Modelling of situation awareness with perception, attention, and prior and retrospective awareness

• Published in: Biologically inspired cognitive architectures 2015-04, Vol.12, p.77-104
• Main Author: Thilakarathne, Dilhan J.
• Format: Article
• Language: English
• Subjects: Bottom-up; Cognitive modelling; Prior and retrospective awareness; Situation awareness; Top-down
• ISSN: 2212-683X; 2212-6848
• DOI: 10.1016/j.bica.2015.04.010

Human awareness under different circumstances is complex and non-trivial to understand. Nevertheless, due to the importance of awareness for safety and efficiency in many domains (e.g., the aviation domain), it is necessary to study the processes behind situation awareness, to eliminate possible errors in action selection that may lead to disasters. Interesting models for situation awareness have been presented, mainly from an ecological psychology perspective, but they are debatable with respect to the latest neurocognitive evidences. With the developments in brain imaging and recording techniques, more and more detailed information on complex cognitive processes becomes available. This provides room to further investigate the mechanisms behind many cognitive phenomena, including situation awareness. This paper presents a computational cognitive agent model for situation awareness from the perspective of action selection, which is inspired by neurocognitive evidences. The model integrates bottom-up and top-down cognitive processes, related to various cognitive states: perception, desires, attention, intention, (prior and retrospective) awareness, ownership, feeling, and communication. Based on the model, various cognitive effects can be explained, such as perceptual load, predictive processes, inferential processes, cognitive controlling, unconscious bias, and conscious bias. A model like this will be useful in domains that benefit from complex simulations of socio-technical systems (e.g. the aviation domain) based on computational models of human behaviour. In such domains, existing agent-based simulations are limited, since most of the agent models do not include realistic nature-inspired processes. The validity of the model is illustrated based on simulations for the aviation domain, focusing on a particular situation where an agent has biased perception, poor comprehension, habitual driven projection, and conflict between prior and retrospective effects on action execution.