Abstract
| Language | English |
|---|---|
| Pages | 84-94 |
| Journal | Lecture Notes in Computer Science |
| Volume | 9101 |
| DOIs | |
| Publication status | Published - 2015 |
| Event | 28th International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems - Seoul, South Korea Duration: 1 Jan 2015 → 1 Jan 2015 |
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Bibliographical note
Proceedings title: Current Approaches in Applied Artificial IntelligencePublisher: Springer
Place of publication: Seoul, South Korea
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A Neurologically Inspired Model of the Dynamics of Situation Awareness Under Biased Perception. / Thilakarathne, D.J.
In: Lecture Notes in Computer Science, Vol. 9101, 2015, p. 84-94.Research output: Contribution to Journal › Article › Academic › peer-review
TY - JOUR
T1 - A Neurologically Inspired Model of the Dynamics of Situation Awareness Under Biased Perception
AU - Thilakarathne, D.J.
N1 - Proceedings title: Current Approaches in Applied Artificial Intelligence Publisher: Springer Place of publication: Seoul, South Korea
PY - 2015
Y1 - 2015
N2 - This paper presents a computational cognitive agent model of Situation Awareness (SA), 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, awareness, ownership, feeling, and communication. The emphasis is on explaining the cognitive basis for biased perception in SA, which is considered to be the most frequent factor in poor SA (the reason for 76% of poor SA errors), through perceptual load. A model like this will be useful in applications which relay on complex simulations (e.g. aviation domain) that need computational agents to represent human action selection together with cognitive details. 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.
AB - This paper presents a computational cognitive agent model of Situation Awareness (SA), 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, awareness, ownership, feeling, and communication. The emphasis is on explaining the cognitive basis for biased perception in SA, which is considered to be the most frequent factor in poor SA (the reason for 76% of poor SA errors), through perceptual load. A model like this will be useful in applications which relay on complex simulations (e.g. aviation domain) that need computational agents to represent human action selection together with cognitive details. 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.
U2 - 10.1007/978-3-319-19066-2_9
DO - 10.1007/978-3-319-19066-2_9
M3 - Article
VL - 9101
SP - 84
EP - 94
JO - Lecture Notes in Computer Science
T2 - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
ER -