Terrorism: Arriving at an understanding of a term

157-93 (Michael J. Glennon and Serge Sur eds.,

Effects of active information processing on the understanding of risk information

Two experiments investigated effects of active processing of risk information on participants' understanding and judgments. It was hypothesized that more active processing would lead to better understanding and differences in affective judgments (e.g. increased satisfaction and reduced perceived risk to health). In both experiments participants were given a written scenario about their being prescribed a fictitious medication. This medication was said to cause side effects in 2% of people who took it. Before answering a series of written questions, participants in the active conditions of both experiments were asked to carry out a reflective task (portraying the size of risk on a bar chart in Experiment 1 and answering a reflective question in Experiment 2). The results showed that active participants rated the likelihood of experiencing possible side effects significantly lower than passive participants (Experiment 1), and that active participants were significantly more satisfied with the information and judged perceived risk to health from taking the medication significantly lower than passive participants (Experiment 2). In both experiments, active participants were significantly more correct in their probability and frequency estimates. The studies demonstrate that active processing of risk information leads to improved understanding of the information given. This has important implications for risk communication. In the context of health, better understanding should lead to improved decision-making and health outcomes. Copyright (C) 2004 John Wiley Sons, Ltd.

Patients' understanding of risk associated with medication use: impact of European Commission guidelines and other risk scales

Patients want and need comprehensive and accurate information about their medicines so that they can participate in decisions about their healthcare: In particular, they require information about the likely risks and benefits that are associated with the different treatment options. However, to provide this information in a form that people can readily understand and use is a considerable challenge to healthcare professionals. One recent attempt to standardise the Language of risk has been to produce sets of verbal descriptors that correspond to specific probability ranges, such as those outlined in the European Commission (EC) Pharmaceutical Committee guidelines in 1998 for describing the incidence of adverse effects. This paper provides an overview of a number of studies involving members of the general public, patients, and hospital doctors, that evaluated the utility of the EC guideline descriptors (very common, common, uncommon, rare, very rare). In all studies it was found that people significantly over-estimated the likelihood of adverse effects occurring, given specific verbal descriptors. This in turn resulted in significantly higher ratings of their perceived risks to health and significantly lower ratings of their likelihood of taking the medicine. Such problems of interpretation are not restricted to the EC guideline descriptors. Similar levels of misinterpretation have also been demonstrated with two other recently advocated risk scales (Caiman's verbal descriptor scale and Barclay, Costigan and Davies' lottery scale). In conclusion, the challenge for risk communicators and for future research will be to produce a language of risk that is sufficiently flexible to take into account different perspectives, as well as changing circumstances and contexts of illness and its treatments. In the meantime, we urge the EC and other legislative bodies to stop recommending the use of specific verbal labels or phrases until there is a stronger evidence base to support their use.

CO migration in native and mutant myoglobin: atomistic simulations for the understanding of protein function

Molecular dynamics simulations of the events after the photodissociation of CO in the myoglobin mutant L29F in which leucine is replaced by phenylalanine are reported. Using both classical and mixed quantum-classical molecular dynamics calculations, we observed the rapid motion of CO away from the distal heme pocket to other regions of the protein, in agreement with recent experimental results. The experimentally observed and calculated infrared spectra of CO after dissociation are also in good agreement. We compared the results with data from simulations of WT myoglobin. As the time resolution of experimental techniques is increased, theoretical methods and models can be validated at the atomic scale by direct comparison with experiment.

Effect of phonetic complexity on word reading and repetition in deep dyslexia

This investigation moves beyond the traditional studies of word reading to identify how the production complexity of words affects reading accuracy in an individual with deep dyslexia (JO). We examined JO’s ability to read words aloud while manipulating both the production complexity of the words and the semantic context. The classification of words as either phonetically simple or complex was based on the Index of Phonetic Complexity. The semantic context was varied using a semantic blocking paradigm (i.e., semantically blocked and unblocked conditions). In the semantically blocked condition words were grouped by semantic categories (e.g., table, sit, seat, couch,), whereas in the unblocked condition the same words were presented in a random order. JO’s performance on reading aloud was also compared to her performance on a repetition task using the same items. Results revealed a strong interaction between word complexity and semantic blocking for reading aloud but not for repetition. JO produced the greatest number of errors for phonetically complex words in semantically blocked condition. This interaction suggests that semantic processes are constrained by output production processes which are exaggerated when derived from visual rather than auditory targets. This complex relationship between orthographic, semantic, and phonetic processes highlights the need for word recognition models to explicitly account for production processes.