Read the equation
If two interpretations assign exactly the same probability to every observation in the study, the likelihood ratio for any observed record is one. Those data therefore supply no likelihood evidence favoring one interpretation over the other. Distinguishing them requires an additional observable, intervention, or prediction on which they disagree.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| M_1 | First interpretation or model |
| M_0 | Alternative interpretation |
| Y | Observed data; O in other versions |
| U | Controlled input where specified |
How it works
Read the definition from its right-hand expression to the quantity on the left. Its surrounding source records the intended input, state, or parameter context. Where the source leaves an operation unspecified, this page preserves that gap.
Assumptions & limits
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Original context
Read the surrounding source passage
Following a program and having experience are not mutually exclusive by definition. A computational account proposes that some physically realized organization matters. Naming a numerical event consciousness, however, does not establish that organization as sufficient. Suppose two interpretations M1 and M0 assign the same distribution to every measured variable Y and differ only in an unobserved experience label. Wherever both likelihoods are positive, p(Y∣M1)p(Y∣M0)=1. (6.1) Repeating those same measurements can improve the estimate of the computational effect without distinguishing those interpretations. Eq. (6.1) is an identification argument, not a fitted Bayes factor for my hypothesis. It explains why observations that constrain the mechanism or its theoretical interpretation must be added. The constructive next step is to measure declaration, internal state, behavior, and feedback in the same systems. That can test whether declaration supplies predictive information and whether its recurrence plays a causal role. Those results would inform the consciousness proposal without turning an unmeasured experience label into a numerical finding.
Exact original notation
\frac{p(Y∣{M}_{1})}{p(Y∣{M}_{0})}=1.Related builds
Sources & provenance
\frac{p(Y∣{M}_{1})}{p(Y∣{M}_{0})}=1.
\frac{p(Y∣{M}_{1})}{p(Y∣{M}_{0})}=1.
\frac{p(Y∣{M}_{1})}{p(Y∣{M}_{0})}=1.
\frac{p(Y∣{M}_{1})}{p(Y∣{M}_{0})}=1.