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).