Foundation Applied: Reconstructing Past Motion Patterns
Archaeological research is where theoretical foundation meets empirical reality. Every excavation, every analysis, every inference about the past tests whether our frameworks actually work.
The signal-based approach transforms how we understand archaeological data:
Ireland RMP Project: 6,000 Years of Territorial Signals
Ireland’s Record of Monuments and Places contains 150,000+ archaeological features spanning from the Neolithic to the Medieval period. Traditional approaches catalog these chronologically by type—wedge tombs, ringforts, ecclesiastical sites.
The signal approach treats these distributions as degraded territorial signals. By applying contiguity matrices, kernel density estimation, and k-nearest neighbor graph analysis, we recovered:
Signal correlations suggesting historical continuity
This demonstrates we can greatly increase temporal scope, resolution, and accuracy even with noisy, incomplete data.
Laetoli Footprints: Pure Motion Traces
The Laetoli footprints from 3.66 million years ago represent the clearest possible archaeological signal—actual motion traces preserved in volcanic ash. Recent photogrammetric reconstruction reveals:
Detailed gait patterns and locomotion mechanics
Evidence for diverse body sizes and ages
Potential behavioral inferences from track distribution
These are motion patterns made directly observable, allowing us to test biomechanical models and infer selective pressures on hominin evolution.
Hominin Evolution & Object Control
The evolution of the Hominidae cannot be understood separately from technological development. Tool use is not a “cultural trait” layered onto biology—it is an extension of the somatic system boundary that fundamentally altered the selective environment.
Each innovation in controlled motion (biface symmetry, hafting, composite tools) required and selected for new neural control systems, creating evolutionary feedback. We can trace this through:
Changes in hand morphology (Ardipithecus to Homo)
Increasing cranial capacity correlated with tool complexity
Evidence for specialized motion patterns in artifact assemblages
Modern computational tools finally make signal-based archaeology practical:
LLM-powered extraction of features from historical documents and survey data
GPU acceleration for processing landscape-scale datasets
Neural networks for pattern recognition in archaeological signals
Embedded systems (Raspberry Pi “Time Engines”) for field data collection
These aren’t just digital conveniences—they enable fundamentally new kinds of analysis by treating archaeological datasets as signal processing problems.
Field Work: Ground Truth for Signal Models
Excavation remains essential. Physical stratigraphy, material analysis, and contextual relationships provide the ground truth that validates (or falsifies) signal-based models. Every trowel stroke records new data points that refine our understanding of how motion traces decay and what patterns persist.
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