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Methods for measuring event-locked changes in entrainment and synchrony

Most papers that have looked at interpersonal entrainment research have basically used the same approach. You measure a load of free interaction data, and then you compute your interpersonal entrainment measure – whether it’s Granger Causality, or Phase Locking Value, or Wavelet Transform Coherence – in a way which, like a Fourier Transform, basically collapses the time dimension out of the picture. In one paper, for example, we just cut out all of the times during an interaction when the baby was looking directly at an adult, stuck them all together, and then calculated the Partial Directed Coherence across all the data that you have available. Other papers use a similar approach to look at how connectivity differs on average between different dyads. But we don’t have the methods yet to look at how connectivity changes over time – for example, relative to particular behavioural events.

This is a pain because it makes it much harder to work out how, exactly, connectivity between two interacting brains is established and maintained. And understanding this would be useful in a bunch of ways.

Take, for example, the finding that in the 3-9Hz range, neural activity in one partner consistently predicts the other partner’s neural activity more strongly during direct compared with indirect gaze. How exactly is this possible? How can two brains influence one other over such a fine-grained scale? Well, there are several different possibilities, and to tell them apart it would certainly help to be able to look at event-related patterns of change: ie how does connectivity change relative to particular, pre-specified events – such as gaze onsets, vocalisations etc. And in the paper we basically lay out some algorithms that would let people do this. We look at concurrent entrainment (e.g. power correlations, phase locking) and sequential entrainment (e.g. Granger causality). And we apply them to three aspects of the brain signal – namely amplitude, power and phase.

Anyway, it’s pretty exciting!! If you’re interested you can read the whole paper here.