According to the deductions above, we can find out that decision X i,j is not i.i.d anymore after the local vote.
Let Pleft u=1Big|x;wright)=h(x) (21) Pleft(u=0Big|x;wright)=1-h =1-h22)where u denotes the FC's decision, x denotes the input of the features, w is the weight of the LRFR, and h(x) represents Eq. (16).
Where decision x t would be chosen, which minimizes the one-period contribution C(S t, x t ).
Compared to intervention participants, fewer standard control participants (6%) made an informed decision (X(1) = 71.97, P <0.001, Table 3).
We were thus surprised to find that no voxels in this region varied inversely with the three-way interaction between rule, decision and value (i.e., encoded decision x value inversely under the two rules).
The contribution (measured in terms of the resulting response times) earned for taking decision x t when in state S t is denoted by C(S t, x t ).
The decision x t we are about to make at time t is represented using x tad, which models the number of ambulances with attribute a upon with we act on with an elementary decision of type d ∈ D, whereas x t = (x tad ) a ∈ A, d ∈ D. There exist two major decision classes for the problem at hand.
By using this post-decision state variable one can reformulate the transition from S t to S t +1 using S M, x and S M, W. S M, x captures the transition triggered by decision x t from state S t to S t x, the post decision state, which describes the state of the system immediately after making the decision but before any new information arrives.
In a subsequent analysis, we additionally extracted the BOLD timeseries from the peak group response to rule x decision x value in the dACC (−6, 32, 34) and included it (and its interaction with defer vs commit) as an additional regressor.
