A Functional Model of Sensemaking in a Neurocognitive Architecture
Table 1
Rules for inferring category likelihoods based on knowledge of category centroid location and an observed feature.
Features
Rules
HUMINT
If an unknown event occurs, then the likelihood of the event belonging to a given category decreases as the distance from the category centroid increases.
IMINT
If an unknown event occurs, then the event is four times more likely to occur on a Government versus Military building if it is from category A or B. If an unknown event occurs, then the event is four times more likely to occur on a Military versus Government building if it is from category C or D.
MOVINT
If an unknown event occurs, the event is four times more likely to occur in dense versus sparse traffic if it is from category A or C. If an unknown event occurs, the event is four times more likely to occur in sparse versus dense traffic if it is from category B or D.
SIGINT
If SIGINT on a category reports chatter, then the likelihood of an event by that category is seven times as likely as an event by each other category. If SIGINT on a category reports silence, then the likelihood of an event by that category is one-third as likely as an event by each other category.
SOCINT
If an unknown event occurs, then the likelihood of the event belonging to a given category is twice as likely if it is within that category’s boundary (represented as a colored region on the display).