Obviously MVPA has a really interesting future. As neuroimaging technology improves spatial and temporal resolution, we’ll get finer distinctions and more detailed pictures of neural representation. Norman et al. have a fantastic overview of MVPA, including numerous interesting questions and speculations about its future, called Beyond mind-reading: multi-voxel pattern analysis of fMRI data.
Some of my thoughts and questions about MVPA follow.
We can use MVPA to hierarchically cluster patterns of neural activation into groups which seem to match up pretty well with our common-sense understanding of categories in the world. Does the MVPA-based discovery of category structure imply knowledge on the part of the subject? Because we can see that a rhesus macaque’s brain responds with a distinct and recognizable pattern of activation when shown various pictures of cars, can we say that it has some knowledge of what a car is? How well correlated are MVPA-derived categorical structures with the reports of subjects regarding their own ideas about categories?
It may be possible to learn something about the acquisition of category discrimination from MVPA. One can learn to perceive things which initially appear imperceptible. It’d be fascinating to see how the categorical perception of different species evolved in terms of patterns of neural activation in a biologist-in-training; or to watch the ability to discriminate chords and their functional associations develop in a music student. Similarly, MVPA may tell us a lot about neural representation and memory.
Kay et al. suggest that it might be possible to reconstruct what a person is seeing from brain activity data alone (Kay et al. 2008). This may shake up debate about the nature of representation within the brain. All of these MVPA studies seem to vindicate a connectionist model of representation, where distributed patterns of activation across a neural network account for the content of mental states.
Most of the MVPA work I’ve discussed focuses on visual experiences as they occur to the subject in the moment. Presumably the brain contains some ephemeral, basically lossless representation of data being transmitted from the retina. Kay et al. used spatial location, orientation, and spatial frequency of as the dimensions of the analysis space in which they compared the similarity of patterns of activation (Kay et al. 2008). That these variables are useful in reconstructing information about the sensory experience of subjects does not necessarily mean that the subjects have conscious access to them. However, if the project of complete reconstruction of visual input from brain activity data is successful, it would seem likely that the parameterization used was at least a good approximation of the terms in which the part of the brain in question utilizes or perhaps makes available relevant information. If a successful model of the perception of syntax in natural language were created using MVPA techniques, it might provide some significant insight into how the brain acts as an information processor.
I think MVPA may also make some progress towards filling the “explanatory gap” claimed by some philosophers as an obstruction to the scientific understanding of the phenomenal nature of experience. For example, if we can use MVPA to examine the relative similarity of massively distributed—perhaps even whole-brain—activation patterns, and classify them in terms of qualitative, phenomenal reports from subjects, we may find a convincing neural correlate for “what it is like” to experience some fairly tricky things, e.g. what it is like to be in love, or to tell a lie. Having a love detector doesn’t necessarily explain why being in love feels the way it does, but comparing that pattern of activation to others, both similar and drastically dissimilar, might have some more explanatory power. Quite fancifully, for example, perhaps the pattern of activation which correlates with being in love is somewhere on a continuum between being hungry and looking at flowers. This is a little better than “it feels that way because it does.”
References:
- Norman, K.A.; Polyn, S.M.; Detre, G.J.; Haxby, J.V. (2006) Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Science Vol. 10, No. 9: 424-430.
- Kay, K.N.; Naselaris, T; Prenger, R.J.; Gallant, J.L. (2008) Identifying natural images from human brain activity. Nature 452: 352-356.