Supra-additive relations among timeseries (separate ratings of a single stimulus) I conducted an online behavioural study (N=20) where people listened to a song with lyrics, and rated (in three counter-balanced conditions) the tension they perceived in the music alone (M), the lyrics alone (L), and in the song as a whole (M&L).
Subjects indicated their rating by moving the mouse to control a slider. They were encouraged to move the slider continuously so as to closely track their subjective tension rating, directing their attention to one or both of the M&L dimensions, as per the current condition. The slider returned data points no less than 50-100 ms apart.
One subject's ratings are shown below as an illustration.

I'd like to find out to what degree the "holistic" (M&L) rating contains "signal" that is above & beyond that found in the individual (M, L) ratings. This is admittedly quite vaguely construed, and so I'm not sure how to best operationalise this in my data analyses. Just computing correlations (or dynamic time warping) between the 3 signals pair-wise seems simplistic. Perhaps Markov-modelling can be of help, but I'm not sure how.
For more context, this is a pilot for a later study with brain measurements. The specific hypothesis I'm hoping these behavioural data weigh evidence for (or against), is that the brain processes that track tension in music and language integrate supra-additively, i.e. that "M&L" > "M"+"L", with the "+" loosely understood to be interaction rather than simple addition.

Why not model it as a linear regression? If you believe you have an interaction you can add it to the model.
Then you can model it as well.
$ML = aM+bL$
You can then do hypothesis testing on the coefficients found for every subject.
Alternatively if you assume you have a single function describing all the subjects you can model it as a single regression with (or without) a random effect for the subject and get uncertainty estimates for the coefficients.
