ARC Research Lunch

2011-12
ARC Research Lunch
Sean Webb, Partner, Adams Inc., North Carolina
January 30, 2012

In the process of outlining the emotional processing within the cognitive mind, it seems that we have accidentally stumbled upon the rule sets and algorithms that will enable computers to define, track and predict human emotions in individual users based on cognitive attachments of the mind in relation to contextually defined external stimuli, such as social media news feeds and other quantifiable informational interactions as tracked via their online activity.

We have pored through 10 years of cognitive emotion studies, all of which indirectly support the rule sets and fundamental model we are assuming, and at this point we expect a focused psychology survey to support the macro components of the model and overall process as well.

One of the most interesting aspects of the model is that it fully lends itself to logical computation, making the definition, tracking and prediction of human emotion possible from a computational perspective.

However, that said, as you can imagine, there are a number of rabbit holes that need exploring in this potentially new psychological/computer science.

For instance, we have identified three potential immediate issues:

  1. We will need to have a focused psych survey to prove out the logical emotion definitions, as that they will be under great scrutiny. Bottom line: beyond the model being accurate at 100% with anecdotal examples, and beyond having 10 years of cognitive emotional studies that indirectly support the model; we are going to need to put some reviewable science behind testing of the macro model from a 'this-is-how-people-actually-work-across-the-globe' perspective.
  2. The creation of elegant algorithms based on the process flowcharts we've created.
  3. A portion of the emotional analysis process is dependent on the individual's perception of the validity of the source of the incoming information.

Why this work is important:

  1. The latest studies show that negative emotional states negatively influences attention to non-related content, meaning the results could increase click-throughs of ads into the billions in revenue.
  2. Tracking and predicting emotional states among users could enable proactive systems designed to assist emotionally troubled teens and other individuals.