Lyapunov characteristic exponent in the direction of community adaptations than in the direction of adaptations affecting cortisol rhythmicity. For example, creating and improving social systems such as educational, political, and legal systems as well as law enforcement and military resources can be viewed as an ongoing feedforward response to prior community experiences that were destabilizing or threatened to be so, where the cortisol rhythmicity of individuals was disrupted, thus, triggering successive cycles of community adaptation. This observation would indicate that a larger range of variation may result from feedforward cycles that produce upward influences (larger scales) affecting the community level of scale than we see resulting from downward (smaller scales) influences, displayed in adaptations involving cellular/molecular parameters that affect cortisol rhythmicity.

In fact, there does not appear to be any long-term healthy adaptations resulting from feedforward behavior operating at the physiological level of stress response. The most commonly occurring adaptations that do occur there are: 1) increased sensitivity to stressors, e.g., stress reactivity reflected in anxiety/behavioral immobility, resulting from increases in cyclical cortisol production, and 2) decreased sensitivity to stressors, e.g., major depression, resulting from decreased cyclical production of—and/or sensitivity to—cortisol. However, these physiological adaptations, which would likely have been initiated with fight-or-flight stressors, afford only short-term or other limited benefits, and they are unhealthy as chronic adaptive states. Also, from a nonlinear perspective they appear to reflect phase transitions that some researchers suggest could be modeled as torus attractor states, which involve an adaptive transition to a quasi-stable state which can be on the verge of becoming chaotic. Yet, it seems unclear that system order parameters constraining cortisol rhythmicity commonly allow it to become noticeably chaotic, and if it does, the degree of divergence over successive iterations appears to remain small. The point being that the boundaries afforded by order parameters influencing the cellular molecular level of human stress response appear relatively constraining. This is likely due to the limited flexibility of genetic and epigenetic mechanisms that are involved as a key aspect of order parameters modulating cortisol rhythmicity on the level of physiological influences. Importantly, adaptation on other levels of scale are not similarly constrained.

On the contrary, stress-initiated feedforward cycles, affecting adaptive cooperation at the level of community behavior, appear able to drive substantial system change. Recognizing the processes driving these iterative cycles, then, becomes important because their operation points to the specific nonlinear mechanisms that operate in human social systems and which, in turn, can illuminate how humans elect to cooperate in effecting system change. Depending on the circumstances and our perspective such change may be regarded as either good or bad, however, the implication is that understanding the mechanisms may allow us to both anticipate and specifically prepare for changes that are likely to occur as a result of stress response. Likewise, we may learn to better avoid unwanted responses and intervene to lessen negative impact or reinforce the constructive benefits of cooperative behavior.

In particular, recognizing that a feedforward response is involved in shaping cooperative community behavior cues us to look for system parameters on the level of community cooperation that are responsible for promoting stability in community— where that stability then feeds back to stabilize and maintain healthy cortisol rhythmicity for individuals. Naturally, the system parameters which serve as determinants of behavior at the community level differ substantially from the system parameters that operate via physiological responses within individuals. Nonetheless, feedback/feedforward relationships, operating across these massive differences in scale, have evolved to promote and maintain stability for the stress response system of individuals. And, perturbations that destabilize individuals’ stress response trigger cooperative community behavior to engage in both reactive and proactive responses intended to defend and protect the stability of human social systems. So far as we know, science has not yet recognized the non-linear characteristics of the feedforward influences on community behavior; attempted to specifically identify the system parameters and related mechanisms which, therefore, must be operating on the community level; or mathematically described the dynamics of this reciprocal relationship for the purpose of computer modeling.

However, the possibility that a self-organizing influence is being exerted across the difference in scale from cortisol rhythmicity to community behavior is important to appreciate because it means that we may be able to design and implement specific strategies that take advantage of this relationship. Consequently, there is a need to model this process with the expectation that a successful model will also help us to specifically identify the system parameters operating in the macro arena—at the level of individuals engaged in their community—and then detail how those system parameters function as determinants of behavior, particularly collective and cooperative behavior. The opportunity to engage mathematical and computer modeling as a helpful resource is, fortunately, a key implication of recognizing that we are addressing nonlinear behavior. High-level modeling will allow us to better identify specific dynamics driving change in the bi-directional multi-scalar relationships of human social systems and, thus, aid our understanding of the system parameters that uniquely drive the complexity of human cooperative behavior. Some of these are intuitive, yet many of them remain counterintuitive with respect to what we might expect if humans were to display purely rational responses.

