Short Summary
This framework reverses the traditional sensory-first view by proposing that self-initiated movement precedes perception, with consciousness and intelligence evolving from this active probing strategy.
Evolutionarily, even simple life forms like bacteria exemplify this through movement-first behaviors, such as E. coli’s chemotaxis, where motion generates sensory feedback.
In complex organisms, consciousness arises from the internal simulation of action—essentially thinking as covert, inhibited movement—allowing for evaluation before physical execution.
This model aligns with active inference theory, where intelligence and creativity emerge from hierarchical motor patterns and the competition between potential actions.
The paper introduces “coherence-depth” as a metric for adaptive capacity, measuring synchronization across scales from neural circuits to ecosystems.
Finally, it applies this to planetary scales, suggesting human society’s current decoherence can be addressed by transferring integrative patterns already found in nature.
J.Konstapel Leiden, 29-1-2026.
A Unified Framework Integrating Probing, Active Inference, and Multi-Scale Adaptation
This is a spin-off of Swarm Intelligence and the Spatial Web
1. Executive Overview
Contemporary neuroscience and artificial intelligence still largely operate within a sensory-first paradigm: perception is assumed to precede action, and cognition is treated as internal computation over representations. The framework developed in From Movement-Based Consciousness to Planetary Coherence reverses this assumption. It argues that action—specifically self-initiated movement—precedes perception, and that consciousness, intelligence, and even large-scale coordination emerge as elaborations of this primordial strategy.
Building on Cotterill’s probing model of consciousness (2001), active inference (Friston et al., 2010–2024), and recent object-centric AI architectures (Heins et al., 2025), the paper introduces coherence-depth as a unifying metric of adaptive capacity across biological and artificial systems, from bacteria to biospheres.
2. Movement as the Evolutionary Primitive
The core biological claim is simple but far-reaching: the most ancient and universal behavioral strategy on Earth is self-paced environmental probing. Even organisms without nervous systems do not passively receive stimuli; they move first, then evaluate the consequences of that movement.
The canonical example is E. coli chemotaxis (Berg, 1993). The bacterium cannot sense spatial gradients instantaneously. Instead, it swims, samples chemical concentrations over time, compares present conditions to a short internal memory (~4 seconds), and adjusts its motion accordingly. In this loop, movement generates the stimulus, and the environment provides feedback. This inverts classical stimulus–response logic.
Cotterill (2001) generalized this observation across evolution: reflexes are late, secondary optimizations layered atop a far older movement-first architecture. Cognition, at its most basic level, is defined operationally as behavior that changes in response to internal or external conditions. Consciousness, however, is something more specific.
3. Consciousness as Covert Probing
In mammals, the probing strategy becomes internalized. Consciousness is not located in sensory cortices, nor does it arise from abstract computation. Rather, it is the capacity to simulate action without executing it, allowing organisms to evaluate possible outcomes before committing to movement.
Neuroanatomically, this capacity depends on a specific inhibitory architecture. Peripheral reflexes and central pattern generators can produce complex behavior autonomously. The brain’s evolutionary innovation lies not in generating movement, but in selectively inhibiting it.
Two systems are central:
• Basal ganglia, which provide rapid, context-sensitive disinhibition of action, strongly modulated by affective valuation (amygdala, dopaminergic signaling).
• Cerebellum, which slowly consolidates successful movement patterns into habits through error correction and long-term depression.
Together, they implement conditionally permitted movement. Motor plans are continuously generated, but only those that pass inhibitory thresholds are executed. When these plans are run at sub-threshold levels—producing efference copies without muscle contraction—the organism experiences thought.
On this account, thinking is covert movement (Ritchie, 1936). Consciousness is the system monitoring its own embodied simulations in order to acquire new, context-specific reflexes. Routine or well-learned behavior requires little or no consciousness; novelty and ambiguity demand it.
4. Alignment with Active Inference
This model maps cleanly onto the active inference framework developed by Friston and colleagues. In active inference, agents minimize expected free energy by continuously cycling between action and perception. Crucially, perception is not passive; it is shaped by predictions generated from action-oriented generative models.
Within this lens, covert motor simulation corresponds to policy evaluation: imagined action sequences are assessed for their expected outcomes before being enacted. Consciousness, therefore, is not a metaphysical add-on, but a functional necessity for navigating complex “muscular hyperspace”—the vast space of possible coordinated actions.
