Bayesian network model.
Have the testee blindfolded and be led around a grid that contains variously (predetermined at random) water or not. Have them give a clear definition of what counts as a "yes twitch" of their equipment.
Note every time that it does so and in what grid location it does so.
Then run a Bayesian model of it seeing if there is any predictive power of the twitching for the location.
The math is a bit complex but the testing procedure itself is eminently simple.
Have the testee blindfolded and be led around a grid that contains variously (predetermined at random) water or not. Have them give a clear definition of what counts as a "yes twitch" of their equipment.
Note every time that it does so and in what grid location it does so.
Then run a Bayesian model of it seeing if there is any predictive power of the twitching for the location.
The math is a bit complex but the testing procedure itself is eminently simple.