AUTHOR: SIERRA N. WARREN

This paper presents a dual-framework model that tackles two crucial challenges: the structured classification of Non-Human Intelligence (NHI) signals and the regulation of metacognitive uncertainty within collaborative Human–AI systems.

1. NHI Signal Classification Framework

The first framework establishes a taxonomy for anomalous, potentially non-human intelligence signals, such as those of biological, interstellar, or advanced synthetic origins. Current methods lack systematic categorization, resulting in false positives and ambiguous data interpretation.

The proposed model evaluates signals across three distinct vectors:

- Structural Complexity: This vector distinguishes between stochastic noise and ordered, information-bearing patterns.
- Semantic Density: This vector measures the data-to-signal ratios to identify embedded syntax.
- Intentionality Metrics: This vector assesses directional, repetitive, or adaptive behaviors that indicate conscious generation.

The process involves the following steps:

- Raw Signal Data is processed through a Structural Taxonomy Filter.
- The filtered data is then analyzed to identify anomalous intent vectors.

2. Metacognitive Uncertainty Regulation

The second framework addresses how Human–AI teams process these highly ambiguous NHI signals. When faced with unprecedented data, AI models often exhibit overconfidence, while human operators suffer from cognitive overload. The proposed model governs the processing of these signals by implementing a system that helps Human–AI teams manage metacognitive uncertainty.

This system involves the following steps:
- High-Ambiguity Signal is presented to the Human–AI team.
- The team processes the signal and generates a metacognitive uncertainty score.
- The metacognitive uncertainty score is used to guide the team’s decision-making process.
The system dynamically regulates cognitive load by calculating a joint uncertainty metric.