Motor imagery, perspective taking and gender differences: A VVIQ2-based study

Scientific literature highlighted gender differences in spatial orientation. In particular, men and women differ in terms of the navigational processes they use in daily life. Scientific literature highlighted that women use analytical strategies while men tend to use holistic strategies. Furthermore, studies exploring gender differences in self-reported dream perspectives reported that women dream mainly in first person and men mainly in third person. This work used Vividness of Movement Imagery Questionnaire-2 to verify if gender differences in perspective taking last also in motor imagery practice. The aim of VVIQ2 questionnaire is to determine the vividness of movement imagery. In the VVIQ2, the image can be obtained watching oneself while performing the movement from an external point of view (External Visual Imagery), or from an internal point of view, as if you were looking out through your own eyes while performing the movement (Internal Visual Imagery). The Questionnaire was administered to students of Sports Sciences Degree from University of Salern

Developing allometric models to predict the individual aboveground biomass of shrubs worldwide

Existing global models to predict standing biomass are based on trees characterized by a single principal stem, well developed in height. However, their use in open woodlands and shrublands, characterized by multistemmed species with substantial crown development, generates a high level of uncertainty in biomass estimates. This limitation led us to (a) develop global models of shrub individual aboveground biomass based on simple allometric variables, (b) to compare the fit of these models with existing global biomass models, and (c) to assess whether models fit change when bioclimatic variables are considered. Location: Global. Time period: Present. Major taxa studied: 118 species of shrubs. Methods: We compile a database of 3,243 individuals across 49 sites distributed worldwide. Including stem basal diameter, height and crown diameter as predictor variables, we built potential models and compared their fit using generalized least squares. We used mixed effects models to determine if bioclimatic variables improved the accuracy of biomass models. Results: Although the most important variable in terms of predictive capacity was stem basal diameter, crown diameter significantly improved the models? fit, followed by height. Four models were finally chosen, with the best model combining all these variables in the same equation [R 2 = 0.930, root mean square error (RMSE) = 0.476]. Selected models performed as well as established global biomass models. Including the individual bioform significantly improved the models? fit. Main conclusions: Stem basal diameter, crown diameter and height measures could be combined to provide robust aboveground biomass (AGB) estimates of individual shrub species. Our study supplements well-established models developed for trees, allowing more accurate biomass estimation of multistemmed woody individuals. We further provide tools for a methodological standardization of individual biomass quantification in these species. We expect these results contribute to improve the quality of biomass estimates across ecosystems, but also to generate methodological consensus on field biomass assessments in shrubs.Fil: Conti, Georgina. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Gorne, Lucas Damián. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Zeballos, Sebastián Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Lipoma, Maria Lucrecia. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Gatica, G.. Universidad Nacional de San Juan; ArgentinaFil: Kowaljow, Esteban. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Whitworth Hulse, Juan Ignacio. Universidad Nacional de San Luis; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Cuchietti, Anibal. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: Poca, María. Universidad Nacional de Córdoba; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Pestoni, Sofía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi"; ArgentinaFil: Fernandes, P. M.. Universidade de Trás‐os‐Montes e Alto Douro; Portuga

La Performance Analysis. Elementi di base ed aspetti applicativi in campo educativo ed integrativo

La Performance Analysis è una metodologia che può consentire di analizzare e spiegare in modo accurato, anche in ambiente educativo, l'evoluzione di un fenomeno motorio e sportivo. I suoi campi d’applicazione sono molteplici e non sono legati esclusivamente all’analisi della prestazione sportiva, ma si riferiscono allo studio dei possibili comportamenti motori che l’individuo, nelle diverse situazioni, può porre in essere. Questa metodologia richiede uno studio continuo per modellare gli strumenti di ricerca utilizzati alle proprie necessità ed alle diverse situazioni performative e comportamentali ed offre grandi possibilità d’integrazione e sviluppo multidisciplinare. La trasferibilità nei contesti educativi della Performance Analysis ed il suo potenziale valutativo nell’ambito dell’integrazione scolastica dei disabili può favorire l’incontro di filoni scientifici diversi, consentendo di tracciare orizzonti di studio e di ricerca suggestivi ed innovativi e superando i limiti della contestualità nella valutazione dei processi performativi in ambito motorio e sportivo. Il metodo dell’analisi performativa costituisce uno strumento in grado di garantire una condizione ottimale per valutare un numero altissimo di elementi utili ad analizzare eventi e prestazioni da diversi punti di vista. Parte II di Maurizio Sibilio, La dimensione educativa della Performance Analysis, pp. 343-38