A Bayesian-optimal principle for learner-friendly adaptation in learning games

Adaptive learning games should provide opportunities for the student to learn as well as motivate playing until goals have been reached. In this paper, we give a mathematically rigorous treatment of the problem in the framework of Bayesian decision theory. To quantify the opportunities for learning, we assume that the learning tasks that yield the most information about the current skills of the student, while being desirable for measurement in their own right, would also be among those that are efficient for learning. Indeed, optimization of the expected information gain appears to naturally avoid tasks that are exceedingly demanding or exceedingly easy as their results are predictable and thus uninformative. Still, tasks that are efficient for learning may be experienced as too challenging, and the resulting failures can lower motivation. Therefore, in addition to quantifying the expected informational benefit for learning of any prospective task to be presented next, we also model the expected motivational cost of its presentation, measured simply as the estimated probability of failure in our example model. We propose a “learner-friendly” adaptation algorithm that chooses the learning tasks by optimizing the expected benefit divided by the expected cost. We apply this algorithm to a Rasch-like student model implemented within a real-world application and present initial results of a pilot experiment.     

Network Hubs and Opportunity for Complex Thinking Among Young British Muslims

This study examines whether individuals in a network esteem peers who think in integratively complex ways about religious issues in the context of a small‐group educational course comprised of young British Muslims. Integrative complexity (IC) measures the degree to which an individual's information processing is characterized by (a) rigid, black‐and‐white thinking or (b) ability to recognize the validity of, and integrate, multiple perspectives. A novel measurement procedure was developed for this research called the Social Field Generator. Results from seven groups (n = 55) showed that (a) participants with levels of IC were described by their peers with more positive sentiment than their low‐IC counterparts; (b) the higher the IC scores of participants, the closer peers felt toward them; and (c) the highest IC individuals were consistently selected as sources of advice, whereas the lowest IC individuals were not viewed as sources of advice. This research shows that within an educational environment aimed at promoting complex thinking, group processes and grassroots religious leadership can encourage higher levels of IC as a group norm.     

Hierarchical models for relational event sequences

Interaction within small groups can often be represented as a sequence of events, each event involving a sender and a recipient. Recent methods for modeling network data in continuous time model the rate at which individuals interact conditioned on the previous history of events as well as actor covariates. We present a hierarchical extension for modeling multiple such sequences, facilitating inferences about event-level dynamics and their variation across sequences. The hierarchical approach allows one to share information across sequences in a principled manner—we illustrate the efficacy of such sharing through a set of prediction experiments. After discussing methods for adequacy checking and model selection for this class of models, the method is illustrated with an analysis of high school classroom dynamics from 297 sessions.     

Task-load manipulation in the Symbol Digit Modalities Test: An alternative measure of information processing speed

ObjectiveTo evaluate the utility of an oral fMRI-adapted version of the Symbol Digit Modalities Test (SDMT) to assess information processing speed (IPS) using three different interstimulus intervals (ISI).MethodsNineteen right-handed healthy controls performed the adapted version of the SDMT, consisting of a block design that had a total of 6 control/activation block pairs with 3 different ISIs (1.5, 2 and 2.5 s) presented in two different runs: in ascending and descending orders. The brain activation patterns during different ISIs were assessed by effective functional connectivity analysis based on independent component analysis.ResultsAs expected, all conditions yielded activations in the fronto-parietal networks (FPNs) related to attention processes. Shorter ISIs (1.5 and 2 s) not only yielded greater patterns of connectivity within fronto-parietal and occipital regions such as the FPN and fronto-occipital network (FON), but also recruited more functional networks overall. Task performance at the shortest ISI was negatively correlated with connectivity at the FPN and activity of the pre-supplementary motor area extending to the cingulate gyrus.ConclusionIncreasing IPS demands due to shorter ISIs resulted in an increased level and number of functional networks required, increased connectivity within the FPN and FON, and enhancement of the prefrontal cortex. IPS does not arise from activity of a single b area but from affective information transfer among distant cortical regions of the frontal and parietal cortices. This adapted version of the SDMT may be useful for studying alterations of IPS in clinical and nonclinical populations.     

Birth of an Abstraction: A Dynamical Systems Account of the Discovery of an Elsewhere Principle in a Category Learning Task

Human participants and recurrent (“connectionist”) neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular (“strong”) classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks’ encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non‐connectionist, rule‐based accounts. The results reveal that the networks “contain” structures related to mechanisms posited by rule‐based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models.     

Being and feeling liked by others: How social inclusion impacts health

This study examined the effects of perceived and actual social inclusion on health across and within individuals from a network perspective. During the first semester, 75 freshmen students provided bi-weekly ratings on their perceived social inclusion and health. To capture actual social inclusion, each student nominated liked and disliked fellow students. Perceived social inclusion mediated the effect of actual social inclusion on health. Specifically, students with more ‘likes’ perceived more social inclusion and those with higher perceived inclusion reported a better health status (between-person effect). In addition, at time points, when students received more ‘likes’ they also perceived more social inclusion. They reported better health at times when they felt more included (within-person effect). Thus, the perception of social inclusion is rooted in reality and actual social inclusion has an impact on health when passing the filter of perception.     

视觉词汇加工的动态神经网络及其形成

揭示大脑加工的神经网络机制成为认知神经科学研究的最新取向.本研究以视觉词汇加工脑区(VWFA)的神经功能作为切入点,探讨视觉词汇加工神经网络的动态机制及其形成.研究一考察VWFA在刺激驱动和任务调节下的动态激活,及其与语音、语义脑区所组成神经网络的动态机制.研究二通过跨文化对比以及儿童阅读发展研究,阐明语言经验对视觉词汇加工网络的塑造作用.研究三对比功能网络、静息网络以及白质纤维束联结,探讨视觉词汇加工网络的动态联结及其形成.研究结果有助于建构视觉词汇加工的神经生理模型,为基于脑科学的阅读教学和阅读障碍矫治奠定理论基础,为认知神经科学研究提供了新的思路.     

伤害回避的脑机制及其生物基础

伤害回避是指个体对厌恶刺激信号做出强烈的反应,并学会被动地回避惩罚的一种倾向,这一倾向使得个体反复思考未来的结局,并谨慎小心地对待不确定情景中的事件,进而更有可能诱发情感障碍。伤害回避涉及的神经网络包括三个子网络,即额顶叶-前扣带皮层的连接、皮层-杏仁核的连接和白质通道的结构性连接,这三个子网络分别与羞怯感-易疲劳性、预期担心以及不确定环境中的害怕情绪有关。而其生物基础则包括单一基因多态性和基因多态间的交互作用。今后的研究应该集中在深化伤害回避神经网络与生物基础间的联合机制、研究三种及以上基因多态间的交互作用、考察其他因素对基因效应的调节作用、注重伤害回避四种亚型相关神经网络之间的连接、探讨5-HT4等其他几种5-羟色胺受体多态性与伤害回避的关系以及分析伤害回避内部机制在抗抑郁治疗中的作用等方面。     

聋校低年级学生班级友伴网络建构及其特点研究— 一项两年的追踪研究

本文采用调查法与社会网络分析法对聋校三年级聋生在班级群体中的友伴网络进行2年的追踪研究,以探索友伴网络的建构及其动态发展特征。结果表明：①三年级初期,聋生在友伴网络的个体中心度与个体的外貌、成绩与年龄等个人特征有显著相关;但随年级升高,这种相关性逐渐消失。②聋生友伴网络中存在一个十分稳定的中心小团体,其团体成员在整个网络中处于中心地位。③聋生在班级群体中的互选朋友关系呈现出中等程度的稳定性。