Additionally, once we come to recognize the nature of system parameters operating on the scale of social cooperation, we will also need to investigate how these parameters come to be physiologically integrated into neurological processes. We already know that this takes a relatively long time for humans, i.e., from birth to maturity, yet this process is likely responsible for the self-organizing effect that differentiates us from other animals on this planet. It is in this respect that humans appear to be unique. Humanity displays an incredibly complex array of cooperative behavior, and while rationality is seen to be crucial, rationality cannot account for the extent to which people routinely cooperate in society. For all other creatures on this planet the determinants of behavior that drive cooperation, which at points can be highly developed, are genetically programmed and result as the product of pheromone or similar chemical messaging, often complemented in higher-order animals by communication via audible, visual, and other sensory cues. Hence, even if not the same in humans as for other animals, we should not be surprised that determinants of behavior also exist for humans, though the operation of such behavioral determinants appears significantly more complex.

Also, we should expect the system parameters that largely determine human social and cooperative behavior to be more complex than for other animals because they must be acquired over the course of maturation—an approximately twenty-year period. The length of this period, in turn, is telling, it means that we need to identify how non-biological system parameters, i.e., extrinsic, become integrated into physiological processes where they are able to serve effectively as the social determinants of behavior for people living cooperatively in community. Extrinsic, here, implies that acquired rule sets become internalized to serve as determinants of behavior capable of modulating stress response, which also involves a highly developed ability to coordinate community behavior. It is reasonable to suspect that the brain’s capacity for manipulating symbols, including language, plays a key role in the integration process. However, little is known about the details, although we might also expect that discoveries in this arena will take us to heart of understanding human uniqueness. (Understanding how and why this internalization process sometimes turns out differently for individuals than desired can also be illuminated by the approach we are suggesting and may also involve valuable discoveries.) Perusing this theoretical model, thus, promises to allow humanity as a whole to significantly improve our understanding of the mechanisms and participation in the processes that underlie our capacity for cooperation on all levels.

As a corollary, we may also be able to better understand why different communities, each with a long history of internal stability, can often exhibit an unwillingness to cooperate with other communities and, on the contrary, often to actively oppose each other’s aims and efforts. Applying this theoretical model to such cases may allow us to explain why developing consensus between communities on what constitutes rational behavior can be difficult to achieve and how to improve. This will be particularly relevant if rationality, rather than being the explanation for human cooperation, actually proves to be subordinate to a more fundamental need to achieve and preserve stability in social systems, which then, ultimately promotes an appropriate range of stability in the stress response center of people’s brains. With that assumption we would expect to see that on those occasions where rationality appears to support stability, then rationality is happily engaged to support cooperation. However, when rationality is engaged to promote cooperation that is perceived as potentially destabilizing, then rationality will likely be unsuccessful. This means that we may come to understand that the prospects for cooperation in community may be ultimately gauged at the level of potential for disruption to individuals’ cortisol rhythmicity. To some extent this is intuitive, nonetheless, explicitly modeling the associated complexities and their implications could be expected to result in significant implications and ultimately improvements in educational, criminal justice, healthcare, political, and perhaps all human social systems.

Finally, while this introduction has focused on the stability of cortisol rhythmicity in the stress response center of the brain, which allows us to begin modeling the complexity of nonlinear relationships in human behavior, there is much more to the story than is being described here. One next step may be to explore science’s emerging understanding of the relationship between a healthy level of instability, on the one hand, and creativity, good mental health and healing, on the other. Additionally, the role of genetic and epigenetic influences as they are associated with stress response, while contributing to or detracting from the potential for human cooperative behavior, could be an important inquiry to pursue.