reafference-corollary-discharge

Reafference and corollary-discharge mechanisms predict your own motor outputs and subtract them from incoming sensory feedback. When prediction matches actual input, the signal is self-generated; mismatches indicate external events. This prediction-and-cancel approach isolates genuine external stimuli from self-produced noise.

The reafference principle, formalized by von Holst, states that motor commands generate predictable sensory consequences called reafference. By comparing predicted feedback (efference copy) against actual sensory input, systems classify interactions as self-initiated or externally driven. This mechanism enables stable perception during active behavior.

Generate deterministic predictions from input hashes to color outputs, then compare predictions with actual sensory history. Compute color-distance-based error scores and apply threshold rules. The comparator flags matches as self-generated and mismatches as external, enabling behavioral verification and threat assessment across multi-agent systems.

Yes. Corollary discharge applies suppression and amplification rules to stabilize perception based on match scores. Interactions meeting thresholds are suppressed as self-generated; deviations are amplified as external threats. Results persist to DuckDB and propagate downstream for analysis in integrated agent workflows.

Use reafference filtering when you must distinguish self-initiated actions from external events—critical for behavioral verification, threat detection, and multi-agent coordination. Raw input alone cannot separate motor-induced feedback from genuine external signals; prediction-and-cancel is necessary for robust interaction classification.