Given the substantial increase in children attending center-based childcare over the past decades, the consequences of center-based childcare for children’s development have gained more attention in developmental research. However, the relation between center-based childcare and children’s neurocognitive development remains relatively underexplored. The aim of this study was therefore to examine the relations between quantity of center-based childcare during infancy and the neurocognitive development (both functional brain networks and self-regulation) of 584 Dutch children. Small-world brain networks and children’s self-regulation were assessed during infancy (around 10 months of age) and the preschool period (2–6 years of age). The findings revealed that the quantity of center-based childcare during infancy was unrelated to individual differences in children’s functional brain networks. However, spending more hours per week in center-based childcare was positively related to the development of self-regulation in preschool age children, regardless of children’s sex or the levels of exposure to risk and maternal support in the home environment. More insight into the positive effects of center-based childcare on children’s development from infancy to toddlerhood can help to increase our insight into a better work–life balance and labor force participation of parents with young children. Moreover, this study highlights that Dutch center-based childcare offers opportunities to invest in positive child outcomes in children, including self-regulation.
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Many attempts have been made to build an artificial brain. This paper aims to contribute to the conceptualization of an artificial learning system that functionally resembles an organic brain in a number of important neuropsychological aspects. Probably the techniques (algorithms) required are already available in various fields of artificial intelligence. However, the question is how to combine those techniques. The combination of truly autonomous learning, in which "accidental" findings (serendipity) can be used without supervision, with supervised learning from both the surrounding and previous knowledge, is still very challenging. In the event of changed circumstances, network models that can not utilize previously acquired knowledge must be completely reset, while in representation-driven networks, new formation will remain outside the scope, as we will argue. In this paper considerations to make artificial learning functionally similar to organic learning, and the type of algorithm that is necessary in the different hierarchical layers of the brain are discussed. To this end, algorithms are divided into two types: conditional algorithms (CA) and completely unsupervised learning. It is argued that in a conceptualisation of an artificial device that is functional similar to an organic learning system, both conditional learning (by applying CA’s), and non-conditional (supervised) learning must be applied.
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From the article: Abstract Over the last decades, philosophers and cognitive scientists have argued that the brain constitutes only one of several contributing factors to cognition, the other factors being the body and the world. This position we refer to as Embodied Embedded Cognition (EEC). The main purpose of this paper is to consider what EEC implies for the task interpretation of the control system. We argue that the traditional view of the control system as involved in planning and decision making based on beliefs about the world runs into the problem of computational intractability. EEC views the control system as relying heavily on the naturally evolved fit between organism and environment. A ‘lazy’ control structure could be ‘ignorantly successful’ in a ‘user friendly’ world, by facilitating the transitory creation of a flexible and integrated set of behavioral layers that are constitutive of ongoing behavior. We close by discussing the types of questions this could imply for empirical research in cognitive neuroscience and robotics.
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Ageing potentially poses a threat to independent functioning of older adults. Although clinicians commonly focus on physical factors limiting Functional Independence (FI), it is likely that personal and environmental interactions also seem important to maintain FI. Herewith, FI exceeds several professional borders and calls for a uniform, multidisciplinary interdisciplinary supported definition of FI. This study aims to provide such a definition of FI in community dwelling older people. A scoping review was performed. Pubmed/Medline, Psychinfo and CINAHL were searched for studies describing aspects of FI. A literature-based definition of FI was discussed by experts (n = 7), resulting in a formulated final definition of FI and insight into contributing factors to FI. A multidisciplinairy focusgroup a stakeholder consultation (n = 15) ensured clinical relevance for daily practice. Data from the focusgroup stakeholder consultation were analyzed by using Atlas.ti (version 8). Based on the literature search, 25 studies were included. FI was finally defined as “Functioning physically safely and independent from another person, within one’s own context”. The contributing factors of FI comprised physical capacity combined with coping, empowerment and health literacy. Moreover, the level of FI is influenced by someone’s own context. This study confirms the relevance of the physical aspect of FI, but additionally stresses the importance of psychological factors. In addition, this study shows that one’s context may affect the level of FI as well. This underlines the importance of a holistic view and calls for multidisciplinary interdisciplinary collaboration in community-dwelling older people.
