Figure 1: Dynamic logic operation example, finding cognitively related events in noise, in EEG signals. The searched processes are shown in Figure 1 at the bottom row. These events are “phase cones,” circular events expanding or contracting in time (horizontal direction t, each time step is 5 ms); in this case, two expanding and one contracting events are simulated as measured by an array of sensors. Direct search through all combinations of models and data leads to complexity of approximately = 1010,000, a prohibitive computational complexity. The models and conditional similarities for this case are described in details in [44], a uniform model for noise (not shown), expanding and contracting cones for the cognitive events. The first 5 rows illustrate dynamic logic convergence from a single vague blob at iteration 2 (row 1, top) to closely estimated cone events at iteration 200 (row 5); we did not attempt to reduce the number of iterations in this example; the number of computer operations was about 1010. Thus, a problem that was not solvable due to CC becomes solvable using dynamic logic.