Children’s scale errors are a natural consequence of learning to associate objects with actions: A computational model

Young children sometimes attempt an action on an object, which is inappropriate because of the object size—they make scale errors. Existing theories suggest that scale errors may result from immaturities in children's action planning system, which might be overpowered by increased complexity of object representations or developing teleofunctional bias. We used computational modelling to emulate children's learning to associate objects with actions and to select appropriate actions, given object shape and size. A computational Developmental Deep Model of Action and Naming (DDMAN) was built on the dual‐route theory of action selection, in which actions on objects are selected via a direct (nonsemantic or visual) route or an indirect (semantic) route. As in case of children, DDMAN produced scale errors: the number of errors was high at the beginning of training and decreased linearly but did not disappear completely. Inspection of emerging object–action associations revealed that these were coarsely organized by shape, hence leading DDMAN to initially select actions based on shape rather than size. With experience, DDMAN gradually learned to use size in addition to shape when selecting actions. Overall, our simulations demonstrate that children's scale errors are a natural consequence of learning to associate objects with actions.     

Feature selection using Ant Colony Optimization (ACO) and Road Sign Detection and Recognition (RSDR) system

Road Sign Detection and Recognition (RSDR) is aimed to enable drivers maintain basic functionality with the aim of identifying and notifying driver through the existing restrictions so that the process is a success on the present widened road. Examples for RSDR include ‘traffic light ahead’ or ‘pedestrian crossing’ signs. An innovative RSDR system has been introduced which comprises of pre-processing, edge detection, feature extraction, features selection and Ensemble Fuzzy Support Vector Machine (EFSVM) classifier. Feature selection is carried out successfully by deployment of Ant Colony Optimization (ACO) algorithm to determine most prominent and definitive features. These features are then fed into the ensemble SVM to enable both road side traffic detection as well as recognition. Suggested system’s performance is analyzed and evaluated with respect to road signs having a capable recognition rate.     

How Otto did not extend his mind,but might have: Dynamic systems theory and social-cultural group selection

Proponents of cognitive Situationism argue that the human mind is embodied, embedded in both natural and social-cultural environments and extended creating both extended and distributed cognition. Anti-situationists reject all or some of these claims. I argue that four major objections to extended cognition: (1) the mark of the cognitive, (2) the function-identity fallacy, (3) cognitive bloat, and (4) scientific irrelevance lose much of their sting in the case of distributed cognition, the extension of cognitive agency to a group of cognitive agents, such as a scientific research team. However, I claim that a crucial fifth challenge, that advocates of the extended mind commit the causal-constitution fallacy, has yet to be satisfactorily addressed. I focus on Spyridon Palermos’ use of dynamic systems theory to refute this charge and I argue that his appeal to dynamic systems theory as a way of understanding system-constitution fails. Instead, I suggest a social-cultural group selection hypothesis for understanding system-constitution. But, I leave it for another day to elaborate that hypothesis’ empirical plausibility.     

The psychological coping,learning potential and career preferences profiles of operational force military candidates

The aim of this study was to profile the psychological coping, learning potential and career-related interests of 251 candidates for operational force military selection for the South African National Defence Force (SANDF) – 26 of whom were successful in the selection. Data on their sense of meaning, learning potential and career-related interest were gathered using standardised measures supplemented with open-ended responses. The data were analysed to compare the profiles of the successful candidates against those not selected. The selected group showed significantly higher mean scores on meaningfulness and learning potential, and lower scores for career-related interest in law and medicine. Findings suggest that psychological coping, learning potential and career-related preference information can be used to refine the selection of operational force military candidates.     

