Attentional Biases for Negative Interpersonal Stimuli in Clinical Depression.

An information-processing paradigm was used to examine attentional biases in clinically depressed participants, participants with generalized anxiety disorder (GAD), and nonpsychiatric control participants for faces expressing sadness, anger, and happiness. Faces were presented for 1,000 ms, at which point depressed participants had directed their attention selectively to depression-relevant (i.e., sad) faces. This attentional bias was specific to the emotion of sadness; the depressed participants did not exhibit attentional biases to the angry or happy faces. This bias was also specific to depression; at 1,000 ms, participants with GAD were not attending selectively to sad, happy, or anxiety-relevant (i.e., angry) faces. Implications of these findings for both the cognitive and the interpersonal functioning of depressed individuals are discussed and directions for future research are advanced. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

When danger lurks in the background: Attentional capture by animal fear-relevant distractors is specific and selectively enhanced by animal fear.

Across 2 experiments, a new experimental procedure was used to investigate attentional capture by animal fear-relevant stimuli. In Experiment 1 (N = 34), unselected participants were slower to detect a neutral target animal in the presence of a spider than a cockroach distractor and in the presence of a snake than a large lizard distractor. This result confirms that phylogenetically fear-relevant animals capture attention specifically and to a larger extent than do non-fear-relevant animals. In Experiment 2 (N = 86), detection of a neutral target animal was slowed more in the presence of a feared fear-relevant distractor (e.g., a snake for snake-fearful participants) than in presence of a not-feared fear-relevant distractor (e.g., a spider for snake-fearful participants). These results indicate preferential attentional capture that is specific to phylogenetically fear-relevant stimuli and is selectively enhanced in individuals who fear these animals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

What do we learn from binding features? Evidence for multilevel feature integration.

Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their aftereffects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects disappeared with increasing practice, presumably because of the fact that only 1 of the features involved was relevant to the task. However, this instability was only observed for arbitrary, not highly overlearned combinations of simple geometric features and not for real objects (colored pictures of a banana and strawberry), where binding effects were strong and resistant to practice. These findings suggest that learning has no direct impact on the strength or resistance of bindings or on speed with which features are bound; however, learning does affect the amount of attention particular feature dimensions attract, which again can influence which features are considered in binding. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A Multinomial Model of Event-Based Prospective Memory.

Prospective memory is remembering to perform an action in the future. The authors introduce the 1st formal model of event-based prospective memory, namely, a multinomial model that includes 2 separate parameters related to prospective memory processes. The 1st measures preparatory attentional processes, and the 2nd measures retrospective memory processes. The model was validated in 4 experiments. Manipulations of instructions to place importance on either the prospective memory task or the background task (Experiments 1 and 2) and manipulations of distinctiveness of prospective memory targets (Experiment 2) had expected effects on model parameters, as did a manipulation of the difficulty of prospective memory target encoding (Experiments 3 and 4). An alternative model was also evaluated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Symbolic control of visual attention: The role of working memory and attentional control settings.

This study examined how 1 symbol is selected to control the allocation of attention when several symbols appear in the visual field. In Experiments 1-3, the critical target feature was color, and it was found that uninformative central arrows that matched the color of the target were selected and produced unintentional shifts of attention (i.e., involuntary, initiated slowly, producing long-lasting facilitatory effects). Experiment 4 tested whether such selection is the result of an attentional filter or of a competition bias due to a match of incoming information against integrated object representations stored in working memory. Here, the critical feature was shape and color was irrelevant, but matching color arrows were still selected. Thus, features of objects in working memory will bias the selection of symbols in the visual field, and such selected symbols are capable of producing unintentional shifts of attention. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Attentional task aptitude and performance anxiety.

Anxious responding (trait, state, and test anxiety) has a negative impact on overt performance. Researchers have used a unidimensional method of assessing anxiety and performance, although a more informative approach would involve a comprehensive assessment battery and multiple performance tasks. Incorporating this strategy, the authors measured the impact of anxiety on 4 attentional processing tasks. Results revealed that "types" of anxiety symptoms were differentially related to attentional task performance; test anxiety accounted for the most variance in predicting performance on the Wechsler Adult Intelligence Scale-Third Edition (D. Wechsler, 1997) letter-number sequencing and digit-span subtests, trait anxiety and fear of negative evaluation were more significant in predicting Stroop performance, and math anxiety accounted for the largest variance toward understanding Paced Auditory Serial Addition Task (C. W. Lejuez, C. W. Kahler, & R. A. Brown, 2003) scores. Theoretical and clinical implications are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Interpreting the negative mood-helping literature via "mega"-analysis: A contrary view.

