  Slow feature analysis (SFA) is a method for extracting slowly varying driving
forces from quickly varying nonstationary time series. We show here that it is
possible for SFA to detect a component which is even slower than the driving
force itself (e.g. the envelope of a modulated sine wave). It is shown that it
depends on circumstances like the embedding dimension, the time series
predictability, or the base frequency, whether the driving force itself or a
slower subcomponent is detected. We observe a phase transition from one regime
to the other and it is the purpose of this work to quantify the influence of
various parameters on this phase transition. We conclude that what is percieved
as slow by SFA varies and that a more or less fast switching from one regime to
the other occurs, perhaps showing some similarity to human perception.
