scholarly journals The impact of prior and ongoing threat on the false alarm threshold for facial discrimination

Author(s):  
Santiago Papini ◽  
Joseph E. Dunsmoor ◽  
Jasper A.J. Smits
2019 ◽  
Author(s):  
Santiago Papini ◽  
Joseph E. Dunsmoor ◽  
Jasper A. J. Smits

Perceptual adaptations facilitate rapid responses to threats but can come with the cost of false alarms, or the failure to discriminate safe or novel stimuli from signals of true threat. For example, a fatigued colleague might be avoided when their tired expression is interpreted as a scowl, or a glimpse at a stranger might cause a rush of anxiety if they resemble a known adversary. We examined false alarms in the context of facial cues, which can become exaggerated signals of threat across anxiety disorders. In Experiment 1, ongoing threat lowered the false alarm threshold for discrimination based on anger intensity compared to prior and no threat. In Experiment 2, prior and ongoing threat each lowered the false alarm threshold for identity-based facial discrimination compared to no threat. These results could be relevant for anxiety disorders in which excessive false alarms may contribute to overgeneralized threat responses.


2021 ◽  
Vol 503 (4) ◽  
pp. 5223-5231
Author(s):  
C F Zhang ◽  
J W Xu ◽  
Y P Men ◽  
X H Deng ◽  
Heng Xu ◽  
...  

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.


2020 ◽  
Vol 642 ◽  
pp. A157 ◽  
Author(s):  
N. Meunier ◽  
A.-M. Lagrange

Context. The detectability of exoplanets and the determination of their projected mass in radial velocity are affected by stellar magnetic activity and photospheric dynamics. Among those processes, the effect of granulation, and even more so of supergranulation, has been shown to be significant in the solar case. The impact for other spectral types has not yet been characterised. Aims. Our study is aimed at quantifying the impact of these flows for other stars and estimating how such contributions affect their performance. Methods. We analysed a broad array of extended synthetic time series that model these processes to characterise the impact of these flows on exoplanet detection for main sequence stars with spectral types from F6 to K4. We focussed on Earth-mass planets orbiting within the habitable zone around those stars. We estimated the expected detection rates and detection limits, tested the tools that are typically applied to such observations, and performed blind tests. Results. We find that both granulation and supergranulation on these stars significantly affect planet mass characterisation in radial velocity when performing a follow-up of a transit detection: the uncertainties on these masses are sometimes below 20% for a 1 MEarth (for granulation alone or for low-mass stars), but they are much larger in other configurations (supergranulation, high-mass stars). For granulation and low levels of supergranulation, the detection rates are good for K and late G stars (if the number of points is large enough), but poor for more massive stars. The highest level of supergranulation leads to a very poor performance, even for K stars; this is both due to low detection rates and to high levels of false positives, even for a very dense temporal sampling over 10 yr. False positive levels estimated from standard false alarm probabilities sometimes significantly overestimate or underestimate the true level, depending on the number of points: it is, therefore, crucial to take this effect into account when analysing observations. Conclusions. We conclude that granulation and supergranulation significantly affect the performance of exoplanet detectability. Future works will focus on improving the following three aspects: decreasing the number of false positives, increasing detection rates, and improving the false alarm probability estimations from observations.


2021 ◽  
Author(s):  
Thomas Röösli ◽  
David N. Bresch

<p>Weather extremes can have high socio-economic impacts. Better impact forecasting and preventive action help to reduce these impacts. In Switzerland, the winter windstorms caused high building damage, felled trees and interrupted traffic and power. Events such as Burglind-Eleanor in January 2018 are a learning opportunity for weather warnings, risk modelling and decision-making.</p><p>We have developed and implemented an operational impact forecasting system for building damage due to wind events in Switzerland. We use the ensemble weather forecast of wind gusts produced by the national meteorological agency MeteoSwiss. We couple this hazard information with a spatially explicit impact model (CLIMADA) for building damages due to winter windstorms. Each day, the impact forecasting system publishes a probabilistic forecast of the expected building damages on a spatial grid.</p><p>This system produces promising results for major historical storms when compared to aggregated daily building insurance claims data from a public building insurer of the canton of Zurich. The daily impact forecasts were qualitatively categorized as (1) successful (2) miss or (3) false alarm. The impacts of windstorm Burglind-Eleanor and five other winter windstorms were forecasted reasonably well, with four successful forecasts, one miss and one false alarm.</p><p> The building damage due to smaller storm extremes was not as successfully forecasted. Thunderstorms are not as well forecasted with 2 days’ lead time and as a result the impact forecasting system produces more misses and false alarms outside the winter storm season. For the Alpine-specific southerly Foehn winds, the impact forecasts produce many false alarms, probably caused by an overestimation of wind gusts in the weather forecast.</p><p>The forecasting system can be used to improve weather warnings and allocate resources and staff in the claims handling process of building insurances. This will help to improve recovery time and costs to institutions and individuals. The open-source code and open meteorological data makes this implementation transferable to other hazard types and other geographical regions.</p>


