How does Gaussian prior on weights guarantees that the units are not likely to interact with each other?

In the Deep Learning book [1], Section 8.4, the authors wrote that

... (imposing a gaussian prior on weights) says that it is more likely that units do not interact with each other than that they do interact. Units interact only if the likelihood term of the objective function expresses a strong preference for them to interact.

I do not understand how imposing a Gaussian prior tells the units are not likely to interact with each other?

I have pasted the paragraph from book for reference:

[1]: Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning. MIT Press, 2016