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Martin Voracek - One of the best experts on this subject based on the ideXlab platform.

  • one century of global iq gains a formal meta analysis of the Flynn effect 1909 2013
    Perspectives on Psychological Science, 2015
    Co-Authors: Martin Voracek, Jakob Pietschnig
    Abstract:

    The Flynn effect (rising intelligence test performance in the general population over time and generations) varies enigmatically across countries and intelligence domains; its substantive meaning and causes remain elusive. This first formal meta-analysis on the topic reveals worldwide IQ gains across more than one century (1909-2013), based on 271 independent samples, totaling almost four million participants, from 31 countries. Key findings include that IQ gains vary according to domain (estimated 0.41, 0.30, 0.28, and 0.21 IQ points annually for fluid, spatial, fullscale, and crystallized IQ test performance, respectively), are stronger for adults than children, and have decreased in more recent decades. Altogether, these findings narrow down proposed theories and candidate factors presumably accounting for the Flynn effect. Factors associated with life history speed seem mainly responsible for the Flynn effect’s general trajectory, whereas favorable social-multiplier effects and effects related to economic prosperity appear to be responsible for observed differences of the Flynn effect across intelligence domains.

  • one century of global iq gains a formal meta analysis of the Flynn effect 1909 2013
    Perspectives on Psychological Science, 2015
    Co-Authors: Martin Voracek, Jakob Pietschnig
    Abstract:

    The Flynn effect (rising intelligence test performance in the general population over time and generations) varies enigmatically across countries and intelligence domains; its substantive meaning a...

Jakob Pietschnig - One of the best experts on this subject based on the ideXlab platform.

  • is ability based emotional intelligence impervious to the Flynn effect a cross temporal meta analysis 2001 2015
    Intelligence, 2017
    Co-Authors: Jakob Pietschnig, Georg Gittler
    Abstract:

    Abstract Generational IQ test score changes (i.e., the Flynn effect) have been shown to affect most measures of cognitive ability, although certain domains appear to be impervious. Because IQ test score changes have been found to differ between domains, evidence for specific domains is warranted to explain the nature, meaning, and causes of the Flynn effect. In the present cross-temporal meta-analysis, we investigated potential test score changes over time for ability emotional intelligence as assessed with a widely-used measure (i.e., the MSCEIT V2.0) based on data from 160 samples ( N  = 16,738) from English-speaking countries over a time-span of 15 years (2001–2015). We found no evidence for substantial generational test score changes in overall or branch (i.e., subscale) ability emotional IQ scores. Consistent with our expectations, test performance was substantially higher for women in most measures. Contrary to our expectations though, average performance of our participants was considerably lower than the performance of normative samples on all scales, thus raising concerns about the appropriateness of the current norms. In all, we argue that ability emotional IQ as measured with the MSCEIT V2.0 is largely unaffected by typical causes that have been proposed as responsible for the Flynn effect. This may be due to the close association of this construct with psychometric g (associations of MSCEIT V2.0 scores with cognitive task performance were modest in our data) on one hand and personality traits on the other hand, both of which have been shown not to be associated with test score gains.

  • one century of global iq gains a formal meta analysis of the Flynn effect 1909 2013
    Perspectives on Psychological Science, 2015
    Co-Authors: Martin Voracek, Jakob Pietschnig
    Abstract:

    The Flynn effect (rising intelligence test performance in the general population over time and generations) varies enigmatically across countries and intelligence domains; its substantive meaning and causes remain elusive. This first formal meta-analysis on the topic reveals worldwide IQ gains across more than one century (1909-2013), based on 271 independent samples, totaling almost four million participants, from 31 countries. Key findings include that IQ gains vary according to domain (estimated 0.41, 0.30, 0.28, and 0.21 IQ points annually for fluid, spatial, fullscale, and crystallized IQ test performance, respectively), are stronger for adults than children, and have decreased in more recent decades. Altogether, these findings narrow down proposed theories and candidate factors presumably accounting for the Flynn effect. Factors associated with life history speed seem mainly responsible for the Flynn effect’s general trajectory, whereas favorable social-multiplier effects and effects related to economic prosperity appear to be responsible for observed differences of the Flynn effect across intelligence domains.

  • one century of global iq gains a formal meta analysis of the Flynn effect 1909 2013
    Perspectives on Psychological Science, 2015
    Co-Authors: Martin Voracek, Jakob Pietschnig
    Abstract:

    The Flynn effect (rising intelligence test performance in the general population over time and generations) varies enigmatically across countries and intelligence domains; its substantive meaning a...

Ashish Ghosh - One of the best experts on this subject based on the ideXlab platform.

  • an improved swarm optimized functional link artificial neural network iso flann for classification
    Journal of Systems and Software, 2012
    Co-Authors: Satchidananda Dehuri, Rahul Roy, Sungbae Cho, Ashish Ghosh
    Abstract:

    Highlights? A novel HONs for classification task of data mining. ? A novel improved PSO for training FLANN (ISO-FLANN). ? A new complex medical domain dataset is introduced for validating the method. Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN=ISO-FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).

Satchidananda Dehuri - One of the best experts on this subject based on the ideXlab platform.

  • a particle swarm optimized functional link artificial neural network pso flann in software cost estimation
    2013
    Co-Authors: Tirimula Rao Benala, Korada Chinnababu, Rajib Mall, Satchidananda Dehuri
    Abstract:

    We use particle swarm optimization (PSO) to train the functional link artificial neural network (FLANN) for software effort prediction. The combined framework is known as PSO-FLANN. This framework exploits the global classification capability of PSO and FLANN’s complex nonlinear mapping between its input and output pattern space by using functional expansion. The Chebyshev polynomial has been used as choice of expansion in FLANN to exhaustively study the performance in three real time datasets. The simulation results show that it not only deals efficiently with noisy data but achieves improved accuracy in prediction.

  • an improved swarm optimized functional link artificial neural network iso flann for classification
    Journal of Systems and Software, 2012
    Co-Authors: Satchidananda Dehuri, Rahul Roy, Sungbae Cho, Ashish Ghosh
    Abstract:

    Highlights? A novel HONs for classification task of data mining. ? A novel improved PSO for training FLANN (ISO-FLANN). ? A new complex medical domain dataset is introduced for validating the method. Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN=ISO-FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN).

Robin G. Morris - One of the best experts on this subject based on the ideXlab platform.

  • The Flynn effect for verbal and visuospatial short-term and working memory: A cross-temporal meta-analysis
    Intelligence, 2017
    Co-Authors: Peera Wongupparaj, Rangsirat Wongupparaj, Veena Kumari, Robin G. Morris
    Abstract:

    Abstract The Flynn effect has been investigated extensively for IQ, but few attempts have been made to study it in relation to working memory (WM). Based on the findings from a cross-temporal meta-analysis using 1754 independent samples (n = 139,677), the Flynn effect was observed across a 43-year period, with changes here expressed in terms of correlations (coefficients) between year of publication and mean memory test scores. Specifically, the Flynn effect was found for forward digit span (r = 0.12, p