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Brains and gender, separately and in their interrelatedness, are hot items today in popular journals and academic literature. It is in particular the complexity of the interdependence of physical-, psychological-, and contextual-related developments of feminization in education that we focus on these contributions. We argue that a combination of recent findings of brain research and Marcia's psychological model of identity development in a “provocative pedagogy”—combining youngsters’ (boys and girls) need for exploration, console, and support—is a promising “stepped care” strategy for religious development of youngsters in a multicultural and multireligious context.
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The aging population presents challenges for healthcare, particularly in maintaining the functional independence of older adults. The Decision Support Tool for Functional Independence was developed to identify declines in functional independence and promote collaboration between healthcare professionals. The DST-FI is specifically designed to support interprofessional collaboration between medical and social care providers, such as GPs, physiotherapists, nurses, and social workers. This study examines the barriers and facilitators to implementing the tool in primary care.
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We present a novel architecture for an AI system that allows a priori knowledge to combine with deep learning. In traditional neural networks, all available data is pooled at the input layer. Our alternative neural network is constructed so that partial representations (invariants) are learned in the intermediate layers, which can then be combined with a priori knowledge or with other predictive analyses of the same data. This leads to smaller training datasets due to more efficient learning. In addition, because this architecture allows inclusion of a priori knowledge and interpretable predictive models, the interpretability of the entire system increases while the data can still be used in a black box neural network. Our system makes use of networks of neurons rather than single neurons to enable the representation of approximations (invariants) of the output.
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In the past decade, the fast and transient coupling and uncoupling of functionally related brain regions into networks has received much attention in cognitive neuroscience. Empirical tools to study network coupling include functional magnetic resonance imaging (fMRI)-based functional and/or effective connectivity, and electroencephalography (EEG)/magnetoencephalography-based measures of neuronal synchronization. Here we use simultaneously recorded EEG and fMRI to assess whether fMRI-based connectivity and frequency-specific EEG power are related. Using data collected during resting state, we studied whether posterior EEG alpha power fluctuations are correlated with connectivity within the visual network and between the visual cortex and the rest of the brain. The results show that when alpha power increases, BOLD connectivity between the primary visual cortex and occipital brain regions decreases and that the negative relation of the visual cortex with the anterior/medial thalamus decreases and the ventral–medial prefrontal cortex is reduced in strength. These effects were specific for the alpha band, and not observed in other frequency bands. The decreased connectivity within the visual system may indicate an enhanced functional inhibition during a higher alpha activity. This higher inhibition level also attenuates long-range intrinsic functional antagonism between the visual cortex and the other thalamic and cortical regions. Together, these results illustrate that power fluctuations in posterior alpha oscillations result in local and long-range neural connectivity changes.
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Assistive Technology (AT) is any technology that supports people with functional difficulties to perform their daily activities with less difficulty and/or obstruction, thus contributing to a more fulfilling life. This refers to people of all ages and to all kinds of functional limitations, either permanent or temporary. Assistive products can be traditional physical products, such as wheelchairs, eyeglasses, hearing aids, or prostheses, but they can also be special input devices, care robots, computers with accessible software, apps for smartphones, home automation solutions, virtual realities, etc. It is essential to understand that AT involves more than just familiar products, and that it also includes knowledge about the personalized selection of appropriate solutions, provisions, and services, as well as the training of all parties involved, the measurement of outcomes and impacts, awareness of ethical issues, etc.
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During sentence level language comprehension, semantic and syntactic unification are functionally distinct operations. Nevertheless, both recruit roughly the same brain areas (spatially overlapping networks in the left frontotemporal cortex) and happen at the same time (in the first few hundred milliseconds after word onset). We tested the hypothesis that semantic and syntactic unification are segregated by means of neuronal synchronization of the functionally relevant networks in different frequency ranges: gamma (40 Hz and up) for semantic unification and lower beta (10–20 Hz) for syntactic unification. EEG power changes were quantified as participants read either correct sentences, syntactically correct though meaningless sentences (syntactic prose), or sentences that did not contain any syntactic structure (random word lists). Other sentences contained either a semantic anomaly or a syntactic violation at a critical word in the sentence. Larger EEG gamma-band power was observed for semantically coherent than for semantically anomalous sentences. Similarly, betaband power was larger for syntactically correct sentences than for incorrect ones. These results confirm the existence of a functional dissociation in EEG oscillatory dynamics during sentence level language comprehension that is compatible with the notion of a frequency-based segregation of syntactic and semantic unification.
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