  With reference to a previous work, the problem of the experimental detection
of non-causal synordination patterns between two series of physical events is
examined. It is necessary that the patterns in question act in a reproducible,
or at least regular, manner, so that they can be identified by means of
statistical analysis; action on individual occasional facts cannot be proved
statistically, though it is hypothetically possible. As established in the
previous work, the first series must be constituted by microevents (Penrose R
processes), that can be amplified at the macroscopic level so that they are
translated at this level into a choice between alternatives. The obtained
result defines (by means of a structural constraint which is not, however, a
causal link) the choice between execution or non-execution of certain actions
on a macroscopic system S, without any feedback on the microevents source. A
succession of macroevents is thus generated, which makes up the second series.
Statistics of the events belonging to the second series is thus completely
defined by the probability distribution of the first series of events. In the
absence of a synordination, this is the a priori probability distribution,
which can be measured by removing the constraint between the two series. If
there is synordination, the probability distribution of the two sets of events
will deviate in a measurable manner from the expected trend once the constraint
is activated.
