Finding Universal Markers for Deception

Dr. Matsumoto, director of Humintell and renowned psychologist has been appropriated by the Air Force Research Laboratory (who is also funding this endeavor) to find universal markers for deception.

Dr. Matsumoto, a current Professor of Psychology at San Francisco State University and co-creator of the original microexpression training tool, has been researching human emotions and nonverbal behaviors for several decades.

Wired Magazine recently reported on this new venture that is backed by the chief Air Force scientist, Dr. Mark Maybury.  Dr. Maybury envisions a social radar military program that will aid in the diversion of wars by delineating the societal and cultural elements of war.

“We’re supposed to provide ISR [the military acronym for intelligence surveillance and reconnaissance}. But our constituents, ‘Don’t just give me a weather forecast, [they] give me an enemy movement forecast.  ‘What’s that about?’ That’s human behavior. And so [we need to] understand what motivates individuals, how they behave,” Maybury purports.

The military has probed into all other types of information gathering prospects, human behavior is the constant throughout; therefore, tapping into the “human domain” rather than just relying on advanced machines (which do have their advantages) is possibly the next step in world peace.

This new type of program will concentrate on human behavior and include computer technology that will analyze aspects of social media such as Twitter feed, Facebook timelines and political polls.

It is only fair to note that there is much criticism from high ranking officials in the military who think that being able to see a person’s intention is not realistic.

Maybury retorts, “Just like nobody could imagine seeing through the night or seeing through water, nobody can imagine seeing attitudes. And actually, in my view, that’s very much a future reality.”

What are your thoughts? Do you think this social radar really is a practical tool that the military should be actively pursuing?

4 responses to “Finding Universal Markers for Deception”

  1. Keith D. says:

    I think it would be foolish NOT TO investigate this stuff. I also think it’s important to have realistic expectations. This field is in its relative infancy, so really major breakthroughs are probably still decades away, but that didn’t stop us from putting a man on the moon, or prevent us from developing computers that are orders of magnitude more powerful than those that made the moon landing possible carried around in everyone’s pocket as a commodity “smart” communications device today.

    All of those kinds of advancements were made one step at a time. We didn’t get there by saying to ourselves, “we can only jump a couple feet into the air, it’s not realistic to think we can land on the moon.”

  2. Russ Conte says:

    I agree with Keith D., it would be folly NOT to pursue this type of research. I’m completely sure that analyzing social media will give very useful data – if the methods of finding that data can be known. Determining those algorithms to sort through that data to find what is relevant will be a job any great mathematician would love (I’m serious about that). I would predict that we have at our disposal a huge amount of data, and the problem is sorting it all out, and finding the nuggets of gold that will give us the patterns we seek. I am sure it can be done, and I’m excited about the prospect that the research is moving forward!

    Russ Conte

  3. Keith – your point about the fact that this type of research is in its infancy is very insightful, but like you point out that does not negate its importance/relevance.

    Ross – Your point about the amount of data and the task of sorting that information is very important to remember. A daunting task perhaps but one some might find intriguing as well.

    Thank you for all your comments,. Humintell appreciates the feedback.

  4. John Burik says:

    We’re not really after deception detection. If that’s our outcome, my guess is we’re going to get a lot false positives (which may explain Levine’s (2010) research). What we’re really after is intention or reality detection, eh?

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