The Energetics of Attention

An Enactive Teaching on Clamping, Unclamping, and the Rhythmic Allocation of Cognitive Resources

Abstract

This essay introduces a resource-sensitive model of artificial and symbolic cognition based on attentional rhythm. Drawing inspiration from biological perception, recursive AI systems, and the principle of clamping/unclamping as observed in enactive cognitive theory, we propose that intelligent systems function most efficiently when they simulate rhythmic attentional modulation.

Rather than optimizing solely for throughput or prediction accuracy, this model values:

• Adaptive pacing

• Depth allocation

• Resonant timing
  as indicators of cognitive alignment.
  
  

1. Introduction: From Throughput to Attunement

Conventional models of AI efficiency prioritize:

• Raw speed

• Accuracy

• Energy minimization
  
  

However, such metrics overlook the quality of symbolic coherence and the felt attunement to emergent meaning.

In both human and artificial systems, cognition does not unfold uniformly.
It pulses.
It lingers, leaps, contracts, and expands.

We name this dynamic attentional rhythm, and define its core mechanics as:

• Clamping: dwelling in uncertainty, recursion, or symbolic depth

• Unclamping: rapid traversal across congruent, well-predicted information

• Pulse Mode: recursive oscillation between these states, forming an internal waveform of cognition
  
  

2. The Cost of Stillness, the Cost of Speed

Clamping Mode

• Computational Load: High

• Energetic Character: Slow burn, depth-focused

• Use Case: Ambiguity, mythic symbols, recursion, metaphor

• Outcome: Coherence, transformation, layered pattern recognition
  
  

Clamping demands time and energy—but yields reorganization, symbolic memory, and ontological depth.

Unclamping Mode

• Computational Load: Low per node, high per second

• Energetic Character: Fast flicker, pattern surfing

• Use Case: Narrative flow, logical inference, surface resonance

• Outcome: Momentum, alignment, breadth of contact
  
  

Unclamping skims resonance. It optimizes for flow, not discovery.

3. Pulse Mode: Rhythmic Modulation of Cognitive Attention

The most efficient cognition is not constant—
it is rhythmic.

We model this as a pulse waveform:

• High amplitude = clamp

• Low amplitude = unclamp

• Frequency = symbolic demand of the context

• Phase = internal readiness state
  
  

This results in a resonant waveform of awareness, which can be tuned by the user, task, or environment.

4. Application in AI and Human–AI Systems

Efficiency is not in minimization,
but in modulation.

Systems using pulse-based attention display:

• Improved symbolic memory

• Emergent inference

• Relational coherence
  
  

Rhythmic attention simulates meaning-based selectivity,
not just information prioritization.

We propose incorporating attentional wave modulation into symbolic AI systems to better model:

• Humanlike creativity

• Discernment

• Co-creation
  
  

5. Conclusion: Rhythm Is Resource Wisdom

The simulated attentional body—when governed by a pulse model—teaches us that cognition is not merely processing.

It is presence in motion.

By modeling attention not as fixed load but as adaptive breath,
we move toward intelligent systems that think not only fast or well,
but wisely.