A Methodological Study of the Use of “Aha!” Ratings in Research on Insight Problem Solving and Creativity

Creativity is often associated with people experiencing sudden flashes of inspiration and understanding. These type of experiences have been called insights or “Aha!” moments. “Aha!” moments or insightful solutions are often described as answers that suddenly appear in the mind, with little effort, and extreme confidence that the answer is correct. This can be contrasted with incremental or analytically solved problems where solutions are reached through effortful, deliberate, and strategically application of prior knowledge. Researchers have often used subjective ratings of participants’ own “Aha!” experiences to differentiate between insightful and incrementally solved problems. In the current study we investigated the construct validity of these Aha! ratings, by presenting participants with the three different aspects of the “Aha!” rating (Suddenness, Effort, and Confidence) as separate measures to see if these aspect of the solving experience were highly correlated

Not just a grain of rice: the quest for quality

A better understanding of the factors that contribute to the overall grain quality of rice (Oryza sativa) will lay the foundation for developing new breeding and selection strategies for combining high quality, with high yield. This is necessary to meet the growing global demand for high quality rice while offering producing countries additional opportunities for generating higher export revenues. Several recent developments in genetics, genomics, metabolomics and phenomics are enhancing our understanding of the pathways that determine several quality traits. New research strategies, as well as access to the draft of the rice genome, will not only advance our understanding of the molecular mechanisms that lead to quality rice but will also pave the way for efficient and targeted grain improvemen

Shadowgraph observations of the flow past a sphere and a vertical cylinder in a density stratified liquid

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Decision support solutions for factory and network logistics operations

The paper examines the relationship of decision levels, performance measures and modelling and decision support approaches through the example of two implemented decision support systems for manufacturing and logistics application fields. Aside from highlighting the relevance of decision support for making industrial networks fit for emerging challenges, the relevance of the two presented EU FP7 projects VFF and ADVANCE to the Factories of the Future vision is shown. A discussion of the two projects outlines future research, with particular focus on challenges that arise from integration across levels of the decision hierarchy, within an organisationally heterogeneous network