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101.
伪忽视是指个体存在的轻微偏左的不对称空间注意,而视觉空间伪忽视和表征伪忽视是其两种主要表现形式。研究初期,研究者认为两种伪忽视基于相同的注意定向左偏机制,但近年来研究发现,它们存在神经机制的差异。本文主要从两种伪忽视神经机制的异同出发,梳理近期研究结果,以期增进对伪忽视的理解。未来研究可以从认知时间进程角度或设计更为完善的研究范式进一步探讨这两种伪忽视神经机制的异同。 相似文献
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大量的神经生理学实验久已证实由刺激引起的外周神经生理效应具有类似于心理物理效应的高度压缩现象。从外周神经纤维中观察到的神经脉冲频率或发生电位是描述这种压缩效应的常用方法。半个多世纪以来一部分研究人员观察到压缩效应遵守刺激强度的线性对数函数,而另一部分研究人员则观察到幂函数。这种传统实验分歧,通过笔者前时建立的刺激一效应方程,得到合理的说明和两者的统一 相似文献
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Driving is a highly complex task that involves the execution of multiple cognitive tasks belonging to different levels of abstraction. Traffic emerges from the interaction of a big number of agents implementing those behaviours, but until recent years, modelling it by the interaction of these agents in the so called micro-simulators was a nearly impossible task as their number grows. However, with the growing computing power it is possible to model increasingly large quantities of individual vehicles according to their individual behaviours. These models are usually composed of two sub-models for two well-defined tasks: car-following and lane-change. In the case of lane-change the literature proposes many different models, but few of them use Computational Intelligence (CI) techniques, and much less use personalization for reaching individual granularity. This study explores one of the two aspects of the lane-change called lane-change acceptance, where the driver performs or not a lane-change given his intention and the vehicle environment. We demonstrate how the lane-change acceptance of a specific driver can be learned from his lane change intention and surrounding environment in an urban scenario using CI techniques such as feed-forward Artificial Neural Network (ANN). We work with Multilayer Perceptron (MLP) and Convolutional Neural Networks (CNN) architectures. How they perform one against the other and how the different topologies affect both to the generalization of the problem and the learning process are studied. 相似文献
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《Quarterly journal of experimental psychology (2006)》2013,66(4):739-759
Hockley, Hemsworth, and Consoli (1999) found that following the study of normal faces, a recognition test of normal faces versus faces wearing sunglasses produced a mirror effect: The sunglasses manipulation decreased hit rates and increased false-alarm rates. The stimuli used by Hockley et al. (1999) consisted of separate poses of models wearing or not wearing sunglasses. In the current experiments, we separately manipulated same versus different depictions of individual faces and whether or not the faces were partially obscured. The results of a simulation and four experiments suggest that the test-based, mirror effect observed by Hockley et al. (1999) is actually two separable effects. 相似文献
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A web-based software model was developed as an example for data mining in aphasiology. It is used for educating medical and engineering students. It is based upon a database of 254 aphasic patients which contains the diagnosis of the aphasia type, profiles of an aphasia test battery (Aachen Aphasia Test), and some further clinical information. In addition, the cerebral lesion profiles of 147 of these cases were standardized by transferring the coordinates of the lesions to a 3D reference brain based upon the ACPC coordinate system. Two artificial neural networks were used to perform a classification of the aphasia type. First, a coarse classification was achieved by using an assessment of spontaneous speech of the patient which produced correct results in 87% of the test cases. Data analysis tools were used to select four features of the 30 available test features to yield a more accurate diagnosis. This classifier produced correct results in 92% of the test cases. The neural network approach is similar to grouping performed in group studies, while the nearest-neighbor method shows a design more similar to case studies. It finds the neurolinguistic and the lesion data of patients whose AAT profiles are most similar to the user's input. This way lesion profiles can be compared to each other interindividually. The Aphasia Diagnoser is available on the Web address http://fuzzy.iau.dtu.dk/aphasia.nsf and thus should facilitate a discussion about the reliability and possibilities of data-mining techniques in aphasiology. 相似文献
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A couple presented for genetic counseling because of an elevated maternal serum alpha fetoprotein. Ultrasound examination revealed the presence of a neural tube defect. The couple declined an amniocentesis, but chose serial ultrasound evaluations instead. Ultrasounds eventually identified microcephaly, but the couple continued to decline amniocentesis. After the child's birth, the diagnosis of 5p- syndrome was made. The couple's decision not to have an amniocentesis allowed the family their right to autonomy; however, prenatal chromosome analysis would have provided this couple with a great deal more prognostic information. We discuss the conflict between a counselor's duty to respect a client's freedom vs. duty to care for a client's welfare. We address issues of nondirective counseling and the need for more studies looking at the decision-making process in prenatal diagnosis. 相似文献
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BackgroundThe Face-to-Face Still-Face (FFSF) task is a validated and commonly used observational measure of mother-infant socio-emotional interactions. With the ascendence of deep learning-based facial emotion recognition, it is possible that common complex tasks, such as the coding of FFSF videos, could be coded with a high degree of accuracy by deep neural networks (DNNs). The primary objective of this study was to test the accuracy of four DNN image classification models against the coding of infant engagement conducted by two trained independent manual raters.Methods68 mother-infant dyads completed the FFSF task at three timepoints. Two trained independent raters undertook second-by-second manual coding of infant engagement into one of four classes: 1) positive affect, 2) neutral affect, 3) object/environment engagement, and 4) negative affect.ResultsTraining four different DNN models on 40,000 images, we achieved a maximum accuracy of 99.5% on image classification of infant frames taken from recordings of the FFSF task with a maximum inter-rater reliability (Cohen's κ-value) of 0.993.LimitationsThis study inherits all sampling and experimental limitations of the original study from which the data was taken, namely a relatively small and primarily White sample.ConclusionsBased on the extremely high classification accuracy, these findings suggest that DNNs could be used to code infant engagement in FFSF recordings. DNN image classification models may also have the potential to improve the efficiency of coding all observational tasks with applications across multiple fields of human behavior research. 相似文献