2019 ◽  
Vol 11 (3) ◽  
pp. 549-563 ◽  
Author(s):  
JungKyu Rhys Lim ◽  
Brooke Fisher Liu ◽  
Michael Egnoto

Abstract On average, 75% of tornado warnings in the United States are false alarms. Although forecasters have been concerned that false alarms may generate a complacent public, only a few research studies have examined how the public responds to tornado false alarms. Through four surveys (N = 4162), this study examines how residents in the southeastern United States understand, process, and respond to tornado false alarms. The study then compares social science research findings on perceptions of false alarms to actual county false alarm ratios and the number of tornado warnings issued by counties. Contrary to prior research, findings indicate that concerns about false alarm ratios generating a complacent public may be overblown. Results show that southeastern U.S. residents estimate tornado warnings to be more accurate than they are. Participants’ perceived false alarm ratios are not correlated with actual county false alarm ratios. Counterintuitively, the higher individuals perceive false alarm ratios and tornado alert accuracy to be, the more likely they are to take protective behavior such as sheltering in place in response to tornado warnings. Actual country false alarm ratios and the number of tornado warnings issued did not predict taking protective action.


Robotica ◽  
2010 ◽  
Vol 29 (3) ◽  
pp. 421-432 ◽  
Author(s):  
R. E. Mohan ◽  
W. S. Wijesoma ◽  
C. A. A. Calderon ◽  
C. J. Zhou

SUMMARYEstimating robot performance in human robot teams is a vital problem in human robot interaction community. In a previous work, we presented extended neglect tolerance model for estimation of robot performance, where the human operator switches control between robots sequentially based on acceptable performance levels, taking into account any false alarms in human robot interactions. Task complexity is a key parameter that directly impacts the robot performance as well as the false alarms occurrences. In this paper, we validate the extended neglect tolerance model for two robot tasks of varying complexity levels. We also present the impact of task complexity on robot performance estimations and false alarms demands. Experiments were performed with real and virtual humanoid soccer robots across tele-operated and semi-autonomous modes of autonomy. Measured false alarm demand and robot performances were largely consistent with the extended neglect tolerance model predictions for both real and virtual robot experiments. Experiments also showed that the task complexity is directly proportional to false alarm demands and inversely proportional to robot performance.


2004 ◽  
Vol 7 (1) ◽  
pp. 26-39 ◽  
Author(s):  
Sanjeev Agarwal ◽  
R. Joe Stanley ◽  
Satish Somanchi

2012 ◽  
Vol 19 (4) ◽  
pp. 753-761 ◽  
Author(s):  
Yanlong Cao ◽  
Yuanfeng He ◽  
Huawen Zheng ◽  
Jiangxin Yang

In order to reduce the false alarm rate and missed detection rate of a Loose Parts Monitoring System (LPMS) for Nuclear Power Plants, a new hybrid method combining Linear Predictive Coding (LPC) and Support Vector Machine (SVM) together to discriminate the loose part signal is proposed. The alarm process is divided into two stages. The first stage is to detect the weak burst signal for reducing the missed detection rate. Signal is whitened to improve the SNR, and then the weak burst signal can be detected by checking the short-term Root Mean Square (RMS) of the whitened signal. The second stage is to identify the detected burst signal for reducing the false alarm rate. Taking the signal's LPC coefficients as its characteristics, SVM is then utilized to determine whether the signal is generated by the impact of a loose part. The experiment shows that whitening the signal in the first stage can detect a loose part burst signal even at very low SNR and thusly can significantly reduce the rate of missed detection. In the second alarm stage, the loose parts' burst signal can be distinguished from pulse disturbance by using SVM. Even when the SNR is −15 dB, the system can still achieve a 100% recognition rate


2020 ◽  
Vol 17 (1) ◽  
pp. 9-23
Author(s):  
Eva D. Regnier

Emergency managers must make high-stakes decisions regarding preparation for tropical storms when there is still considerable uncertainty regarding the storm’s impacts. Forecast quality improves as lead time until the forecast events declines. Reducing the lead time required for preparation decisions can substantially improve the quality of forecasts available for decision making and thereby, reduce the expected total costs of preparations plus storm damage. Measures of forecast quality are only indirectly linked to their value in preparation decisions and changes in the parameters of those decisions—in particular lead time. This paper provides decision-relevant measures of the quality of recent National Hurricane Center forecasts from the 2014–2018 seasons, which can be used to evaluate reductions in decision lead time in terms of false alarm rate, missed detections, and expected annual costs. For decision makers in some regions with decision lead times of 48–72 hours—typical for evacuation decisions—every 6-hour reduction in required lead time can reduce the false alarm rate by more than 10%.


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