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Shan et al. (2012) propose an emergency response decision support system (ERDSS) as an important tool for authorities to enhance their emergency response capabilities.
These log-transformed response time errors were significantly related to an overall tendency to respond quickly in pattern response decision making (r = −.648, p = .004), and subject Stroop T scores (r = −.564, p = .015), suggesting that these response time errors may be a behavioral indicator of impulsive responding.
Focal-attentional selection, in turn, mediates high-level stimulus analysis and response decision processes.
Since overall binding patterns do not appear to account for the selective cellular responses to different treatments, we investigated whether p53 post-translational modifications could be involved in the cellular response decision.
Note that the P3 event was unlikely to be accounted for by motor action or response decision because its mean amplitude was not correlated to reaction times (R2 WR) = .03; R2(PR) = .08; R2(WH) = .63; R2(PH) = .30).30
If the difficulty hypothesis is correct, then the BOLD response of frontoparietal regions should be predicted by the proximity of the currently presented object to the 50% target/distractor decision boundary, where the response decision is at its most ambiguous (figure 1).
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This study could be helpful for emergency response decision-making and loss prevention.
Compared with other response decision-making models, the response decision-making model consists of not only the response measure decision-making process but also response time decision-making process that is firstly proposed in the paper.
The response decision-making model is self-adaptive and has the ability of tolerating to false positive IDS alerts.
An intrusion response decision-making model based on hierarchical task network (HTN) planning is presented in the paper.
CDEM personnel need to quickly and accurately comprehend often complex scientific information for rapid response decision-making during a volcanic crisis.
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