Preliminary Notes on Cognitive Dynamics and Neural Efficiency

When people talk about intelligence, they often imagine it as a fixed trait—something like
height or eye color. Traditional IQ scores reinforce this idea by presenting intelligence as a
single number that supposedly captures a person’s mental ability. But real thinking doesn’t
work that way. It’s not static, and it’s not uniform. It changes depending on the situation,
the task, the time available, and even how tired or distracted someone is.

The cognitive dynamics model starts from a simple observation: thinking is a kind of
activity. It takes time, it requires energy, and it happens under conditions that can either
help or hinder it. Instead of treating intelligence as a fixed quantity, this model treats it as a
process—something that unfolds over time, shaped by the conditions under which a person
is working. 

Imagine navigating a landscape. Some terrain is smooth and easy; some is steep or rocky.
The distance you cover depends not only on how fast you can move but also on how much
resistance the terrain gives you. Thinking works the same way. A person’s natural mental
speed matters, but so does the difficulty of the problem, the level of fatigue, and the
amount of distraction or pressure they’re under. These sources of resistance are what the
model calls “cognitive friction.”

Cognitive friction is the drag on mental movement. It’s the slowing force that makes
thinking harder as tasks become more complex or as time wears on. Anyone who has
worked on a long exam or a demanding project knows the feeling: the first hour is sharp
and focused, but later hours feel heavier, slower, and more effortful. The model treats this
not as a flaw but as a natural part of how minds operate.

Once you think of mental work as movement through a problem space, you can talk about
“effort” as the total distance traveled. Time contributes to effort, but so does mental speed.
Someone who works slowly but steadily may cover the same cognitive distance as
someone who works quickly but tires early. This makes effort a more realistic measure of
what a person actually does during a task.

From here, the idea of “neural efficiency” emerges. Neural efficiency is not about how
smart someone is in the abstract. It’s about how effectively they convert their effort into
meaningful output. Two people might spend the same amount of time on a test, but one
produces clearer, more accurate, or more insightful results because they lose less energy to
friction. In this sense, neural efficiency is closer to the idea of power in physics: it
measures how much useful work is produced under real-world conditions.

This leads to the most interesting shift: instead of talking about IQ as a static score, the
model talks about IQ output—the actual performance that emerges from a person’s
interaction with a task. IQ output depends on ability, but also on friction, time, and the
structure of the problem itself. It’s a dynamic measure, not a fixed one.

In the end, this model reframes intelligence as something alive, adaptive, and responsive. It
treats thinking as a journey through a landscape, shaped by both the traveler and the
terrain. And it reminds us that performance is never just about raw ability—it’s about how
effectively that ability is used under the conditions at hand.

Kenneth Myers