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

Thilakarathne, Dilhan J.

doi:10.1016/j.bica.2015.04.010

Cited by 8 publications

(6 citation statements)

References 57 publications

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“…Furthermore, neural underpinnings of PRAFA revealed areas of the limbic lobe (insular cortex and parahippocampal gyrus) and parietal cortex (precuneus and parietal operculum cortex). During SA-task, the perception and attention resources play a very important role in the behavioral outcome of the task (Thilakarathne, 2015). The limbic region has direct access to perceptual information prior to sensory cortical systems and also modulates innate behaviors, including motivation and avoidance behaviors (Nishijo, Rafal, & Tamietto, 2018).…”

Section: Discussionmentioning

confidence: 99%

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

2019

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Objective: In this study, the influence of pretask resting neural mechanisms on situational awareness (SA)-task is studied. Background: Pretask electroencephalography (EEG) information and Stroop effect are known to influence task engagement independently. However, neural mechanisms of pretask resting absolute alpha (PRAA) and pretask resting alpha frontal asymmetry (PRAFA) in influencing SA-task which is undergoing Stroop effect is still not understood. Method: The study involved pretask resting EEG measurements from 18 healthy individuals followed by functional magnetic resonance imaging (fMRI) acquisition during SA-task. To understand the effect of pretask alpha information and Stroop effect on SA, a robust correlation between mean reaction time, SA Index, PRAA, and PRAFA were assessed. Furthermore, neural underpinnings of PRAA, PRAFA in SA-task, and functional connectivity were analyzed through the EEG-informed fMRI approach. Results: Significant robust correlation of reaction time was observed with SA Index (Pearson: r = .50, pcorr = .05) and PRAFA (Pearson: r = .63; pcorr = .01), respectively. Similarly, SA Index significantly correlated with PRAFA (Pearson: r = .56, pcorr = .01; Spearman: r = .61, pcorr = .007), and PRAA (Pearson: r = .59, pcorr = .005; Spearman: r = .59, pcorr = .002). Neural underpinnings of SA-task revealed regions involved in visual-processing and higher-order cognition. PRAA was primarily underpinned at frontal-temporal areas and functionally connected to SA-task regions pertaining to the emotional regulation. PRAFA has correlated with limbic and parietal regions, which are involved in integration of visual, emotion, and memory information of SA-task. Conclusion: The results suggest a strong association of reaction time with SA-task and PRAFA and strongly support the hypothesis that PRAFA, PRAA, and associated neural mechanisms significantly influence the outcome of SA-task. Application: It is beneficial to study the effect of pretask resting information on SA-task to improve SA.

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Section: Discussionmentioning

confidence: 99%

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

2019

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“…A set of FOL rules are proposed in Table 2 so that an agent can recognize the FIRE and EVACUATE situations in the similar way as a human counterpart recognizes them. The preconditions (antecedents of FOL rules) used here are common among experts and have been suggested in earlier studies [8,21,49,56,57,[61][62][63][64][65]. Similar work is reported in [38] where the authors constructed decision trees based on some of the preconditions used in this study, such as the presence of hazard, route direction in PA that actuallyis a byproduct of understanding thePA.…”

Section: An Explanation Of the Fol Rulesmentioning

confidence: 90%

“…There are several sources that give intention a vital role in deliberation [4,62]. In rule #2, an agent must be listening to an alarm, which means she is paying attention to the alarm, and at the same time developing the deliberative intention [4], p. 56), due to deliberation that involves carefully listening the alarm, to moving to a (particular) muster location.…”

Section: An Explanation Of the Fol Rulesmentioning

confidence: 99%

On the realization of the recognition-primed decision model for artificial agents

Danial

Smith

Veitch

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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Naturalistic decision making (NDM) is a relatively new approach to decision-making that relies on situation awareness (SA) [20] rather than having a fixed set of principles from which to choose the best or optimal solution. One of the prominent models of NDM is Klein's [33] recognition-primed decision model. RPDM has a descriptive nature, and it requires a thorough understanding of philosophical concepts, such as intuition, perception, and mental simulation. The purpose of this study is to develop a method based on the theory of RPDM that can be implemented in an artificial agent.

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“…A set of FOL rules are proposed in Table 2 so that an agent recognizes these situations like the way a human counterpart recognizes them. The preconditions (antecedents of FOL rules) used here are common among experts and have been suggested in earlier studies [8,18,49,55,57,62,[64][65][66]68]. The query predicates determine the probability of recognizing alarms, having a FIRE situation, having an EVACUATE situation, and having some (unknown) situation given the evidence predicates.…”

Section: S2mentioning

confidence: 99%

“…The required muster station is referred to by the variable mloc that takes values from the set {MESSHALL, LIFEBOAT}. Literature shows that intention is an important cognitive state that affects one's ability to participate in a decision-making process [7,64]. Intention is modeled here as a predicate HITR that takes a value true if the agent develops the intention to move to mloc during a time interval t. An agent's intention can be inferred by observing which route is taken up immediately after listening to the alarm.…”

Section: Reasoningmentioning

confidence: 99%

Situation awareness modeling for emergency management on offshore platforms

Danial

Smith

Khan

et al. 2019

Hum. Cent. Comput. Inf. Sci.

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IntroductionThe present work proposes a model based on Markov Logic Network (MLN) [16] for representing emergency situations involving smoke and fire on offshore petroleum platforms. The model is tested for two important situations, FIRE and EVACUATE. In the FIRE situation, fire is observed due to smoke at some place on the platform, and all workers need to muster to their primary muster station. In the EVACUATE situation, the fire is escalated so that some escape routes to the primary muster station are blocked and all personnel needs to muster at the lifeboat or alternative muster station. The purpose of this work is to have a model that can be used by a software agent so that the agent can exhibit human-like situation awareness (SA). Such agents can subsequently be used, for example, in training simulators to enrich trainees' experience by showing them various scenarios in which the agent shows recognition of different situations (to makes various decisions). A participant can learn from the agent what information is important in a given scenario for correct SA.Representing the emergency response of agents operating in a virtual environment (VE) is a challenging and active research area. Emergencies on board can arise from Abstract Situation awareness is the first and most important step in emergency management. It is a dynamic step involving evolving conditions and environments. It is an area of active research. This study presents a Markov Logic Network to model SA focusing on fire accidents and emergency evacuation. The model has been trained using empirical data obtained from case studies. The case studies involved human participants who were trained for responding to emergencies involving fire and smoke using a virtual environment. The simulated (queried) and empirical findings are reasonably consistent. The proposed model enables implementing an agent that exploits environmental cues and cognitive states to determine the type of emergency currently being faced. Considering each emergency type as a situation, the model can be used to develop a repertoire of situations for agents so that the repertoire can act as an agent's experience for later decision-making. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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

Cited by 8 publications

References 57 publications

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

Effects of Neural Mechanisms of Pretask Resting EEG Alpha Information on Situational Awareness: A Functional Connectivity Approach

On the realization of the recognition-primed decision model for artificial agents

Situation awareness modeling for emergency management on offshore platforms

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