"Mega"-analysis was developed by M. Carlson and N. Miller (see record 1987-31249-001) as an extension of traditional meta-analytic procedures for conducting integrative reviews of existing research literatures. One such mega-analysis was conducted by Carlson and Miller to synthesize the literature on the relation between negative mood states and helping. That analysis found no support for a theoretical account (negative state relief) that had been confirmed previously by using various experimental approaches. In an attempt to reconcile the discrepancy, the logic and methods used in Carlson and Miller's mega-analysis of the negative mood-helping literature were examined, and several serious problems were found. These problems are discussed, and data are presented to show that the results of that mega-analysis, and perhaps all mega-analyses, should not be viewed with confidence. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于眨眼持续时间的司机疲劳检测方法

利用红外敏感的摄像机获得司机脸部图像,通过可变性模板匹配的方法对眼睛进行定位,然后利用卡尔曼滤波的方法跟踪眼睛,得到司机的眨眼持续时间参数,以此为依据判断司机是否疲劳.主要研究了其中的图像处理方法--特征提取算法和眼睛定位、跟踪算法.实验结果证明,眨眼持续时间判断是否疲劳的有效指标.     

RTDNet：面向高分辨率卫星影像的赤潮探测网络

目的 赤潮是一种常见的海洋生态灾害,严重威胁海洋生态系统安全。及时准确获取赤潮的发生和分布信息可以为赤潮的预警和防治提供有力支撑。然而,受混合像元和水环境要素影响,赤潮分布精细探测仍是挑战。针对赤潮边缘探测的难点,结合赤潮边缘高频特征学习与位置语义,提出了一种计算量小、精度高的网络模型RTD-Net （red tide detection network）。方法 针对赤潮边缘探测不准确的问题,设计了基于RIR （residual-in-residual）结构的网络,以提取赤潮边缘水体的高频特征;利用多感受野结构和坐标注意力机制捕获赤潮水体的位置语义信息,增强赤潮边缘水体的细节信息并抑制无用的特征。结果 在GF1-WFV （Gaofen1 wide field of view）赤潮数据集上的实验结果表明,所提出的RTDNet模型赤潮探测效果不仅优于支持向量机（support vector machine,SVM）、U-Net、Deep-Labv3+及HRNet （high-resolution network）等通用机器学习和深度学习模型,而且也优于赤潮指数法GF1_RI （Gaofen1 red tide index ）以及赤潮探测专用深度学习模型RDU-Net （red tide detection U-Net）,赤潮误提取、漏提取现象明显减少,F1分数在两幅测试图像上分别达到了0.905和0.898,相较于性能第2的模型DeepLabv3+提升了2%以上。而且,所提出的模型参数量小,仅有2.65 MB,约为DeepLabv3+的13%。结论 面向赤潮探测提出一种基于RIR结构的赤潮深度学习探测模型,通过融合多感受野结构和注意力机制提升了赤潮边缘探测的精度和稳定性,同时有效降低了计算量。本文方法展现了较好的应用效果,可适用于不同高分辨率卫星影像的赤潮探测。     

自适应加权的多特征融合ECT图像重建算法

为了解决电容层析成像(electrical cpacitance tomography, ECT)图像重建中电容值与介电常数这一非线性病态问题,提出一种自适应加权的多特征融合(adaptive weighted multi-feature fusion, AWMF) ECT图像重建算法,利用网络模型拟合电容张量和介电常数的非线性映射关系。首先,在网络模型中采用密集卷积网络(DenseNet),不仅缓解梯度消失现象,还融合不同通道的特征信息;添加挤压激励网络(squeeze excitation network, SENet)自适应调整特征通道的权重,用以提取不同通道的关键特征,提高重建图像的精度。其次,构建树形聚合结构（tree aggregation structure network, TASN）网络模块,扩大感受野并提取丰富的多尺度特征信息,消除普通卷积所带来的伪影现象。在COMSOL5.3软件上建模仿真后,通过MATLAB2014a对图像进行重建。实验结果表明,重建图像误差系数降低至0.0256,相关系数提高至0.9717,与传统算法和CNN算法相比,具有更高的图像重建质量。