Verbal mimicry isn’t verbal (well, not lexical anyway)

One of my students, Carolyn Lamb, has been looking at deception in interrogation settings.

The Pennebaker model of deception, as devoted readers will know, is robust only for freeform documents. Sadly, the settings in which deception is often most interesting tend to be dialogues (law enforcement, forensic) and it’s known that the model doesn’t extend in any straightforward way to such settings.

We started out with the idea that responses would be mixtures of language elicited by the words in a question and freeform language from the respondent, and developed a clever method to separate them. Sadly, it worked, but it didn’t help. When the effect of question language was removed from answers, the differences between deceptive and truthful responses decreased.

Digging a little deeper, we were able to show that the influence of words from the question must impact response language at a higher level (i.e. earlier in the answer construction process than simply the lexical). Those who are being deceptive respond in qualitatively different ways to prompting words than those being truthful. A paper about this has been accepted for the IEEE Intelligence and Security Informatics Conference in Seattle next month.

Part of the explanation seems to be mirror neurons. There’s a considerable body of work on language acquisition, and on responses to single words, that uses mirror neurons as a big part of the explanation; I haven’t seen anything at an intermediate level where these results fit.

There are some interesting practical applications for interrogators. One strategy would be to reduce the presence of prompting words (and do so consistently across all subjects) so that responses become closer to statements, and so closer to freeform. My impression from my acquaintance is that smarter law enforcement personnel already know this and act on it.

But our results also suggest a new strategy: increase the number of prompting words because that tends to increase the separation between the deceptive and the truthful. This needs a good understanding of what kinds of response words to look for (and, for most, this has to be done offline because we as humans are terrible at estimating rates of words in real-time, especially function words). But it could be very powerful.


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