The influence of memory for prior instances on performance in a conflict detection task

In 3 experiments, the authors examined the role of memory for prior instances for making relative judgments in conflict detection. Participants saw pairs of aircraft either repeatedly conflict with each other or pass safely before being tested on new aircraft pairs, which varied in similarity to the training pairs. Performance was influenced by the similarity between aircraft pairs. Detection time was faster when a conflict pair resembled a pair that had repeatedly conflicted. Detection time was slower, and participants missed conflicts, when a conflict pair resembled a pair that had repeatedly passed safely. The findings identify aircraft features that are used as inputs into the memory decision process and provide an indication of the processes involved in the use of memory for prior instances to make relative judgments.

Body-mass management of Australian lightweight rowers prior to and during competition

Purpose: Although the body-mass management strategies of athletes in high-participation weight-category sports such as wrestling have been thoroughly investigated, little is known about such practices among lightweight rowers. This study examined the body-mass management practices of lightweight rowers before competition and compared these with current guidelines of the International Federation of Rowing Association (FISA). Quantification of nutrient intake in the 1-2 h between weigh-in and racing was also sought. Methods: Lightweight rowers (N = 100) competing in a national regatta completed a questionnaire that assessed body-mass management practices during the 4 wk before and throughout a regatta plus recovery strategies after weigh-in. Biochemical data were collected immediately after weigh-in to validate questionnaire responses. Responses were categorized according to gender and age category (Senior B or younger than 23 yr old, i.e., U23, Senior A or OPEN, i.e., open age limit) for competition. Results: Most athletes (male U23 76.5%, OPEN 92.3%; female U23 84.0%, OPEN 94.1%) decreased their body mass in the weeks before the regatta at rates compliant with FISA guidelines. Gradual dieting, fluid restriction, and increased training load were the most popular methods of body-mass management. Although the importance of recovery after weigh-in was recognized by athletes, nutrient intake and especially sodium (male U23 5.3 ± 4.9, OPEN 7.7 ± 5.9; female U23 5.7 ± 6.8, OPEN 10.2 ± 5.4 mg-kg(-1)) and fluid intake (male U23 12.1 ± 7.1, OPEN 13.5 ± 8.1; female U23 9.4 ± 7.4, OPEN 14.8 ± 6.9 mL.kg(-1)) were below current sports nutrition recommendations. Conclusion: Few rowers were natural lightweights; the majority reduced their body mass in the weeks before a regatta. Nutritional recovery strategies implemented by lightweight rowers after weigh-in were not consistent with current guidelines.

Patient teaching prior to coronary angiography in Hong Kong: A pilot study

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Shortcomings of protein removal prior to high performance liquid chromatographic analysis - A case study using method development for BAY 11-7082

During the analytical method development for BAY 11-7082 ((E)-3-[4-methylphenylsulfonyl]-2-propenenitrile), using HPLC-MS-MS and HPLC-UV, we observed that the protein removal process (both ultrafiltration and precipitation method using organic solvents) prior to HPLC brought about a significant reduction in the concentration of this compound. The use of a structurally similar internal standard, BAY 11-7085 ((E)-3-[4-t-butylphenylsulfonyl]-2-propenenitrile), was not effective in compensating for the loss of analyte as the extent of reduction was different to that of the analyte. We present here a systematic investigation of this problem and a new validated method for the determination of BAY 11-7082. (c) 2006 Elsevier B.V. All rights reserved.

Non-zero mean Gaussian process prior wind field models

This report outlines the derivation and application of a non-zero mean, polynomial-exponential covariance function based Gaussian process which forms the prior wind field model used in 'autonomous' disambiguation. It is principally used since the non-zero mean permits the computation of realistic local wind vector prior probabilities which are required when applying the scaled-likelihood trick, as the marginals of the full wind field prior. As the full prior is multi-variate normal, these marginals are very simple to compute.

Block GTM: Incorporating prior knowledge of covariance structure in data visualisation

Visualising data for exploratory analysis is a big challenge in scientific and engineering domains where there is a need to gain insight into the structure and distribution of the data. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are used, but it is difficult to incorporate prior knowledge about structure of the data into the analysis. In this technical report we discuss a complementary approach based on an extension of a well known non-linear probabilistic model, the Generative Topographic Mapping. We show that by including prior information of the covariance structure into the model, we are able to improve both the data visualisation and the model fit.

The role of prior exposure on the capture of attention by items in working memory

The Biased Competition Model (BCM) suggests both top-down and bottom-up biases operate on selective attention (e.g., Desimone & Duncan, 1995). It has been suggested that top-down control signals may arise from working memory. In support, Downing (2000) found faster responses to probes presented in the location of stimuli held vs. not held in working memory. Soto, Heinke, Humphreys, and Blanco (2005) showed the involuntary nature of this effect and that shared features between stimuli were sufficient to attract attention. Here we show that stimuli held in working memory had an influence on the deployment of attentional resources even when: (1) It was detrimental to the task, (2) there was equal prior exposure, and (3) there was no bottom-up priming. These results provide further support for involuntary top-down guidance of attention from working memory and the basic tenets of the BCM, but further discredit the notion that bottom-up priming is necessary for the effect to occur.

'I like this interview; I get cakes and cats!':the effect of prior relationships on interview talk

Research interviews are a form of interaction jointly constructed by the interviewer and interviewee, what Silverman (2001: 104) calls 'interview-as-local-accomplishment'. From this perspective, interviews are an interpretative practice in which what is said is inextricably tied to where it is said, how it is said and, importantly, to whom it is said (Holstein and Gubrium, 2004). The relationship between interviewer and interviewee, then, is fundamental in research interviews. But what happens when the relationship between interviewer and interviewee is not only that of researcher-informant but also involves other roles such as colleague and friend? In this article we will show how prior relationships are invoked and made relevant by both parties during educational research interviews and how these prior relationships therefore contribute to the 'generation' (Baker, 2004: 163) of interview data. © 2010 The Author(s).

Data visualisation and exploration with prior knowledge

Visualising data for exploratory analysis is a major challenge in many applications. Visualisation allows scientists to gain insight into the structure and distribution of the data, for example finding common patterns and relationships between samples as well as variables. Typically, visualisation methods like principal component analysis and multi-dimensional scaling are employed. These methods are favoured because of their simplicity, but they cannot cope with missing data and it is difficult to incorporate prior knowledge about properties of the variable space into the analysis; this is particularly important in the high-dimensional, sparse datasets typical in geochemistry. In this paper we show how to utilise a block-structured correlation matrix using a modification of a well known non-linear probabilistic visualisation model, the Generative Topographic Mapping (GTM), which can cope with missing data. The block structure supports direct modelling of strongly correlated variables. We show that including prior structural information it is possible to improve both the data visualisation and the model fit. These benefits are demonstrated on artificial data as well as a real geochemical dataset used for oil exploration, where the proposed modifications improved the missing data imputation results by 3 to 13%.