Evaluation of Simulated Recognition Aids for Human Sensemaking in Applied Surveillance Scenarios

Hum Factors. 2022 Sep 3;187208221120461. doi: 10.1177/00187208221120461. Online ahead of print.

Abstract

Objective: We determined whether the human capability for sensemaking, or for identifying essential elements of information (EEIs), could be enhanced by a simulated recognition aid that directed attention to people and vehicles in scenes or by a simulated recognition aid that directed attention to EEIs.

Background: For intelligence analysts, sensemaking is challenging because it frequently involves making inferences about uncertain data. One way to enhance sensemaking may involve collaboration from a machine recognition aid such as Project Maven, an established algorithm that directs analysts' attention to people and vehicles in scenes. We simulated the directed attention of Project Maven as well as a machine recognition aid that directed attention to EEIs.

Method: We created full-motion videos of simulated compounds viewed by an overhead camera. Sensemaking was assessed by measuring participants' ability to predict events and identify signs. Participants' attention was directed by placing small globe symbols above either all people and vehicles, or all EEIs. Novices and intelligence analysts participated.

Results: Simulated recognition aids directing participants' attention to EEIs improved EEI identification but directing attention to people and vehicles (emulating Project Maven) did not. Participants' sensemaking was not enhanced by either type of simulated recognition aid.

Conclusion: Guiding attention to features in a scene improves their identification whereas indiscriminate steering of attention to entities in the scene does not improve understanding of the holistic meaning of events, unless attention is drawn to relevant signs of those events.

Application: Results contribute to our goal of determining which human-machine systems improve the sensemaking capability of intelligence analysts in the field.

Keywords: decision making; human performance modeling; human-automation interaction; information processing; synthetic task environments.