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     

The Influence of Posttreatment Mutual Help Group Participation on the Friendship Networks of Substance Abuse Patients

The effect of 12-step mutual help groups (e.g., Narcotics Anonymous) on members' friendship networks has received little attention. This 1-year longitudinal study examined such effects in a sample of 2,337 male substance abuse inpatients, 57.7% of whom became significantly involved in 12-step activities (e.g., reading program literature, attending meetings) after treatment. An a priori model of the interplay of 12-step involvement and friendship networks was tested using structural equation modeling, and found to have excellent fit to the data. Twelve-step group involvement after treatment predicted better general friendship characteristics (e.g., number of close friends) and substance abuse-specific friendship characteristics (e.g., proportion of friends who abstain from drugs and alcohol) at follow-up. Results are discussed in terms of how mutual help group involvement benefits patients and how the self-help group evaluation paradigm should be broadened.     

The effects of nodality on the formation of equivalence classes

A four-member equivalence class (A → B → C → D) can be formed by training AB, BC, and CD. The nodal stimuli, B and C, mediate all of the derivative (transitive and equivalence) relations in the class. The derivative relations AC, CA, BD, and DB are separated by one node, whereas AD and DA are separated by two nodes. How do the number of nodes that separate the stimuli in a derivative relation influence the induction of stimulus control exerted by that relation? Seven college students learned two four-member classes made up of nonsense syllables. After training, all derivative relations were presented repeatedly without informative feedback. Stimulus control exerted by each derivative relation was assessed concurrently. For the 7 subjects, control exerted by the derivative relations increased gradually with repeated presentations. With 6 of the 7 subjects, the one-node relations exerted more control than the two-node relations during the process. However, the disparity between the one- and two-node relations decreased with repeated presentations. Eventually, all derivative relations exerted complete control. The control exerted by derivative relations during induction was inversely related to the number of nodes separating the terms in the derivative relations. These results demonstrate that nodal distance is a determinant of the relatedness of stimuli in equivalence classes. The findings are discussed in terms of remote association, semantic memory networks, and the study of transitive inference.     

Shy teens and their peers: Shyness in respect to basic personality traits and social relations

The main purpose of this paper was to examine shyness in teenagers from two perspectives: in terms of its relations with basic personality traits and in terms of its influence on the processes that occur in the social networks of high school students. First, we found that shyness was negatively predicted by extraversion and positively by neuroticism. Second, using exponential random graph models we demonstrated that shyness across network effects was similar to reversed extraversion (introversion): Both negatively predicted the number of outgoing relations, whereas they did not affect the number of incoming relations. We discuss the issue of locating shyness in the space of personality traits, supporting the relevance of ascribing it to introversion.     

Developmental stuttering and the role of the supplementary motor cortex

Developmental stuttering is a frequent neurodevelopmental disorder with a complex neurobiological basis. Robust neural markers of stuttering include imbalanced activity of speech and motor related brain regions, and their impaired structural connectivity. The dynamic interaction of cortical regions is regulated by the cortico-basal ganglia-thalamo-cortical system with the supplementary motor area constituting a crucial cortical site. The SMA integrates information from different neural circuits, and manages information about motor programs such as self-initiated movements, motor sequences, and motor learning. Abnormal functioning of SMA is increasingly reported in stuttering, and has been recently indicated as an additional “neural marker” of DS: anatomical and functional data have documented abnormal structure and activity of the SMA, especially in motor and speech networks. Its connectivity is often impaired, especially when considering networks of the left hemisphere. Compatibly, recent data suggest that, in DS, SMA is part of a poorly synchronized neural network, thus resulting in a likely substrate for the appearance of DS symptoms. However, as evident when considering neural models of stuttering, the role of SMA has not been fully clarified. Herein, the available evidence is reviewed, which highlights the role of the SMA in DS as a neural “hub”, receiving and conveying altered information, thus “gating” the release of correct or abnormal motor plans.     

Teaming up with temps: the impact of temporary workers on team social networks and effectiveness

Temporary workers offer immediate benefits to the bottom line; yet, it is unclear how incorporating temporary workers into teams affects how they function. We apply social identity theory to propose that temporary workers significantly reduce individual- and team-level networks and team effectiveness but that commitment to the leader and intergroup competition can help temporary and permanent employees work together more effectively. Using a sample of employees nested in teams (Study 1, n = 312), we found that status differences affected member interactions resulting in sparser advice and friendship networks for temporary workers compared to their permanent counterparts. At the team level (Study 2, n = 58), these team member differences or contract diversity impacted team functioning through advice networks, such that, teams with greater contract diversity had sparser networks and were less effective. Further, commitment to the leader was found to moderate the negative impact of contract diversity on advice and friendship network density. With the increasing use of temporary worker and the prevalent use of teams, these findings have broader implications for HR functions and present possible avenues to mitigate the negative consequences of temporary workers.     

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.