A) STOCHASTIC RESONANCE
Assume that the transition to m depends on a threshold for total activation of m *relative to* competitive activity nearby.
Now, to recall m, simply add uniform noise to the general neighborhood of M. The noise is merely additive for neurons not in M, whereas it's *multiplicative* for M due to its loops. The noise relatively amplifies activity in recurrent networks, but only if they survive the noise (ie exceed the "error threshold" for mutation).
I actually think the brain uses both.
I think it's usually model B. I think effortfwl recall correlates w alpha oscillations bombarded across the cortex, and this reduces temperature in non-bombarded areas. Successfwl recall correlates w P300 EEG-signature, which according to Dehaene indicates a preponderance of inhibitory neurons.
But I also think I've had personal success w "deconcentration of concentration" to recall blocked Tip-of-the-Tongue memories. And this corresponds to model A!
B) FREE UP ENERGY FOR ORDER
Assume that the height of the threshold depends on the stability of sub-completions of m, because the pattern can't complete if its progress gets partially reset all the time.
Now, you can make it easier to recall m by lowering neighborhood noise. But if there are competing memories in that same neighborhood, you may end up accidentally stabilizing the wrong one.