signal probability
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2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lu-xia Jia ◽  
Xiao-jing Qin ◽  
Ji-fang Cui ◽  
Qi Zheng ◽  
Tian-xiao Yang ◽  
...  

AbstractSchizotypy, a subclinical group at risk for schizophrenia, has been found to show impairments in response inhibition. However, it remains unclear whether this impairment is accompanied by outright stopping (reactive inhibition) or preparation for stopping (proactive inhibition). We recruited 20 schizotypy and 24 non-schizotypy individuals to perform a modified stop-signal task with electroencephalographic (EEG) data recorded. This task consists of three conditions based on the probability of stop signal: 0% (no stop trials, only go trials), 17% (17% stop trials), and 33% (33% stop trials), the conditions were indicated by the colour of go stimuli. For proactive inhibition (go trials), individuals with schizotypy exhibited significantly lesser increase in go response time (RT) as the stop signal probability increasing compared to non-schizotypy individuals. Individuals with schizotypy also exhibited significantly increased N1 amplitude on all levels of stop signal probability and increased P3 amplitude in the 17% stop condition compared with non-schizotypy individuals. For reactive inhibition (stop trials), individuals with schizotypy exhibited significantly longer stop signal reaction time (SSRT) in both 17% and 33% stop conditions and smaller N2 amplitude on stop trials in the 17% stop condition than non-schizotypy individuals. These findings suggest that individuals with schizotypy were impaired in both proactive and reactive response inhibition at behavioural and neural levels.


2020 ◽  
Vol 114 ◽  
pp. 113877
Author(s):  
Y.Q. Aguiar ◽  
F. Wrobel ◽  
J.-L. Autran ◽  
P. Leroux ◽  
F. Saigné ◽  
...  

Author(s):  
Adnane El Hanjri ◽  
Aawatif Hayar ◽  
Abdelkrim Haqiq

<p>The Fifth Generation of Mobile Networks (5G) is changing the cellular network infrastructure paradigm, and Small Cells are a key piece of this shift. But the high number of Small Cells and their low coverage involve more Handovers to provide continuous connectivity, and the selection, quickly and at low energy cost, of the appropriate one in the vicinity of thousands is also a key problem. In this paper, we propose a new method, to have an efficient, blind and rapid handover just by analysing Received Signal probability density function instead of demodulating and analysing Received Signal itself as in classical handover. The proposed method exploits KL Distance, Akaike Information Criterion (AIC) and Akaike weights, in order to decide blindly the best handover and the best Base Station (BS) for each user</p>


2019 ◽  
Author(s):  
Arianna Zuanazzi ◽  
Uta Noppeney

AbstractIn our natural environment, the brain needs to combine signals from multiple sensory modalities into a coherent percept. While spatial attention guides perceptual decisions by prioritizing processing of signals that are task-relevant, spatial expectations encode the probability of signals over space. Previous studies have shown that behavioral effects of spatial attention generalize across sensory modalities. However, because they manipulated spatial attention as signal probability over space, these studies could not dissociate attention and expectation or assess their interaction.In two experiments, we orthogonally manipulated spatial attention (i.e., task-relevance) and expectation (i.e., signal probability) selectively in one sensory modality (i.e., primary modality) (experiment 1: audition, experiment 2: vision) and assessed their effects on primary and secondary sensory modalities in which attention and expectation were held constant.Our results show behavioral effects of spatial attention that are comparable for audition and vision as primary modalities; yet, signal probabilities were learnt more slowly in audition, so that spatial expectations were formed later in audition than vision. Critically, when these differences in learning between audition and vision were accounted for, both spatial attention and expectation affected responses more strongly in the primary modality in which they were manipulated, and generalized to the secondary modality only in an attenuated fashion. Collectively, our results suggest that both spatial attention and expectation rely on modality-specific and multisensory mechanisms.


2018 ◽  
Vol 28 (02) ◽  
pp. 1950032
Author(s):  
Xingjian Xu ◽  
Tian Ban ◽  
Yuehua Li

Reliability evaluation by using probabilistic computational models has become an important research field in modern digital designs. Based on the profound understanding of different reliability evaluation methods, this paper proposes a universal model for signal probability and reliability analysis of logic circuits. The proposed Signal Probability Level Matrix (SPLM) provides us with the reliability and signal probability of the entire circuit as well as individual outputs. We can deal with SPLM very flexibly depending on different applications and design constraints. The accuracy and efficiency of the proposed model have been proved and verified by representative circuits in literatures. Furthermore, the proposed model is particularly useful in reliability assessment in cascade-structure circuits such as ripple carry adders.


Author(s):  
Ben D. Sawyer ◽  
Peter A. Hancock

Objective: This work assesses the efficacy of the “prevalence effect” as a form of cyberattack in human-automation teaming, using an email task. Background: Under the prevalence effect, rare signals are more difficult to detect, even when taking into account their proportionally low occurrence. This decline represents diminished human capability to both detect and respond. As signal probability (SP) approaches zero, accuracy exhibits logarithmic decay. Cybersecurity, a context in which the environment is entirely artificial, provides an opportunity to manufacture conditions enhancing or degrading human performance, such as prevalence effects. Email cybersecurity prevalence effects have not previously been demonstrated, nor intentionally manipulated. Method: The Email Testbed (ET) provides a simulation of a clerical email work involving messages containing sensitive personal information. Using the ET, participants were presented with 300 email interactions and received cyberattacks at rates of either 1%, 5%, or 20%. Results: Results demonstrated the existence and power of prevalence effects in email cybersecurity. Attacks delivered at a rate of 1% were significantly more likely to succeed, and the overall pattern of accuracy across declining SP exhibited logarithmic decay. Application: These findings suggest a “prevalence paradox” within human-machine teams. As automation reduces attack SP, the human operator becomes increasingly likely to fail in detecting and reporting attacks that remain. In the cyber realm, the potential to artificially inflict this state on adversaries, hacking the human operator rather than algorithmic defense, is considered. Specific and general information security design countermeasures are offered.


2017 ◽  
Author(s):  
Arianna Zuanazzi ◽  
Uta Noppeney

AbstractSpatial attention and expectation are two critical top-down mechanisms controlling perceptual inference. Based on previous research it remains unclear whether their influence on perceptual decisions is additive or interactive.We developed a novel multisensory approach that orthogonally manipulated spatial attention (i.e. task relevance) and expectation (i.e. signal probability) selectively in audition and evaluated their effects on observers’ responses in vision. Critically, while experiment 1 manipulated expectation directly via the probability of task-relevant auditory targets across hemifields, experiment 2 manipulated it indirectly via task-irrelevant auditory non-targets.Surprisingly, our results demonstrate that spatial attention and signal probability influence perceptual decisions either additively or interactively. These seemingly contradictory results can be explained parsimoniously by a model that combines spatial attention, general and spatially selective response probabilities as predictors with no direct influence of signal probability. Our model provides a novel perspective on how spatial attention and expectations facilitate effective interactions with the environment.


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