Learning new names for new objects: cortical effects as measured by magnetoencephalography

We tracked the evolvement of naming-related cortical dynamics with magnetoencephalography when five normal adults successfully learned names and/or meanings of unfamiliar objects. In all subjects, the learning of new names was associated with pronounced cortical effects. The learning effect was of long latency and emerged as a change of activation in the same cortical network that was active during naming of familiar items. In four out of five subjects, the cortical learning effect occurred in the inferior parietal lobe. In three of these subjects, the cortical effect was left-sided. These results suggest that the inferior parietal lobe plays an important role in the acquisition of novel words, presumably as a part of working memory systems.     

What kind of tensions are involved in the pedagogical use of religious narratives? Perspectives from Finnish Evangelic Lutheran and Islamic religious education

Abstract

In this article, we examine how narratives of religious traditions are used as resources in religious education (RE) and compare practices from Evangelic Lutheran religious education (LRE) and Islamic religious education (IRE) in Finnish public schools. The sacredness of narratives from holy books entails that there can be contestations over their pedagogical use, and teachers need to negotiate the possible tensions between the pedagogical aims of liberal RE and the integrity of the holy narratives. The research data consists of interviews with teachers of LRE (n = 4) and IRE (n = 5) as well as classroom observations. The results of qualitative content analysis show that teachers use narratives as pedagogical resources when teaching about and from religion. However, tensions occurred between the ideals of student-centred, experiential and creative learning, on one hand, and respecting the sacredness of the narratives, on the other. We also present teachers’ ways of negotiating the tensions as well as some differences between LRE and IRE in the pedagogical use of narratives.     

Child's behaviour in mother–child interaction predicts later emotional and behavioural problems

The aims were to examine the association of maternal or child interactive behaviour with emotional and behavioural problems of the child simultaneously and 3 years later, and to assess whether there is continuity in children's emotional and behavioural symptoms from 2 to 5 years. Sixty‐five 2‐year‐old children with their mothers were videotaped during a feeding situation. Their mothers completed the CBCL at 2 years and when the children were 5. Simultaneously, there were no strong correlations between child's emotional and behavioural problems and maternal or child interactive behaviour. Subsequently, mothers' higher sensitivity and more optimal structuring, as well as child's higher involvement of the mother, were associated with lower levels of child externalizing and total problem scores. Also, less responsive children showed more externalizing symptoms 3 years later. There was continuity of emotional and behavioural problems from 2 to 5 years. Problems in mother–child interaction may predict behavioural problems in the child subsequently. Besides maternal behaviour, it is important to consider the interactive behaviour of the child. Children who are less responsive and who show less involvement of the mother are more likely to subsequently present more externalizing symptoms, and these children should be recognized and treated early. Copyright © 2009 John Wiley & Sons, Ltd.     

The holistic and spiritual relevance of Bible stories among Finnish 10-year-old pupils: a classroom case study

Abstract

In this article, we explore what kind of relevance Bible stories have on 10-year-old pupils in religious education lessons. The data was gathered in Lutheran RE lessons in Finland. The Bible stories were told by using special storytelling methods, Godly Play and Religious Pedagogic Practice. The study used stimulated recall method interviews to study the reflections the students (N = 9) had during lessons. Analysis was done via deductive content analysis. The study showed that different forms of holistic relevance could be identified in the recollections of the students. The most common forms of relevance were emotional, moral and religious relevance. The study indicates that different kinds of stories emphasise different kinds of relevance and while there were some similar features in the relevance of the stories among the students, there were also differences. In the end, the implications of this study to teacher education and classroom learning are discussed.     

Neural representation of language: activation versus long-range connectivity

Cognitive functions are thought to build on connectivity within large-scale neuronal networks, rather than on strictly localized processes. Yet, present understanding of neural mechanisms of language function, as derived from neuroimaging, is based on mapping brain areas that are more active during specific linguistic tasks than in control conditions. Connectivity can then be evaluated among those areas. However, network nodes should ideally be determined based on their correlated time series of activity. Recent developments in analysis methods now facilitate localization and characterization of functionally connected neural networks directly from real-time magnetoencephalography data. Analysis of long-range connectivity might clarify and expand the view provided by traditional neurophysiological and hemodynamic activation studies. Here, we use silent reading as the example process.     

Optimal spatial filtering for brain oscillatory activity using the Relevance Vector Machine

Over the past decade, various techniques have been proposed for localization of cerebral sources of oscillatory activity on the basis of magnetoencephalography (MEG) or electroencephalography recordings. Beamformers in the frequency domain, in particular, have proved useful in this endeavor. However, the localization accuracy and efficacy of such spatial filters can be markedly limited by bias from correlation between cerebral sources and short duration of source activity, both essential issues in the localization of brain data. Here, we evaluate a method for frequency-domain localization of oscillatory neural activity based on the relevance vector machine (RVM). RVM is a Bayesian algorithm for learning sparse models from possibly overcomplete data sets. The performance of our frequency-domain RVM method (fdRVM) was compared with that of dynamic imaging of coherent sources (DICS), a frequency-domain spatial filter that employs a minimum variance adaptive beamformer (MVAB) approach. The methods were tested both on simulated and real data. Two types of simulated MEG data sets were generated, one with continuous source activity and the other with transiently active sources. The real data sets were from slow finger movements and resting state. Results from simulations show comparable performance for DICS and fdRVM at high signal-to-noise ratios and low correlation. At low SNR or in conditions of high correlation between sources, fdRVM performs markedly better. fdRVM was successful on real data as well, indicating salient focal activations in the sensorimotor area. The resulting high spatial resolution of fdRVM and its sensitivity to low-SNR transient signals could be particularly beneficial when mapping event-related changes of oscillatory activity.     

Using Statistical Models of Morphology in the Search for Optimal Units of Representation in the Human Mental Lexicon

Determining optimal units of representing morphologically complex words in the mental lexicon is a central question in psycholinguistics. Here, we utilize advances in computational sciences to study human morphological processing using statistical models of morphology, particularly the unsupervised Morfessor model that works on the principle of optimization. The aim was to see what kind of model structure corresponds best to human word recognition costs for multimorphemic Finnish nouns: a model incorporating units resembling linguistically defined morphemes, a whole‐word model, or a model that seeks for an optimal balance between these two extremes. Our results showed that human word recognition was predicted best by a combination of two models: a model that decomposes words at some morpheme boundaries while keeping others unsegmented and a whole‐word model. The results support dual‐route models that assume that both decomposed and full‐form representations are utilized to optimally process complex words within the mental lexicon.