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Understanding the development of spoken language in young children has become increasingly important for advancing basic theories of language acquisition and for clinical practice. However, such a goal requires refined measurements of speech articulation (e.g., from the tongue), which are difficult to obtain from young children. In recent years though, technological advances have allowed developmental researchers to make significant steps in that direction. For instance, movements of the tongue, an articulator that is essential for spoken language, can now be tracked and recorded in children with ultrasound imaging. This technique has opened novel research avenues in (a)typical language acquisition, enabling researchers to reliably capture what has long remained invisible in the speech of young children. Within this context, we have designed an experimental platform for the recording and the processing of kinematic data: SOLLAR (Sonographic and Optical Linguo-Labial Articulatory Recording system). The method has been tailored for children, but it is suitable for adults. In the present article, we introduce the recording environment developed to record over 100 children and 30 adults within SOLLAR. We then describe SOLLAR’s data processing framework, providing examples of data visualization and a summary of strengths and limitations.
Background: Doping attitude is a key variable in predicting athletes' intention to use forbidden performance enhancing drugs. Indirect reaction-time based attitude tests, such as the implicit association test, conceal the ultimate goal of measurement from the participant better than questionnaires. Indirect tests are especially useful when socially sensitive constructs such as attitudes towards doping need to be described. The present study serves the development and validation of a novel picture-based brief implicit association test (BIAT) for testing athletes' attitudes towards doping in sport. It shall provide the basis for a transnationally compatible research instrument able to harmonize anti-doping research efforts.
Method: Following a known-group differences validation strategy, the doping attitudes of 43 athletes from bodybuilding (representative for a highly doping prone sport) and handball (as a contrast group) were compared using the picture-based doping-BIAT. The Performance Enhancement Attitude Scale (PEAS) was employed as a corresponding direct measure in order to additionally validate the results.
Results: As expected, in the group of bodybuilders, indirectly measured doping attitudes as tested with the picture-based doping-BIAT were significantly less negative (eta(2) = .11). The doping-BIAT and PEAS scores correlated significantly at r = .50 for bodybuilders, and not significantly at r = .36 for handball players. There was a low error rate (7%) and a satisfactory internal consistency (r(dagger dagger) = .66) for the picture-based doping-BIAT.
Conclusions: The picture-based doping-BIAT constitutes a psychometrically tested method, ready to be adopted by the international research community. The test can be administered via the internet. All test material is available "open source". The test might be implemented, for example, as a new effect-measure in the evaluation of prevention programs.
As national efforts to reduce CO2 emissions intensify, policy-makers need increasingly specific, subnational information about the sources of CO2 and the potential reductions and economic implications of different possible policies. This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city level estimates of CO2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure.