In 1932 Frederic Bartlett laid the foundation for the later Schema Theory. His findings considerably contributed to the understanding of how previous knowledge affects processing of visual stimuli. Empirical proof however is sparse and Bartlett’s results are hardly verifiable. We replicated Bartlett’s methods of ‘serial’ (Experiment 1, N=177) and ‘repeated reproduction’ (Experiment 2, N=53). In both experiments undergraduates inspected particular one of different sketches variing in the degree of faceness. After a delay of a minimum of 15 minutes participants were asked to reproduce their visual imageries. In Experiment 1 we rotated the produced sketches among participants and repeated the procedure five times. In Experiment 2 we asked them to reproduce their visual imageries after minimum weekly delays for five times. In two subsequent studies the participant’s sketches were evaluated in the degree of faceness on a 7-point Likert-scale. In contrast to Bartlett’s findings the sketches did not become more face-like the more often the reproduction process was performed, indicating more complex cognitive processes underlying the schema formation. For Experiment 1 we even obtained reversed effects with less face-likeness after a series of reproduction. Further research should vary influential factors such as creativity, drawing abilities and delays to get further insights into the formation of memory from visual inputs.
When it comes to conserving electricity, it is crucial for
users to understand how much electricity is used by single appliances. However, the rather technical feed-
back in pure numbers and intangible units of existing
energy feedback systems is not appropriate for most users. To improve this situation, we present PowerPe-
dia, a system that aims at providing action guiding
feedback beyond pure consumption values. PowerPedia
enables users to identify and compare the consumption of their residential appliances to those of others. It thus helps users to better assess their electricity consump-
tion and draw effective measures to save electricity.
Legislation, customer pressure, and energy costs are increasing the interest of enterprises in environmental performance indicators such as greenhouse gas emissions and energy usage. Currently, business users take decisions across the value chain, from product design to disposal, without the ability to compare the environmental impact of alternatives within their information systems, thus limiting the optimization potential. In this paper we consider procurement as an example business operation and show how capturing previously-unknown material parameters in the respective information system can significantly increase the achievable optimizations. We use apple procurement into the U.K. to illustrate the paper’s idea, and conduct Monte Carlo analysis to quantify the realizable impact reductions as each additional life cycle parameter is tracked. The results show that taking into account the production country alone achieves a decrease in energy consumption of around 1250MJ per ton of apples, equivalent to 28% reduction from the base case.
Information and communication technology (ICT) consumes energy, but is also an important means of conserving energy. Conventionally, it has done so by optimizing the performance of energy-using systems and processes in industry and commerce. In the near future, ICT will also play a critical role in supporting the necessary paradigm shifts within the energy sector towards more sustainable electricity generation. However, with the advent of "smart" technology from the field of ubiquitous computing, further ways of reducing growing levels of domestic energy consumption are now emerging. With this in mind, we discuss how getting consumers "into the loop" can achieve energy savings on top of the efficiency gains resulting from automated systems, and we describe a prototype application aimed at inducing behavioral change by providing direct feedback on household electricity consumption.