We all know that the interaction with other people affects our mood and thoughts very strongly. While an individual is interacting with another person, the brains of the two persons become coupled as one's brain analyzes the behavior of the other and vice versa. This is why the neuroscience community is now looking towards a pair instead of an individual as a proper unit of analysis.
There have already been studies on humans under controlled interaction, such as a movie or a computer game. While watching a movie, brains of individual viewers have been shown to be activated in a very synchronous fashion. Game against a human opponent activates the brain differently from a game against computer, which is also reflected in the reported feelings of the players.
Mirroring is a phenomenon which has been possible to study with existing technology. We feel pain when we are shown a picture of a suffering person. Already Ludwig Wittgenstein noted that "The human body is the best picture of the human soul". How individual's feelings tune into other person's feelings, is a more complicated question. It is a combination of the following factors:
- similar senses, motor systems and the brain that the individuals have
- the experience that they collect throughout their lives, and
- the beliefs they test by acting in the community.
Machine learning steps in for the analysis of the high-dimensional data produced by the functional measurement technologies. Dimensionality reduction methods such as independent component analysis (ICA) extract noise-free components that can potentially be biologically interpreted.
So far in most of the studies of human interaction, only the activity of one brain has been measured regardless of the presence of the other interacting person. Soon, however, accurate measurements of several subjects at a time will be possible, and that will most likely push for a leap in the development of computational data fusion techniques. Then, we will not only have a link between a stimulus and a brain image but between a stimulus and images of several subjects' brains.
When the focus of brain research moves towards the analysis of two or more interacting subjects, efficient multi-view methods will be needed. Thus, multi-view learning is currently a hot area of machine learning research.
Prof. Hari's message to the ICANN audience was that the analysis remains the bottleneck in brain research. As methodological researchers, we should next consider the opportunities opened by the new experiment settings and measurement technologies, and see how to learn more from the data.