Pictures can bring back vivid and specific memories, and the same can happen when listening to music – for that matter, any sensory input can trigger this. Spend enough years listening to heavy metal, and overlap those same years with motorcycle riding, and the odds of them converging increase. Certain riffs have now become time machines to relive specific motorcycling experiences.
Listening to the opening riffs of Opeth’s Bleak rewinds to that 2:00 AM lonely highway ride, to this small college town to see them live. Jazz-fusion-like bass lines in Beyond Creation’s Surface’s Echoes bring back late autumn rides and glimpses of Olympic Peninsula sunsets. Deafheaven’s Honeycomb recreates early summers spent exploring the backroads and forts around Port Townsend. Frankly, it’s not an exhaustive list; somehow, these experiences got overlaid in memory. One begets the other. But this is not intentional, and it doesn’t happen all the time.
Often, such recurring patterns motivate the study of the underlying causes. In the past, we were satisfied with merely discovering that correlation. Just like how animals keep returning to a spot where they got food, but without knowing the underlying reason. Similarly, primitive men were satisfied with different degrees of correlation. For example, natural medicines associate certain herbs with symptoms and cures, or certain actions like freezing food or using spices as preservation mechanisms. But they never investigate the structural properties of herbs or the underlying mechanisms causing that effect.
The modern scientific mentality was not satisfied with those mere correlations. It sought to discover causes, theorize, and apply those mechanisms in multiple diverse contexts. For instance, insights from discovering the causal mechanism for overlapping memories may lead to other insights. An understanding of the underlying structure that causes this may lead to new theories — how certain neural pathways can be intentionally activated. That may have unrelated applications, such as motivational tools or strategies for mitigating depression. Transforming it into a generic theory that finds relevance in diverse psychological contexts.
If we abstract further, then the concurrent activation of neural signals may have applications in computing circuit designs. The logic embedded in such circuits is driven by changes in their internal state triggered by external stimuli, similar to how the mind itself operates. If we abstract further, a neural layout may provide inputs to design efficient factory workflows. They both process input stimuli through a series of dependent steps to produce an output.
Eventually, a complex system will have encapsulated layers and substructures. Studying and isolating those structures generates abstract, reusable information. In short, if we iteratively examine and identify an intelligible structure in overlapping memories, then its utility might extend beyond motorcycling metalheads. To paraphrase Louis Rougier, it’s a mentality that moves from specific to the abstract.
“Rising from the concrete to the abstract, Greek geometry disengaged the intelligible essence from the particular observable details, or accidents, as such particulars were later to be called. In this it exercised the proper function of intelligence: the faculty of abstracting, of grasping the unity of a concept in a number of particular cases, the constancy of relationships and permanence of structures amid the diversity of sensible patterns; in a word, finding unity in multiplicity and harmony in discord. With the Greek language was born the language of abstraction.”— Louis Rougier
That Greek language of abstraction can be applied across disciplines and structural layers – from theoretical psychology to institutional economics. That means, the same process scales beyond individual reasoning into the design of institutions themselves. For instance, moving from specific rules to the abstract Rule of Law illustrates the same concept. Instead of directing everyone to specific duties, we created laws that satisfied certain properties, leading to an efficient social order. An obvious example would be — the vehicles on the road are never directed to destinations, instead the expectation is to just follow the rules of the road. We basically abstracted the essence of efficient navigation into the impersonal Rule of Law.
Movement from Magna Carta to American federalism is sort of that steady evolution from specific to the abstract. In fact, Federalism adds one more layer to that dispersed rule-of-law framework. Sort of a higher-level instrument to institutionalize the development of good laws. An attempt to automate the generation of efficient laws itself. As James Madison explained, all the checks and balances should lead to good legislation. The results of this experiment can be debated, but there is an interesting scientific argument for an approach like Federalism. We can see parallels to that in modern scientific methods.
In fact, Federalism is structurally very similar to scientific frameworks such as ISO 21434, ISO 26262, etc. Just as in Federalism, these standards enforce internal consistency of information against a set of goals by subjecting the system to diverse external pressures. The V-model refines engineering details, while federalism refines legislation. ISO26262 may have a narrow goal, but it still does not dictate the nature of the vehicle to be engineered; following it just enforces systemic consistency of whatever the engineers want to design. Federalism has a relatively more open-ended goal and, as a result, introduces greater personnel and role dependencies in the outcome. But they are both structured approaches to stress test epistemological foundations. They both abstracted the properties of the process of reasoning that leads to truth, then designed an impersonal framework to enforce it.
Instinctive exploration and abstraction enable the development of reusable cognitive tools and functions. They allow the development of higher-level mental models to deduce meaningful information from complex life experiences. But improving their explanatory power requires repeated application in real-world encounters, followed by refinement through feedback. This process is illustrated in the next chapter.
Republished at ridersmodel.com