青少年偏差行为的同伴选择和影响效应:基于纵向社会网络的元分析

以22篇纵向社会网络分析研究文献(30项独立样本研究,27746个被试)为对象,使用元分析技术探讨青少年偏差行为的同伴选择和同伴影响效应假设。结果发现:(1)同伴选择和同伴影响效应都显著,但同伴影响效应显著大于同伴选择效应;(2)青少年发展阶段在同伴选择和同伴影响过程中存在显著的调节效应。在同伴选择过程中,青少年早期阶段不显著,而中、晚期阶段显著;在同伴影响过程中,青少年早、中期显著,晚期阶段不显著。(3)偏差行为类型在同伴选择和同伴影响效应中的调节效应不显著。(4)网络数量在同伴影响过程中存在显著的调节效应,随着网络数量增加同伴影响效应越可能成立。结论,中晚期青少年会基于偏差行为相似性选择朋友,早中期青少年会受朋友的影响而产生偏差行为。     

个体特征因素对师徒之间选择倾向的影响

师徒的相互选择过程是师徒关系研究中的一个重要问题。本文采用三个实验探讨了影响师徒相互选择倾向的个体特征因素。实验一和实验二探讨了徒弟选师傅时,师傅的外向性、成就动机水平、管理职务和性别一致性对徒弟选择师傅倾向的影响。实验三探讨了师傅选择徒弟时,徒弟的外向性、成就动机水平和性别一致性对师傅选择徒弟倾向的影响。实验结果表明在徒弟选择师傅时,师傅的外向性、成就动机、管理职务,是影响因素;在师傅选择徒弟时,徒弟的外向性、成就动机水平是影响因素。     

Accommodating Small Sample Sizes in Three-Level Models When the Third Level is Incidental

Small samples sizes are a pervasive problem when modeling clustered data. In two-level models, this problem has been well studied, and several resources provide guidance for modeling such data. However, a recent review of small-sample clustered data methods has noted that no studies have investigated methods for modeling three-level data with small sample sizes. Furthermore, strategies for two-level models do not necessarily translate to the three-level context. Moreover, three-level models are prone to small samples because the “small sample” designation is primarily based on the sample size of the highest level, and large samples are increasingly difficult to amass as one progresses up a hierarchy. In this study, we focus on the case when the third level is incidental, meaning that the third level is important to consider but there are no explicit research questions at the third level. This study performs a simulation study to examine the performance of seven methods for modeling three-level data with a small sample at the third level. A motivating educational psychology example is also provided to demonstrate how the choice of method can greatly affect results.     

Double Decomposition of Level-1 Variables in Multilevel Models: An Analysis of the Flynn Effect in the NSLY Data

This paper introduces an extension of cluster mean centering (also called group mean centering) for multilevel models, which we call “double decomposition (DD).” This centering method separates between-level variance, as in cluster mean centering, but also decomposes within-level variance of the same variable. This process retains the benefits of cluster mean centering but allows for context variables derived from lower level variables, other than the cluster mean, to be incorporated into the model. A brief simulation study is presented, demonstrating the potential advantage (or even necessity) for DD in certain circumstances. Several applications to multilevel analysis are discussed. Finally, an empirical demonstration examining the Flynn effect (Flynn, 1987 Flynn, J. R. (1987). Massive IQ gains in 14 nations: What IQ tests really measure. Psychological Bulletin, 101(2), 171. https://doi.org/10.1037/h0090408.[Crossref], [Web of Science ®] , [Google Scholar]), our motivating example, is presented. The use of DD in the analysis provides a novel method to narrow the field of plausible causal hypotheses regarding the Flynn effect, in line with suggestions by a number of researchers (Mingroni, 2014 Mingroni, M. A. (2014). Future efforts in Flynn effect research: Balancing reductionism with holism. Journal of Intelligence, 2(4), 122. https://doi.org/10.3390/jintelligence2040122.[Crossref] , [Google Scholar]; Rodgers, 2015 Rodgers, J. L. (2015). Methodological issues associated with studying the Flynn effect: Exploratory and confirmatory efforts in the past, present, and future. Journal of Intelligence, 3(4), 111. https://doi.org/10.3390/jintelligence3040111. [Crossref] , [Google Scholar]).