Bibliografías temáticas / Probability cueing

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Literatura académica sobre el tema "Probability cueing"

Autor: Grafiati
Publicado: 11 de marzo de 2023

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Artículos de revistas sobre el tema "Probability cueing"

1

Zhang, Yiting, Jianhua He, Siqi Tao, Wensheng Ji, and Libin Chen. "Research on Target Searching Strategy Using Mutual Cueing of Multi-sensor in Multi-platform." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 37, no. 2 (2019): 308–14. http://dx.doi.org/10.1051/jnwpu/20193720308.

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The cueing model for sensor cueing phased array radar is built in this paper to research the mutual cueing of multi-sensor in multi-platform for target searching. For the purpose of establishing the distribution probability density model of targets in cueing airspace, cueing error of multi-platform is derived with error transfer method. The successful cueing probability and false hand-off probability between platforms are analyzed, meanwhile the requirement of timeliness is defined. An optimized target searching strategy of cueing radar which adopts distance search through using the cueing center as starting point and controlling false hand-off probability of a single wave position is put forward. The simulation result illustrates that the searching strategy can significantly save radar search resources to capture target successfully, simultaneously decrease the false hand-off probability. The strategy can be applied to the design of dynamic programming software of multi-sensor in multi-platform for collaborative search, which requires accuracy and real-time capability.
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2

Girardi, Giovanna, Gabriella Antonucci, and Daniele Nico. "Cueing spatial attention through timing and probability." Cortex 49, no. 1 (2013): 211–21. http://dx.doi.org/10.1016/j.cortex.2011.08.010.

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Pang, Ce, Shucai Huang, Yan Zhao, Daozhi Wei, and Jinchang Liu. "Sensor Network Disposition Facing the Task of Multisensor Cross Cueing." Mathematical Problems in Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/7372013.

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In order to build the sensor network facing the task of multisensor crossing cueing, the requirements of initiating cueing and being cued are analyzed. Probability theory is used when building models, then probability of sensor cueing in the case of target moving is given, and, after that, the best distance between two sensors is calculated. The operational environment is described by normal distribution function. In the process of distributing sensor network, their elements, operational environment demand of cueing, and the probability of sensor network coverage are considered; then the optimization algorithm of sensor network based on hypothesis testing theory is made. The simulation result indicates that the algorithm can make sensor network which is required. On the basis of that, the two cases, including targets that make linear motion and orbit motion, are used to test the performance of the sensor network, which show that the sensor network can make uninterrupted detection on targets through multisensor cross cuing.
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4

홍인재 and Su Keun Jeong. "The properties and mechanism of probability cueing effect." Korean Journal of Cognitive and Biological Psychology 31, no. 1 (2019): 53–66. http://dx.doi.org/10.22172/cogbio.2019.31.1.004.

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Kabata, Takashi, and Eriko Matsumoto. "Cueing effects of target location probability and repetition." Vision Research 73 (November 2012): 23–29. http://dx.doi.org/10.1016/j.visres.2012.09.014.

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Hurst, Austin J., Michael A. Lawrence, and Raymond M. Klein. "How Does Spatial Attention Influence the Probability and Fidelity of Colour Perception?" Vision 3, no. 2 (2019): 31. http://dx.doi.org/10.3390/vision3020031.

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Existing research has found that spatial attention alters how various stimulus properties are perceived (e.g., luminance, saturation), but few have explored whether it improves the accuracy of perception. To address this question, we performed two experiments using modified Posner cueing tasks, wherein participants made speeded detection responses to peripheral colour targets and then indicated their perceived colours on a colour wheel. In E1, cues were central and endogenous (i.e., prompted voluntary attention) and the interval between cues and targets (stimulus onset asynchrony, or SOA) was always 800 ms. In E2, cues were peripheral and exogenous (i.e., captured attention involuntarily) and the SOA varied between short (100 ms) and long (800 ms). A Bayesian mixed-model analysis was used to isolate the effects of attention on the probability and the fidelity of colour encoding. Both endogenous and short-SOA exogenous spatial cueing improved the probability of encoding the colour of targets. Improved fidelity of encoding was observed in the endogenous but not in the exogenous cueing paradigm. With exogenous cues, inhibition of return (IOR) was observed in both RT and probability at the long SOA. Overall, our findings reinforce the utility of continuous response variables in the research of attention.
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7

Berggren, Nick, and Nazanin Derakshan. "Trait anxiety reduces implicit expectancy during target spatial probability cueing." Emotion 13, no. 2 (2013): 345–49. http://dx.doi.org/10.1037/a0029981.

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Ishibashi, Kazuya, and Shinichi Kita. "Probability Cueing Influences Miss Rate and Decision Criterion in Visual Searches." i-Perception 5, no. 3 (2014): 170–75. http://dx.doi.org/10.1068/i0649rep.

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9

HASEGAWA, Yuuki, Satoshi TAKAHASHI, Jiajia YANG, Qiong WU, Yoshimichi EJIMA, and Jinglong WU. "Study on attentional shift by probability cueing in peripheral visual field." Proceedings of Conference of Kyushu Branch 2019.72 (2019): G33. http://dx.doi.org/10.1299/jsmekyushu.2019.72.g33.

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10

Hitchcock, Edward M., William N. Dember, Joel S. Warm, Brian W. Moroney, and Judi E. See. "Effects of Cueing and Knowledge of Results on Workload and Boredom in Sustained Attention." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (1997): 1298–302. http://dx.doi.org/10.1177/1071181397041002127.

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Two accounts of the recently reported high workload associated with vigilance tasks (Warm, Dember, & Hancock, 1996) are the direct-cost and indirect-cost views. The former attributes this effect to the need for continuous observing in discriminating signals from noise; the latter attributes the effect to combating the boredom associated with vigilance tasks. These opposing views were tested by providing monitors with reliable cueing which rendered observing necessary only when low probability critical signals were imminent. On the basis of the direct-cost model, it was anticipated that cueing would lead to low workload but high boredom, since observers would have little to do during most of the vigil; the indirect-cost model would lead to a prediction of both high workload and high boredom. The results clearly supported the direct cost view that the workload of vigilance is task induced. Also as predicted from the direct cost account, cueing led to lower workload than did knowledge of results.
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