fMRI, Lie Detection, and Statistics

I'm blogging from the AALS Mid-Year Conference on Evidence in Cleveland, where I just moderated a discussion this morning on fMRI and Lie Detection featuring Steve Laken (Cephos Corp.) and Mike Pardo (Alabama).  Although the studies on fMRI lie detection have their limitations, the results so far are quite impressive, with accuracy rates in the 90% range.  One wonders how soon they will make their way into court, where admissibility questions loom large.  Even if the technology is in fact sufficiently reliable for Daubert (and what I saw this morning suggests that this is true), the inherent conservatism of the legal system, coupled with the bias against analogs to polygraphs, will make admissibility a tough hurdle for the technology.  (For more on the bias against mind-reading devices, see this Note written by my student Leo Kittay.)

One striking aspect of the various discussions on fMRI during and after the session was the focus that people had on mechanism.  Many people are concerned that researchers have not yet pinpointed specific areas of the brain associated with lying, or have not determined specific pathways for deception.  Often, they are similarly concerned that other brain activities may "light up" the same regions.  I'm skeptical, however, that these concerns really matter.  While it may be desirable and interesting to know the specific mechanisms associated with deception, we really don't need to make such discoveries to have a practically useful lie detection machine.  All that matters is that some model exists (here, presumably using brain scans) that can with reasonable accuracy separate liars from non-liars.  How the model does that is in many ways beside the point.  As Laken pointed out during the discussion, medical researchers often have little or idea about the specific mechanism for a drug's success, yet such a limitation never prevents us from using its therapeutic benefits as proven through statistical/epidemiological studies.

--EKC