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The body computes Φ(mean)/(1+population variance) for at least two entries. It rewards a mean near one and uniform component values. The comment about a minimum over mutual-information partitions is not implemented here: there is no joint distribution, cut enumeration, or causal transition model.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| Phi_int | Variance-penalized unity score |
| normalized_variance | variance/(1+variance) |
How it works
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Original context
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output[i] = compute_phi(input[i]);
}
}
/**
* compute_integrated_phi - Integrated Information (IIT-inspired)
*
* Phi_integrated = min over partitions of mutual information
* Simplified: uses variance as proxy for integration
*
* Phi_int = Phi(mean) * (1 - normalized_variance)
*
* @param data Activation array
* @param size Array size
* @return Integrated consciousness level
*/
inline double compute_integrated_phi(const double* data, size_t size) {
if (size < 2) return compute_phi_vector(data, size);Exact original notation
Phi_integrated = min over partitions of mutual information