Aphasia rehabilitation and the strange neglect of speed.

Timing data is infrequently reported in aphasiological literature and time taken is only a minor factor, where it is considered at all, in existing aphasia assessments. This is not surprising because reaction times are difficult to obtain manually, but it is a pity, because speed data should be indispensable in assessing the severity of language processing disorders and in evaluating the effects of treatment. This paper argues that reporting accuracy data without discussing speed of performance gives an incomplete and potentially misleading picture of any cognitive function. Moreover, in deciding how to treat, when to continue treatment and when to cease therapy, clinicians should have regard to both parameters: Speed and accuracy of performance. Crerar, Ellis and Dean (1996) reported a study in which the written sentence comprehension of 14 long-term agrammatic subjects was assessed and treated using a computer-based microworld. Some statistically significant and durable treatment effects were obtained after a short amount of focused therapy. Only accuracy data were reported in that (already long) paper, and interestingly, although it has been a widely read study, neither referees nor subsequent readers seemed to miss "the other side of the coin": How these participants compared with controls for their speed of processing and what effect treatment had on speed. This paper considers both aspects of the data and presents a tentative way of combining treatment effects on both accuracy and speed of performance in a single indicator. Looking at rehabilitation this way gives us a rather different perspective on which individuals benefited most from the intervention. It also demonstrates that while some subjects are capable of utilising metalinguistic skills to achieve normal accuracy scores even many years post-stroke, there is little prospect of reducing the time taken to within the normal range. Without considering speed of processing, the extent of this residual functional impairment can be overlooked.

CO2Y the intelligent green solution: minimising carbon emissions by maximising shared travel opportunity.

It is in the interests of everybody that the environment is protected. In view of the recent leaps in environmental awareness it would seem timely and sensible, therefore, for people to pool vehicle resources to minimise the damaging impact of emissions. However, this is often contrary to how complex social systems behave – local decisions made by self-interested individuals often have emergent effects that are in the interests of nobody. For software engineers a major challenge is to help facilitate individual decision-making such that individual preferences can be met, which, when accumulated, minimise adverse effects at the level of the transport system. We introduce this general problem through a concrete example based on vehicle-sharing. Firstly, we outline the kind of complex transportation problem that is directly addressed by our technology (CO2y™ - pronounced “cosy”), and also show how this differs from other more basic software solutions. The CO2y™ architecture is then briefly introduced. We outline the practical advantages of the advanced, intelligent software technology that is designed to satisfy a number of individual preference criteria and thereby find appropriate matches within a population of vehicle-share users. An example scenario of use is put forward, i.e., minimisation of grey-fleets within a medium-sized company. Here we comment on some of the underlying assumptions of the scenario, and how in a detailed real-world situation such assumptions might differ between different companies, and individual users. Finally, we summarise the paper, and conclude by outlining how the problem of pooled transportation is likely to benefit from the further application of emergent, nature-inspired computing technologies. These technologies allow systems-level behaviour to be optimised with explicit representation of individual actors. With these techniques we hope to make real progress in facing the complexity challenges that transportation problems produce.