This also explains why consciousness is graded rather than binary, and why it correlates with behavioral flexibility rather than sensory acuity.
5. Intelligence and Creativity as Motor Phenomena
Intelligence, in this framework, is defined as the capacity to consolidate simple movements into hierarchically organized patterns. Learning to walk, speak, or manipulate tools involves integrating atomic motor elements into increasingly abstract sequences. Once consolidated, these patterns become implicit and no longer require conscious oversight.
Creativity emerges from competition among motor plans. Experimental work on decision thresholds (Carpenter, 1988; 1999) shows that multiple action plans race toward execution. Novelty arises when feedback connections introduce unexpected correlations into this race, occasionally allowing low-probability plans to win. Exploration is thus a controlled violation of habit.
This reframes creativity not as a mysterious mental faculty, but as a stochastic property of embodied control systems operating near threshold.
6. Computational Embodiment: AXIOM
Recent developments in artificial intelligence provide independent support for this view. The AXIOM architecture developed by VERSES AI implements active inference using object-centric, hierarchical belief models rather than large-scale backpropagation.
AXIOM agents plan by simulating actions, updating beliefs via variational Bayesian inference, and selecting policies that minimize expected surprise. Benchmark results show dramatic gains in efficiency and generalization compared to deep reinforcement learning systems (Heins et al., 2025).
Conceptually, AXIOM operationalizes Cotterill’s insight: intelligence arises from probing, not from passive data ingestion.
7. Coherence-Depth: A Cross-Scale Metric
The paper’s primary theoretical innovation is coherence-depth: a proposed quantitative measure of consciousness and adaptive capacity defined as the degree of phase-locking across nested scales of organization.
At different levels:
• Neurons coordinate via synchronized oscillations.
• Organisms integrate motor–sensory loops.
• Collectives coordinate via movement synchrony (flocking, schooling).
• Ecosystems coordinate via nutrient cycles and mycorrhizal networks.
High coherence-depth corresponds to resilient, integrated adaptation. Low coherence-depth corresponds to fragmentation and brittleness. The concept connects naturally to existing mathematical tools, including Kuramoto order parameters, entropy measures, and integrated information metrics, while remaining agnostic to any single formalism.
8. Planetary Implications
Human civilization, as currently structured, exhibits low coherence-depth: broken feedback loops, extractive flows, and misaligned temporal scales. The paper frames this as a decoherence crisis, not a moral failure.
Drawing on TRIZ-style contradiction analysis, it outlines nine core tensions (individual vs. collective, growth vs. regeneration, technology vs. nature) and shows that each has already been resolved somewhere in the biosphere through specific organizational patterns. These resolutions generate 45 plausible, organism-validated futures, emphasizing pattern transfer rather than ideological design.
The implication is that planetary-scale intelligence is not speculative science fiction, but an extension of principles already operating in ecosystems.
9. Scientific Status and Testability
The framework is synthetic rather than empirical, but it generates clear predictions:
• Systems with higher coherence-depth should adapt better under uncertainty.
• Consciousness correlates with the ability to simulate and inhibit action, not with sensory richness.
• Breakdown of coherence should precede dysfunction in brains, organizations, and ecosystems.
These claims are testable using neurophysiology, multi-agent AI systems, and ecological network analysis.
10. Conclusion
This work reframes consciousness as an evolutionary solution to a control problem: how to safely explore an uncertain world through action. From bacterial chemotaxis to human thought to planetary coordination, the same principle applies—movement generates meaning.
By integrating neurobiology, active inference, and systems ecology, the framework offers a unified, non-mystical account of mind, intelligence, and collective futures. Its value lies less in any single claim than in the coherence of the whole.
References (selected)
Berg, H. C. (1993). Random Walks in Biology. Princeton University Press.
Carpenter, R. H. S. (1988). Movements of the Eyes. Pion.
Cotterill, R. M. J. (2001). Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus. Progress in Neurobiology, 64, 1–33.
Friston, K. (2010). The free-energy principle. Nature Reviews Neuroscience, 11, 127–138.
Heins, C. et al. (2025). Active inference as a computational framework. arXiv:2505.24784.
Ritchie, A. D. (1936). Scientific Method. Routledge.
Sherrington, C. S. (1924). Problems of muscular receptivity. Nature, 113, 892–894.
Sperry, R. W. (1952). Neurology and the mind-brain problem. American Scientist, 40, 291–312.