Read the equation
The first transition allows the outer process L to depend on x, while the second allows x to depend on L. This notation makes the proposed two-way path explicit. Actual causal coupling requires those dependencies to be operative and testable; a function signature can include an argument that its implementation ignores.
Terms & definitions
| Term | Meaning in this source |
|---|---|
| L_t | Outer process or loop state |
| x_t | Internal state |
| u_t | External input |
| F | Outer transition |
| G | Internal transition |
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
Source recovery establishes that the expression appears in this record. Its mathematical validity, implementation behavior, and interpretation must be assessed separately. See the linked source and research notes for the checks available for this version.
Original context
Read the surrounding source passage
2.4 Coupling is a causal requirement A loop drawn around a node does not change that node merely because of its position on a diagram. The dynamical model must specify an influence. A generic possible form is: Lt+1=FLt,ut,xt, xt+1=Gxt,Lt. If G is independent of Lt, the loop cannot affect xt through that channel. If G depends on Lt, feedback can cause changes, fixed points, oscillations, or other trajectories depending on the functions and parameters. No universal threshold law follows without specifying them. This is not an argument that computation excludes consciousness. A physically realized conscious mechanism, if it is computational, would still follow its dynamics. The issue is whether the observations distinguish the proposed consciousness-relevant organization from other recurrent mechanisms. The present analysis measures training-log dynamics and adapter parameters, not an independently observed self-model. 3. Source acquisition, verification, and experimental unit
Exact original notation
{L}_{t+1}=F\left({L}_{t},{u}_{t},{x}_{t}\right), {x}_{t+1}=G\left({x}_{t},{L}_{t}\right).Related builds
Sources & provenance
{L}_{t+1}=F\left({L}_{t},{u}_{t},{x}_{t}\right), {x}_{t+1}=G\left({x}_{t},{L}_{t}\right).
{L}_{t+1}=F\left({L}_{t},{u}_{t},{x}_{t}\right), {x}_{t+1}=G\left({x}_{t},{L}_{t}\right).
{L}_{t+1}=F\left({L}_{t},{u}_{t},{x}_{t}\right), {x}_{t+1}=G\left({x}_{t},{L}_{t}\right).
{L}_{t+1}=F\left({L}_{t},{u}_{t},{x}_{t}\right), {x}_{t+1}=G\left({x}_{t},{L}_{t}\right).