大众与个人审美品位的认知与神经机制

大众与个人的审美品位分别代表了审美活动在个体间的一致性和差异性。将大众品位和个人品位相结合的新趋势正在挑战传统的"普遍性"审美法则,并日益凸显审美反应的个体差异。存在诸多因素可以调节大众和个人审美品位的相对比例,包括刺激类型、专业性、文化背景、先前经验和年龄等。大众与个人审美品位的神经机制中,奖赏系统和默认模式网络扮演重要角色。目前,审美品位与审美加工模型的理论关系有待进一步厘清和验证。未来相关研究可以在拓展审美对象领域、完善大众与个人审美品位与不同审美加工阶段的对应关系等方向上继续开展。     

语言产生中词频效应老化的神经基础与时间进程

词频效应指语言产生中人们对高频词汇的加工比低频词汇更快更准确的一种现象,它可能发生在语言产生中的不同阶段。对青年人和老年人词频效应的不同特点和加工机制进行比较,可以考察语言产生的认知老化机制。通过语言产生理论可对词频效应的老化进行预测,提出词频效应在个体发展和老化阶段的相对稳定性,分析老化导致词频效应相关的神经基础和加工时间进程的改变。未来研究可进一步分离词频效应与习得年龄效应对语言产生老化的影响,并扩展至神经退行性疾病患者中。     

人眼深度运动知觉的研究

人类的深度运动知觉,是通过检测二维平面内的相对运动而形成的。本文在心理物理学实验基础上,揭示该机制的数学与生理学实现形式。视觉系统中存在对视平面内的相对运动敏感的神经元,构成深度运动的检测机制,产生单眼深度运动知觉与双眼深度运动知觉。在心理学上,视觉利用物体视角变化及大小知觉的恒常性判断深度运动。文中还初步讨论了人类的深度运动知觉与蜜蜂,家蝇等昆虫的深度运动知觉的区别。     

试评外周神经生理效应的函数之争

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     

Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks

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.     

Unmasking a shady mirror effect: Recognition of normal versus obscured faces

Hockley, Hemsworth, and Consoli (1999) Hockley, W. E., Hemsworth, D. E. and Consoli, A. 1999. Shades of the mirror effect: Recognition of faces with and without sunglasses. Memory & Cognition, 27: 128–138. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] 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) Hockley, W. E., Hemsworth, D. E. and Consoli, A. 1999. Shades of the mirror effect: Recognition of faces with and without sunglasses. Memory & Cognition, 27: 128–138. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] 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) Hockley, W. E., Hemsworth, D. E. and Consoli, A. 1999. Shades of the mirror effect: Recognition of faces with and without sunglasses. Memory & Cognition, 27: 128–138. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar] is actually two separable effects.     

An aphasia database on the internet: a model for computer-assisted analysis in aphasiology

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.     

Genetic counseling for a family with two distinct anomalies: A case report of a neural tube defect and 5p- syndrome in a fetus

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.     

Coding infant engagement in the Face-to-Face Still-Face paradigm using deep neural networks

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.