At a high level, theories of human information processing can be viewed as collections of if-then statements: If the subject is in a particular cognitive state, this should be associated with a
particular set of outcomes. To test these theories, experimenters attempt to put subjects in a particular cognitive state and then
observe neural activity and behavior in that state. However, our ability (as experimenters) to control a subject's cognitive state is
limited; there is almost always variability in subjects’ cognitive state, above and beyond the variability that is directly driven by the
experimental manipulation. In analyses that focus on comparing experimental conditions, this extra variability is treated as a source of noise and
(as such)may make it harder to see the predicted effect. Multivariate pattern analysis (MVPA) gives us a way of addressing this problem: Instead of simply assuming that experimental conditions are effective in eliciting the cognitive state of interest, we can train a pattern classifier to recognize the pattern of neural activity associated with a cognitive state, and (subsequently) we can use the classifier
to track fluctuations in that cognitive state over time. In paradigms where there is extensive uncontrolled variance in subjects’ cognitive state, this approach gives us a much more
sensitive way of testing theories of how cognitive states drive behavior. I will describe how we have used MVPA in my laboratory to test theories
of how neural activation drives brain plasticity and how subjects strategically guide memory search; I will also discuss (in more general
terms) how to construct appropriate experimental designs for MVPA studies. I will conclude by discussing shortcomings of current methods and some particularly challenging directions
for future research.
Audio (MP3 File, Podcast Ready)
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